From 318f5d672c70efc6050974488ec564463d1f5729 Mon Sep 17 00:00:00 2001 From: Lszidv Date: Mon, 30 Dec 2024 07:53:03 +0000 Subject: [PATCH] =?UTF-8?q?=F0=9F=8E=89auto=20update=20by=20Gmeek=20action?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 4 +- ...annel analysis of shear wave splitting .md | 210 +++++++++++++ blogBase.json | 2 +- ...nel analysis of shear wave splitting .html | 282 +++++++++++++++++- 4 files changed, 493 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4b13f60..5c7fbfe 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 芜尽 :link: https://Lszidv.github.io ### :page_facing_up: [28](https://Lszidv.github.io/tag.html) ### :speech_balloon: 5 -### :hibiscus: 101209 -### :alarm_clock: 2024-12-30 15:21:40 +### :hibiscus: 104756 +### :alarm_clock: 2024-12-30 15:53:03 ### Powered by :heart: [Gmeek](https://github.com/Meekdai/Gmeek) diff --git a/backup/[Literature Reading]Multichannel analysis of shear wave splitting .md b/backup/[Literature Reading]Multichannel analysis of shear wave splitting .md index 7bdf819..17476a9 100644 --- a/backup/[Literature Reading]Multichannel analysis of shear wave splitting .md +++ b/backup/[Literature Reading]Multichannel analysis of shear wave splitting .md @@ -51,3 +51,213 @@ The analysis of shear wave splitting is greatly simplified if the polarization o | **优点** | 多震相联合分析，鲁棒性强 | 单震相分析，结果精确 | | **局限性** | 方位角覆盖不足时会影响结果 | 手动窗口选择主观性强，低信噪比受噪声干扰 | | **适用场景** | 多震相、多台站联合分析区域性各向异性研究 | 单震相分析，高质量数据的精确测量 | + +--- + +## 3. Effects of a Dipping Axis of Symmetry +### 剪切波在各向异性层中的传播总结 + +#### 1. 剪切波分裂的物理本质 +- **剪切波分裂现象**： + - 剪切波通过各向异性介质时分裂为两个分量： + - **快波（Fast Shear Wave）**：传播速度较快，极化方向 $\phi$ 通常与介质的主要对称轴一致。 + - **慢波（Slow Shear Wave）**：传播速度较慢，与快波方向正交。 + - **延迟时间** $\delta t$：快慢波传播时间的差异，反映各向异性强度和路径厚度。 + +- **关键参数**： + 1. **快波极化方向** $\phi$：与介质的主要应力方向或晶体取向相关。 + 2. **延迟时间** $\delta t$：与介质的各向异性强度及传播路径长度成正比。 + +--- + +#### 2. 数学模型 +- **剪切波分裂公式**： + - 假设入射波为一个线性极化的剪切波，经过各向异性层后，其快波和慢波可以分别表示为： + +$$ + R(t) = w(t + \frac{\delta t}{2}) \cos^2\phi + w(t - \frac{\delta t}{2}) \sin^2\phi +$$ + +$$ + T(t) = \frac{1}{2} \left[ w(t + \frac{\delta t}{2}) - w(t - \frac{\delta t}{2}) \right] \sin 2\phi +$$ + +- **$R(t)$**：径向分量； +- **$T(t)$**：横向分量； +- **$w(t)$**：入射波形； +- **$\delta t$**：快波和慢波的延迟时间； +- **$\phi$**：快波极化方向。 + + +- **小延迟时间的近似**： + - 当 $ \delta t $ 较小时，可以简化为： + +$$ + R(t) \approx w(t) +$$ + +$$ + T(t) \approx \frac{\delta t}{2} w'(t) \sin 2\phi +$$ + +其中 $w'(t)$ 是 $w(t)$ 的时间导数。 + +- **矩阵表示**： + - 横向分量的矩阵形式可以表示为： + +$$ + T = a \cdot s \otimes r +$$ + + +- **$a = -\frac{1}{2}$** 是比例系数； +- **$s$** 是分裂矢量，与方位角变化的横向分量振幅相关； +- **$r$** 是径向分量的导数； +- **$\otimes$** 表示张量外积。 + +--- + +#### 3. 横向各向同性（TI-H）模型 +- **定义**： + - 假设介质具有水平对称轴（Transverse Isotropy with Horizontal Symmetry Axis, TI-H）。 + - 在这种模型下，分裂矢量的变化可以通过以下公式描述： + +$$ + s(\phi) = \delta t \sin[2(\phi - \phi_0)] + $$ + + +--- + +#### 4. 多震相的传播特性 +- **不同震相的分裂现象**： + - 远震震相（如 SKS 波、SKKS 波）由于传播路径较长，分裂现象主要受到上地幔各向异性的影响。 + - 不同方位角的震相传播路径对分裂参数的测量结果有显著影响。 + +- **联合分析的必要性**： + - 单震相分裂结果可能受到路径效应或噪声干扰，通过整合多个震相数据，可以提高分裂参数的可靠性。 + +--- + +#### 5. 剪切波分裂的观测与测量 +- **观测方式**： + - 分析横波的横向分量，通过以下特性提取分裂参数： + - 横向分量振幅的变化； + - 横向分量与径向分量的时间差异。 +- **观测限制**： + - 传播路径的复杂性（如地壳效应）和噪声可能导致测量结果的不稳定。 + +--- + +#### 总结 +- 剪切波分裂由介质的各向异性引起，其关键参数包括快波极化方向 $ \phi $ 和延迟时间 $ \delta t $。 +- 数学模型提供了对分裂现象的定量描述，TI-H 模型是一种常用假设，用以简化分析。 +- 多震相联合分析能有效提高测量精度，为后续 Multichannel 方法的提出奠定了理论基础。 + +## 3. Effects of a Dipping Axis of Symmetry + +#### 核心思想 +- **目标**： + - 提出一种鲁棒的剪切波分裂测量方法，称为 **Multichannel Method**，该方法能够在高噪声条件下稳定估计各向异性参数。 + - 通过联合分析多个震相记录的数据（多通道），减少单次测量的随机误差，并提高分裂参数的可靠性。 + +- **基本原理**： + - 多震相观测中，横波的分裂现象包含了介质的各向异性信息。通过整合这些不同震相的数据，可以提高对快波方向 $ \phi $ 和延迟时间 $ \delta t $ 的估计精度。 + - 采用 **分裂强度矩阵（Splitting Intensity Matrix）** 和奇异值分解（SVD）技术来优化分裂参数的提取。 + +--- + +#### 分裂强度矩阵 +- **定义**： + - 分裂强度矩阵表示横向分量中的分裂信息，定义如下： + - +$$ +\mathbf{S} = \sum_i \mathbf{T}_i \cdot \mathbf{T}_i^T +$$ + + 其中： + - **$\mathbf{T}_i$** 是第 $i$ 个震相的横向分量； + - **$\mathbf{S}$** 表示分裂强度矩阵，综合了不同震相的横波分量信息。 + +- **矩阵性质**： + - 矩阵的主方向（即主奇异向量）反映了快波方向 $\phi$； + - 奇异值的分布用于估计快波和慢波的分裂程度，即延迟时间 $\delta t$。 + +--- + +#### 奇异值分解（SVD）技术 +- **引入 SVD 的必要性**： + - 在噪声数据中，分裂强度矩阵可能包含较大的随机误差，通过奇异值分解，可以有效提取主信号方向并消除噪声的影响。 + - 奇异值分解公式为： + +$$ +\mathbf{S} = \mathbf{U} \Sigma \mathbf{V}^T +$$ + + 其中： + - **$\mathbf{U}$** 和 **$\mathbf{V}$** 是正交矩阵； + - **$\Sigma$** 是对角矩阵，包含奇异值。 + +- **物理意义**： + - 最大奇异值方向对应于快波方向 $\phi$； + - 奇异值的大小反映分裂效应的强弱，与延迟时间 $\delta t$ 相关。 + +--- + +#### Multichannel 方法的步骤 +1. **数据准备**： + - 收集多个震相的横波分量数据（如 SKS 波和 SKKS 波）。 +2. **构建分裂强度矩阵**： + - 将所有震相的横波分量数据叠加，生成分裂强度矩阵 $\mathbf{S}$。 +3. **奇异值分解**： + - 对矩阵 $\mathbf{S}$ 进行奇异值分解，提取快波方向 $\phi$ 和延迟时间 $\delta t$。 +4. **参数优化**： + - 使用优化算法进一步调整结果，提高分裂参数的准确性。 +5. **结果验证**： + - 验证测量结果的稳定性和可靠性，例如通过残差分析或对比多个震相的独立结果。 + +--- + +#### 方法局限 +- **数据需求高**： + - Multichannel 方法依赖多个震相的高质量记录，特别是方位角分布均匀的震相覆盖。 + +- **复杂性较高**： + - 方法实现涉及较多矩阵计算和参数优化过程，需要较高的计算资源。 + +--- + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/blogBase.json b/blogBase.json index 5d7a2c0..cd0d27a 100644 --- a/blogBase.json +++ b/blogBase.json @@ -1 +1 @@ -{"singlePage": [], "startSite": "", "filingNum": "", "onePageListNum": 15, "commentLabelColor": "#006b75", "yearColorList": ["#bc4c00", "#0969da", "#1f883d", "#A333D0"], "i18n": "CN", "themeMode": "manual", "dayTheme": "light", "nightTheme": "dark", "urlMode": "pinyin", "script": "", "style": "", "head": 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"P2": {"htmlDir": "docs/post/[Literature Reading] Classification of Teleseismic Shear Wave Splitting Measurements- A Convolutional Neural Network Approach.html", "labels": ["documentation", "GRL"], "postTitle": "[Literature Reading] Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach", "postUrl": "post/%5BLiterature%20Reading%5D%20Classification%20of%20Teleseismic%20Shear%20Wave%20Splitting%20Measurements-%20A%20Convolutional%20Neural%20Network%20Approach.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/2", "commentNum": 1, "wordCount": 6222, "description": "# Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach\r\n# Abstract\r\n \u526a\u5207\u6ce2\u5206\u88c2\r\n \u95ee\u9898\uff1a\u9700\u8981\u53ef\u9760\u7684\u5206\u88c2\u6d4b\u91cf\u6570\u636e\uff0c\u76ee\u89c6\u6548\u7387\u4f4e\r\n \u65b9\u6cd5\uff1aCNN \u4eba\u5de5\u8bc6\u522b\u6570\u636e\u8bad\u7ec3\uff0c\u5408\u6210\u6570\u636e\u6d4b\u8bd5\uff0c\u5b9e\u9645\u6570\u636e\u5bf9\u6bd4\r\n \u5e94\u7528\uff1aBoardband seismic data recorded in south central Alaska\r\n\r\n# 1.Introduction\r\n XKS\u6ce2\u5728\u5404\u5411\u5f02\u6027\u4ecb\u8d28\u4e2d\u4f1a\u5206\u88c2\u6210\u4e24\u4e2a\u6b63\u4ea4\u6781\u5316\u7684\u5feb\u6ce2\u548c\u6162\u6ce2\u3002", "top": 0, "createdAt": 1730108420, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-10-28", "dateLabelColor": "#bc4c00"}, "P3": {"htmlDir": "docs/post/[Literaturre Reading] Making Reliable Shear-Wave Splitting Measurements.html", "labels": ["documentation"], "postTitle": "[Literaturre Reading] Making Reliable Shear-Wave Splitting Measurements", "postUrl": "post/%5BLiteraturre%20Reading%5D%20Making%20Reliable%20Shear-Wave%20Splitting%20Measurements.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/3", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1730711179, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-04", "dateLabelColor": "#bc4c00"}, "P4": {"htmlDir": "docs/post/CNN-SWS.html", "labels": ["Code"], "postTitle": "CNN-SWS", "postUrl": "post/CNN-SWS.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/4", "commentNum": 0, "wordCount": 10762, "description": "## \u8bba\u6587\u201cClassification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach\u201d\u4ee3\u7801\u90e8\u5206\r\n\r\n### \u6587\u4ef6\u7ed3\u6784\r\n```\r\nCNN-SWS-main/\r\n\u251c\u2500\u2500 1_data/ # \u6570\u636e\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 Out_Bin/ # \u5b58\u50a8 XKS.out \u6587\u4ef6\uff0c.out\u6587\u4ef6\u5305\u542b\u4e09\u5217\uff0c\u53f0\u7ad9\u548c\u7f51\u7edc\u540d\u79f0\uff08stname_NW\uff09\u3001\u4e8b\u4ef6\u540d\u79f0\uff08EQ123456789\uff09\u3001\u6d4b\u91cf\u8d28\u91cf\uff08A \u548c B \u8868\u793a\u53ef\u63a5\u53d7\uff0c\u5176\u4f59\u8868\u793a\u4e0d\u53ef\u63a5\u53d7\uff09 \r\n\u2502 \u2502 \u251c\u2500\u2500 PKS.out \r\n\u2502 \u2502 \u251c\u2500\u2500 SKK.out\r\n\u2502 \u2502 \u2514\u2500\u2500 SKS.out\r\n\u2502 \u2514\u2500\u2500 PKSOut/ # \u5b58\u50a8\u4e0d\u540c\u53f0\u7ad9\u548c\u4e8b\u4ef6\u7684\u6ce2\u5f62\u6570\u636e\r\n\u2502 \u251c\u2500\u2500 109Cxx_TA/ # \u53f0\u7ad9\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u251c\u2500\u2500 EQ140250514/ # \u4e8b\u4ef6\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.rl # \u6821\u6b63\u5f84\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.ro # \u539f\u59cb\u5f84\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.tl # \u6821\u6b63\u6a2a\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 109Cxx_TA.to # \u539f\u59cb\u6a2a\u5411\u5206\u91cf\r\n\u2502 \u2502 \u251c\u2500\u2500 EQ********/ #\u5176\u4ed6\u4e8b\u4ef6\r\n\u2502 \u251c\u2500\u2500 ************** # \u5176\u4ed6\u53f0\u7ad9\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 PKS.list # Out_Bin/PKS.out\r\n\u2502 \u251c\u2500\u2500 SKK.list # Out_Bin/SKK.out\r\n\u2502 \u2514\u2500\u2500 SKS.list # Out_Bin/SKS.out\r\n\u2502\r\n\u251c\u2500\u2500 load/ # \u6570\u636e\u52a0\u8f7d\u548c\u9884\u6d4b\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 2_load/ # \u52a0\u8f7d\u8f93\u51fa\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u2514\u2500\u2500 Outp/ # \u5b58\u653e\u52a0\u8f7d\u9884\u6d4b\u7ed3\u679c\r\n\u2502 \u251c\u2500\u2500 load.py # \u6570\u636e\u52a0\u8f7d\u548c\u9884\u6d4b\u811a\u672c\r\n\u2502 \u2514\u2500\u2500 parameter.list # \u52a0\u8f7d\u8fc7\u7a0b\u53c2\u6570\r\n\u2502\r\n\u251c\u2500\u2500 model/ # \u6a21\u578b\u6587\u4ef6\u5939\r\n\u2502 \u2514\u2500\u2500 CNN_XKS.h5 # \u5df2\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6743\u91cd\r\n\u2502\r\n\u251c\u2500\u2500 train/ # \u6a21\u578b\u8bad\u7ec3\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 2_train/ # \u8bad\u7ec3\u8f93\u51fa\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u251c\u2500\u2500 CNN_XKS.h5 # \u8bad\u7ec3\u540e\u7684\u6a21\u578b\u6743\u91cd\r\n\u2502 \u2502 \u251c\u2500\u2500 parameters.list # \u8bad\u7ec3\u8fc7\u7a0b\u53c2\u6570(\u5355\u72ec\u5199\u51fa\u6765\uff0c\u65b9\u4fbf\u6539\u52a8)\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.acc # \u8bad\u7ec3\u7cbe\u5ea6\u8bb0\u5f55\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.loss # \u8bad\u7ec3\u635f\u5931\u8bb0\u5f55\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.val_acc # \u9a8c\u8bc1\u7cbe\u5ea6\u8bb0\u5f55\r\n\u2502 \u2502 \u2514\u2500\u2500 train_64.val_loss # \u9a8c\u8bc1\u635f\u5931\u8bb0\u5f55\r\n\u2502 \u2514\u2500\u2500 train.py # \u6a21\u578b\u8bad\u7ec3\u811a\u672c\r\n\u2502\r\n\u251c\u2500\u2500 test/ # \u6d4b\u8bd5\u6587\u4ef6\u5939\r\n\u2502 \u2514\u2500\u2500 test.py # \u6a21\u578b\u53ef\u89c6\u5316\u548c\u6d4b\u8bd5\u811a\u672c\r\n\u2502\r\n\u251c\u2500\u2500 Do_load.cmd # \u52a0\u8f7d\u547d\u4ee4\u811a\u672c\r\n\u251c\u2500\u2500 Do_train.cmd # \u8bad\u7ec3\u547d\u4ee4\u811a\u672c\r\n\u2514\u2500\u2500 README.txt # \u9879\u76ee\u8bf4\u660e\u6587\u4ef6\r\n\r\n\r\n```\r\n\r\n### load.py\r\n ```\r\nimport os\r\nimport numpy as np\r\nfrom obspy import read\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Conv1D, ZeroPadding1D, Flatten, Dense\r\n\r\nX = [] # \u6570\u636e\u5217\u8868\r\nY = [] # \u6807\u7b7e\u5217\u8868\r\nX_nst, Y_nev = [], [] # \u53f0\u7ad9\u540d\u548c\u4e8b\u4ef6\u540d\r\ninput_length = 1000\r\n\r\n# \u6570\u636e\u548c\u6a21\u578b\u52a0\u8f7d\r\nnrt = os.path.normpath('C:/Users/~/S-wave spliting/CNN-SWS-main/1_data')\r\nnmodel = os.path.normpath('C:/Users/~/S-wave spliting/CNN-SWS-main/model/CNN_XKS.h5')\r\n# \u8def\u5f84\u68c0\u67e5\r\nif not os.path.exists(nrt):\r\n raise FileNotFoundError(f'The data root path {nrt} does not exist.')\r\nif not os.path.exists(nmodel):\r\n raise FileNotFoundError(f'The model path {nmodel} does not exist.')\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6SAC\u6570\u636e\r\nXKS = ['PKS', 'SKS', 'SKK']\r\n\r\nfor k in range(3):\r\n XKS_rout = os.path.join(nrt, f'{XKS[k]}.list') # C:/Users/~/main/1_data/Out_Bin/*.out\r\n print(f'Reading {XKS_rout}')\r\n\r\n if not os.path.exists(XKS_rout):\r\n raise FileNotFoundError(f'The file {XKS_rout} does not exist.')\r\n\r\n# \u9010\u884c\u8bfb\u53d6\u6570\u636e\r\nwith open(XKS_rout, 'r') as Pl:\r\n for line_Pl in Pl:\r\n vals = line_Pl.split()\r\n P_rout = os.path.join(nrt, vals[0]) # \u6570\u636e\u6839\u76ee\u5f55nrt \uff0b .out\u6587\u4ef6\u7b2c\u4e00\u5217\r\n print(f'Doing: {XKS[k]} {vals[0]}')\r\n\r\n if not os.path.exists(P_rout):\r\n raise FileNotFoundError(f'The file {P_rout} does not exist.')\r\n```\r\n```\r\nPKS, y = [], [] # PKS\u7528\u4e8e\u50a8\u5b58\u6ce2\u5f62\u6570\u636e\uff0cy\u50a8\u5b58\u5206\u7c7b\u6807\u7b7e\r\nwith open(P_rout, 'r') as P:\r\n for line in P:\r\n vals = line.split()\r\n nst = vals[0] # station name\r\n nev = vals[1] # event name\r\n\r\n # \u5904\u7406\u5206\u7c7b\u6807\u7b7e\r\n if vals[2] in ['A', 'B']:\r\n y.append(1)\r\n y.append(0)\r\n else:\r\n y.append(0)\r\n y.append(1)\r\n\r\n```\r\n```\r\n\r\nncom = ['.ro', '.to', '.rl', '.tl'] # 4\u5206\u91cf\u5217\u8868 \r\ncomponents = []\r\nfor i in range(4):\r\n ro_rout = os.path.join(nrt, f'{XKS[k]}Out', nst, nev, f'{nst}{ncom[i]}')\r\n print(f'Reading file: {ro_rout}')\r\n\r\n if os.path.exists(ro_rout):\r\n st = read(ro_rout)\r\n components.append(st[0].data[:input_length]) # \u622a\u53d6\u524dinput_length\u4e2a\u6570\u636e\r\n else:\r\n raise FileNotFoundError(f'The file {ro_rout} does not exist.')\r\n\r\nfor i in range(input_length):\r\n PKS.append(np.array([comp[i] for comp in components]))\r\n\r\nX.append(np.array(PKS)) # \u5c064\u4e2a\u5206\u91cf\u7ec4\u6210\u7684PKS\u4fdd\u5b58\u5230\u5217\u8868X\u4e2d\r\nY.append(np.array(y)) # \u5206\u7c7b\u6807\u7b7e\u4fdd\u5b58\u5230Y\u4e2d\r\nX_nst.append(f'{nst}_{XKS[k]}_') # \u53f0\u7ad9\u4fe1\u606f\r\nY_nev.append(nev) # \u4e8b\u4ef6\u4fe1\u606f\r\n\r\n```\r\n\r\n```\r\n# \u5b9a\u4e49\u6a21\u578b\r\ninput_shape = (input_length, 4)\r\nmodel = Sequential()\r\n # \u6dfb\u52a0\u5377\u79ef\u5c42\r\nmodel.add(Conv1D(kernel_size=3, filters=32, input_shape=input_shape, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(units=2, activation='softmax'))\r\n\r\n# \u52a0\u8f7d\u6a21\u578b\u6743\u91cd\u5e76\u8fdb\u884c\u9884\u6d4b\r\nmodel.load_weights(nmodel)\r\nresult = model.predict(np.array(X))\r\n\r\n# \u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c\r\noutput_dir = os.path.join('C:/Users/~/S-wave spliting/CNN-SWS-main/load/2_load/Outp')\r\nos.makedirs(output_dir, exist_ok=True)\r\nfor i in range(len(result)):\r\n nst = X_nst[i] \r\n nev = Y_nev[i] \r\n res_name = os.path.join(output_dir, f'{nst}_{nev}.res') \r\n y_name = os.path.join(output_dir, f'{nst}_{nev}.y')\r\n\r\n np.savetxt(res_name, result[i])\r\n np.savetxt(y_name, Y[i])\r\n\r\nprint('finish')\r\n```\r\n\r\n\r\n\r\n### train.py\r\n\r\n```\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport obspy\r\nimport csv\r\nfrom obspy import read\r\nfrom obspy.taup import TauPyModel\r\nimport os\r\nfrom pathlib import Path\r\nimport random\r\nimport keras\r\nfrom keras import regularizers\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, Conv1D, MaxPooling1D, UpSampling1D, ZeroPadding1D\r\n\r\n# \u521d\u59cb\u5316\r\nX_good, Y_good = [], []\r\nX_bad, Y_bad = [], []\r\nX, Y = [], []\r\nX_rand, Y_rand = [], []\r\nnst_good, nev_good = [], []\r\nnst_bad, nev_bad = [], []\r\nX_nst, Y_nev = [], []\r\nnst_rand, nev_rand = [], []\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6\u53c2\u6570\u6587\u4ef6\r\nn=0\r\nwith open('parameters.list') as p:\r\n for line in p:\r\n n += 1\r\n vals = line.split()\r\n if n == 1: nrt = str(vals[0])\r\n if n == 2: batch_size = int(vals[0])\r\n if n == 3: epochs = int(vals[0])\r\n if n == 4: byn = int(vals[0])\r\n if n == 5:\r\n ac = int(vals[0])\r\n uc = int(vals[1])\r\n\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6SAC\u6570\u636e\r\ninput_length = 1000\r\nXKS = ['PKS', 'SKS', 'SKK']\r\n\r\nfor k in range(3):\r\n XKS_rout = nrt + str(XKS[k]) + '.list'\r\n with open(XKS_rout) as Pl:\r\n for line_Pl in Pl:\r\n vals = line_Pl.split()\r\n P_rout = nrt + str(vals[0])\r\n with open(P_rout) as P:\r\n for line in P:\r\n PKS, y = [], []\r\n vals = line.split()\r\n nst = vals[0] # station name\r\n nev = vals[1] # event name\r\n y = [1, 0] if vals[2] in ['A', 'B'] else [0, 1]\r\n \r\n ncom = ['.ro', '.to', '.rl', '.tl']\r\n for i in range(4):\r\n ro_rout = f'{nrt}{XKS[k]}Out/{nst}/{nev}/{nst}{ncom[i]}'\r\n st = read(ro_rout)\r\n if i == 0: ro = st[0].data\r\n if i == 1: to = st[0].data\r\n if i == 2: rl = st[0].data\r\n if i == 3: tl = st[0].data\r\n \r\n for i in range(input_length):\r\n PKS.append(np.array([ro[i], to[i], rl[i], tl[i]]))\r\n if y[0] == 1:\r\n X_good.append(PKS)\r\n Y_good.append(y)\r\n nst_good.append(f'{nst}_{XKS[k]}_')\r\n nev_good.append(nev)\r\n else:\r\n X_bad.append(PKS)\r\n Y_bad.append(y)\r\n nst_bad.append(f'{nst}_{XKS[k]}_')\r\n nev_bad.append(nev)\r\n```\r\n```\r\n# \u6570\u636e\u589e\u5f3a\uff08\u901a\u8fc7\u500d\u589e\u6765\u5e73\u8861\u6570\u636e\u96c6\u4e2d\u7684\u7c7b\u522b\u6570\u91cf\uff09\r\nnpts = int(len(X_bad) / len(X_good))\r\nclass_weight = {0: ac, 1: uc}\r\nif byn == 0: npts = 1\r\n\r\nfor i in range(npts):\r\n for ii in range(len(X_good)):\r\n X.append(X_good[ii])\r\n Y.append(Y_good[ii])\r\n X_nst.append(nst_good[ii])\r\n Y_nev.append(nev_good[ii])\r\n\r\nfor i in range(len(X_bad)):\r\n X.append(X_bad[i])\r\n Y.append(Y_bad[i])\r\n X_nst.append(nst_bad[i])\r\n Y_nev.append(nev_bad[i])\r\n\r\n# \u6570\u636e\u968f\u673a\u5316\u4e0e\u5212\u5206\r\nrann0 = random.sample(range(len(X)), len(X))\r\nX_rand = [X[i] for i in rann0]\r\nY_rand = [Y[i] for i in rann0]\r\nx_train, y_train = np.array(X_rand[:int(len(X) * 0.8)]), np.array(Y_rand[:int(len(Y) * 0.8)])\r\nx_test, y_test = np.array(X_rand[int(len(X) * 0.8):]), np.array(Y_rand[int(len(Y) * 0.8):])\r\n\r\n```\r\n```\r\nmodel = Sequential()\r\nmodel.add(Conv1D(32, kernel_size=3, strides=2, activation='relu', input_shape=(input_length, 4)))\r\nmodel.add(ZeroPadding1D(1))\r\n# \u6dfb\u52a0\u591a\u4e2a\u5377\u79ef\u5c42\uff0c\u6700\u7ec8\u5c55\u5e73\u6210\u5411\u91cf\u5e76\u8fde\u63a5\u5230 softmax \u8f93\u51fa\u5c42\r\nmodel.add(Flatten())\r\nmodel.add(Dense(2, activation='softmax'))\r\n\r\nmodel.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.001), metrics=['accuracy'])\r\nH = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, class_weight=class_weight, validation_data=(x_test, y_test))\r\n\r\n# \u53ef\u89c6\u5316\u8bad\u7ec3\u548c\u9a8c\u8bc1\u7cbe\u5ea6\r\nfig, ax = plt.subplots()\r\nplt.plot(H.history['acc'], label='train_acc')\r\nplt.plot(H.history['val_acc'], label='val_acc')\r\nplt.legend()\r\nplt.show()\r\n\r\nmodel.save_weights('CNN_XKS.h5')\r\nprint('Finish')\r\n\r\n```\r\n\r\n\u3002", "top": 0, "createdAt": 1730863802, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-06", "dateLabelColor": "#bc4c00"}, "P5": {"htmlDir": "docs/post/wei-shen-me-python-lei-zhong-yao-shi-yong-__init__()-te-shu-fang-fa.html", "labels": ["point"], "postTitle": "\u4e3a\u4ec0\u4e48python\u7c7b\u4e2d\u8981\u4f7f\u7528__init__()\u7279\u6b8a\u65b9\u6cd5", "postUrl": "post/wei-shen-me-python-lei-zhong-yao-shi-yong-__init__%28%29-te-shu-fang-fa.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/5", "commentNum": 0, "wordCount": 1694, "description": "\u4eca\u5929\u770b\u5230\u5982\u4e0b\u4ee3\u7801\uff08\u4e00\u4e2a\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\u7c7b\uff09\uff0c\u5f15\u53d1\u4e86\u7b14\u8005\u56f0\u6270\u5df2\u4e45\u7684\u95ee\u9898\uff1a__init__()\u7279\u6b8a\u65b9\u6cd5\u5230\u5e95\u6709\u4ec0\u4e48\u7528\uff0c\u4e3a\u4ec0\u4e48python\u7c7b\u4e2d\u8981\u4f7f\u7528__init__()\u7279\u6b8a\u65b9\u6cd5\uff1f\r\n```\r\nclass LRScheduler():\r\n\t'''\r\n\tLearning rate scheduler. If the validation loss does not decrease for the\r\n\tgiven number of `patience` epochs, then the learning rate will decrease by\r\n\tby given `factor`.\r\n\t'''\r\n\tdef __init__(self, optimizer, patience=7, min_lr=1e-6, factor=0.5):\r\n\t\t'''\r\n\t\tnew_lr = old_lr * factor\r\n\t\t:param optimizer: the optimizer we are using\r\n\t\t:param patience: how many epochs to wait before updating the lr\r\n\t\t:param min_lr: least lr value to reduce to while updating\r\n\t\t:param factor: factor by which the lr should be updated\r\n\t\t'''\r\n\t\tself.optimizer = optimizer\r\n\t\tself.patience = patience\r\n\t\tself.min_lr = min_lr\r\n\t\tself.factor = factor\r\n\t\tself.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\r\n\t\t\t\tself.optimizer,\r\n\t\t\t\tmode='min',\r\n\t\t\t\tpatience=self.patience,\r\n\t\t\t\tfactor=self.factor,\r\n\t\t\t\tmin_lr=self.min_lr,\r\n\t\t\t\tverbose=True\r\n\t\t\t)\r\n\tdef __call__(self, val_loss):\r\n\t\tself.lr_scheduler.step(val_loss)\r\n```\r\n\r\n__init__\u662f\u4e00\u4e2a\u7279\u6b8a\u65b9\u6cd5\uff0c\u89e3\u91ca\u4e3a\u7c7b\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u6216\u6784\u9020\u5668\uff0c\u529f\u80fd\u4e5f\u5c31\u4e0d\u8a00\u800c\u55bb\u4e86\uff0c\u5f53\u521b\u5efa\u4e00\u4e2a\u7c7b\u7684\u5b9e\u4f8b\u65f6\uff0cpython\u4f1a\u81ea\u52a8\u8c03\u7528\u8fd9\u4e2a\u65b9\u6cd5\u3002", "top": 0, "createdAt": 1730981462, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-07", "dateLabelColor": "#bc4c00"}, "P6": {"htmlDir": "docs/post/[Literature Reading]Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution.html", "labels": ["documentation"], "postTitle": "[Literature Reading]Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution", "postUrl": "post/%5BLiterature%20Reading%5DFeasibility%20of%20Deep%20Learning%20in%20Shear%20Wave%20Splitting%20analysis%20using%20Synthetic-Data%20Training%20and%20Waveform%20Deconvolution.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/6", "commentNum": 0, "wordCount": 7359, "description": "## Abstract\r\n\r\n\u80cc\u666f\u4e0e\u4f20\u7edf\u65b9\u6cd5\uff1a\u4f20\u7edf\u65b9\u6cd5\u901a\u8fc7\u9006\u8f6c\u5206\u88c2\u8fc7\u7a0b\uff0c\u901a\u8fc7\u9891\u57df\u548c\u65f6\u57df\u64cd\u4f5c\uff0c\u6700\u5c0f\u5316\u6ce2\u5f62\u5207\u5411\u80fd\u91cf\uff0c\u5f97\u5230\u5206\u88c2\u53c2\u6570\u3002", "top": 0, "createdAt": 1731552896, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-14", "dateLabelColor": "#bc4c00"}, "P7": {"htmlDir": "docs/post/[Literature Reading]SWAS- A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes.html", "labels": ["documentation"], "postTitle": "[Literature Reading]SWAS: A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes", "postUrl": "post/%5BLiterature%20Reading%5DSWAS-%20A%20shear-wave%20analysis%20system%20for%20semi-automatic%20measurement%20of%20shear-wave%20splitting%20above%20small%20earthquakes.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/7", "commentNum": 1, "wordCount": 3381, "description": "# SWAS: A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes\r\n## Abstract\r\n \u95ee\u9898\uff1a\u5c0f\u9707\u526a\u5207\u6ce2\u5230\u8fbe\u590d\u6742\uff0c\u526a\u5207\u6ce2\u6d4b\u91cf\u56f0\u96be\r\n \u65b9\u6cd5\uff1a\u5f00\u53d1SAWS\u4e13\u5bb6\u7cfb\u7edf\uff0c\u81ea\u52a8\u4f30\u8ba1\u5feb\u6ce2\u6781\u5316\u65b9\u5411\u548c\u526a\u5207\u6ce2\u5230\u65f6\uff0c\u4eba\u5de5\u8f85\u52a9\u8c03\u6574\uff08\u5728\u539f\u59cb\u5730\u9707\u56fe\u3001\u65cb\u8f6c\u5730\u9707\u56fe\u548c\u6781\u5316\u56fe\u4e4b\u95f4\u8fdb\u884c\u8c03\u6574\uff09\r\n \u6570\u636e\uff1a\u51b0\u5c9bSIL\u5730\u9707\u7f51\u7edc\u6570\u636e\r\n\r\n ```\r\n \u91cc\u6c0f\u9707\u7ea7\uff1a\u57fa\u4e8e\u5730\u9707\u6ce2\u632f\u5e45\uff0c\u5728\u8fd9\u4e2a\u6807\u5ea6\u4e2d\uff0c\u4e3a\u4e86\u4f7f\u7ed3\u679c\u4e0d\u4e3a\u8d1f\u6570\uff0c\u91cc\u514b\u7279\u5b9a\u4e49\u5728\u8ddd\u79bb\u9707\u4e2d100\u5343\u7c73\u5904\u4e4b\u89c2\u6d4b\u70b9\u5730 \r\n \u9707\u4eea\u8bb0\u5f55\u5230\u7684\u6700\u5927\u6c34\u5e73\u4f4d\u79fb\u4e3a1\u5fae\u7c73\uff08\u8fd9\u4e5f\u662f\u4f0d\u5fb7-\u5b89\u5fb7\u68ee\u626d\u529b\u5f0f\u5730\u9707\u4eea\u7684\u6700\u5927\u7cbe\u5ea6\uff09\u7684\u5730\u9707\u4f5c\u4e3a0\u7ea7\u5730\u9707\uff0c\u5f53\u5730\u9707 \r\n \u4eea\u8bb0\u5f55\u5230\u7684\u6700\u5927\u6c34\u5e73\u4f4d\u79fb\u5c0f\u4e8e1\u5fae\u7c73\uff0c\u9707\u7ea7\u4fbf\u4e3a\u8d1f\u3002", "top": 0, "createdAt": 1731647019, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-15", "dateLabelColor": "#bc4c00"}, "P8": {"htmlDir": "docs/post/[Literature Reading]ES for measuring SWS.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]ES for measuring SWS", "postUrl": "post/%5BLiterature%20Reading%5DES%20for%20measuring%20SWS.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/8", "commentNum": 2, "wordCount": 5321, "description": "## Abstract\r\n \u4e13\u5bb6\u7cfb\u7edf\u548c\u6a21\u5757\u5316\u8bbe\u8ba1\r\n\r\n## 1.Introduction\r\n ```\r\n\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff08Artificial Neural Networks, 1995\uff09\uff1a\r\n\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u8bc6\u522b\u526a\u5207\u6ce2\u5206\u88c2\uff0c\u80fd\u591f\u5904\u7406\u590d\u6742\u7684\u6ce2\u5f62\u3002", "top": 0, "createdAt": 1731895696, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-18", "dateLabelColor": "#bc4c00"}, "P9": {"htmlDir": "docs/post/[Literature Reading] -yi-zhong-shi-yong-yu-di-fang-zhen-shi-jian-de-S-bo-dao-shi-zi-dong-shi-qu-fang-fa.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading] \u4e00\u79cd\u9002\u7528\u4e8e\u5730\u65b9\u9707\u4e8b\u4ef6\u7684S\u6ce2\u5230\u65f6\u81ea\u52a8\u62fe\u53d6\u65b9\u6cd5", "postUrl": "post/%5BLiterature%20Reading%5D%20-yi-zhong-shi-yong-yu-di-fang-zhen-shi-jian-de-S-bo-dao-shi-zi-dong-shi-qu-fang-fa.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/9", "commentNum": 0, "wordCount": 3181, "description": "## \u5f15\u8a00\r\n\r\n S\u6ce2\u62fe\u53d6\u56f0\u96be:\u53d7\u5230P\u6ce2\u5c3e\u6ce2\u53ca\u5176\u4ed6\u8f6c\u6362\u6ce2\u9707\u76f8\u5f71\u54cd\uff0c\u56e0\u6b64S\u6ce2\u7684\u4fe1\u566a\u6bd4\u4e00\u822c\u4f4e\u4e8eP\u6ce2\uff0c\u62fe\u53d6\u7684\u51c6\u786e\u5ea6\u4e5f\u6bd4\u8f83\u4f4e\uff1b\r\n \u73b0\u6709\u624b\u52a8\u62fe\u53d6\u65b9\u6cd5\uff1a\u57fa\u4e8e\u6781\u5316\u7279\u5f81\uff0c\u5229\u7528\u539f\u59cb\u5730\u9707\u4e09\u5206\u91cf\u8bb0\u5f55\uff0c\u6839\u636eP\u6ce2\u548cS\u6ce2\u5728\u504f\u632f\u65b9\u5411\u4e0a\u7684\u4e0d\u540c\u7279\u5f81\uff08\u5982\u8d28\u70b9\u8fd0\u52a8\u7684\u504f\u632f\u5ea6\u3001\u7ebf\u6027\u5ea6\u7b49\uff09\uff0c\u627e\u5230\u9707\u76f8\u7a81\u53d8\u70b9\uff0c\u8fdb\u800c\u786e\u5b9aS\u6ce2\u5230\u65f6\u3002", "top": 0, "createdAt": 1732174245, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-21", "dateLabelColor": "#bc4c00"}, "P10": {"htmlDir": "docs/post/zhen-xiang-shi-qu.html", "labels": ["documentation", "enhancement"], "postTitle": "\u9707\u76f8\u62fe\u53d6", "postUrl": "post/zhen-xiang-shi-qu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/10", "commentNum": 0, "wordCount": 2902, "description": "[PhaseNet <\u8f6c\u8f7d>](https://blog.csdn.net/qq_40206371/article/details/129748282?utm_source=chatgpt.com)\r\n\r\n\r\n---\r\n\r\n### \u9707\u76f8\u62fe\u53d6\u4e0e\u81ea\u52a8\u5316\u6280\u672f\u7684\u7814\u7a76\u80cc\u666f\u4e0e\u53d1\u5c55\r\n\r\n#### \u603b\u7ed3\uff1a\r\n\u5730\u9707\u9707\u76f8\u4fe1\u606f\u662f\u5730\u9707\u5b66\u7814\u7a76\u4e2d\u7684\u91cd\u8981\u57fa\u7840\u6570\u636e\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5730\u9707\u5b9a\u4f4d\u3001\u9707\u6e90\u673a\u5236\u5206\u6790\u548c\u8d70\u65f6\u5c42\u6790\u6210\u50cf\u7b49\u9886\u57df\u3002", "top": 0, "createdAt": 1732175989, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-21", "dateLabelColor": "#bc4c00"}, "P13": {"htmlDir": "docs/post/[Literature Reading]-juan-ji-shen-jing-wang-luo-zai-yuan---jin-di-zhen-zhen-xiang-shi-qu-zhong-de-ying-yong-ji-mo-xing-jie-shi.html", "labels": ["documentation"], "postTitle": "[Literature Reading]\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u8fdc-\u8fd1\u5730\u9707\u9707\u76f8\u62fe\u53d6\u4e2d\u7684\u5e94\u7528\u53ca\u6a21\u578b\u89e3\u91ca", "postUrl": "post/%5BLiterature%20Reading%5D-juan-ji-shen-jing-wang-luo-zai-yuan---jin-di-zhen-zhen-xiang-shi-qu-zhong-de-ying-yong-ji-mo-xing-jie-shi.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/13", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1732684092, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-27", "dateLabelColor": "#bc4c00"}, "P14": {"htmlDir": "docs/post/[Literature Reading]-jian-gu-su-du-he-jing-du-de-shen-du-shen-jing-wang-luo-zhen-xiang-shi-qu.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading]\u517c\u987e\u901f\u5ea6\u548c\u7cbe\u5ea6\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u9707\u76f8\u62fe\u53d6", "postUrl": "post/%5BLiterature%20Reading%5D-jian-gu-su-du-he-jing-du-de-shen-du-shen-jing-wang-luo-zhen-xiang-shi-qu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/14", "commentNum": 0, "wordCount": 4602, "description": "## \u6458\u8981\r\n \u6839\u636e\u5730\u9707\u6ce2\u5f62\u7684\u7279\u70b9\u8bbe\u8ba1\u4e86\u56db\u79cd\u5177\u6709\u4e0d\u540c\u590d\u6742\u5ea6\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u6539\u8fdb\u6a21\u578b\uff0c\u53ef\u4ee5\u7efc\u5408\u5177\u4f53\u7684\u7cbe\u5ea6\u548c\u901f\u5ea6\u9700\u6c42\u4ece\u4e2d\u9009\u53d6\u5408\u9002\u7684\u6a21\u578b\uff0c\u5c06\u6539\u8fdb\u6a21\u578b\u4e0e\u73b0\u6709\u56db\u79cd\u5230\u65f6\u62fe\u53d6\u7684\u6df1\u5ea6\u5b66\u6a21\u578b\u4f5c\u5bf9\u6bd4\u3002", "top": 0, "createdAt": 1732685235, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-27", "dateLabelColor": "#bc4c00"}, "P15": {"htmlDir": "docs/post/[Literature Reading]-jin-zhen-S-bo-zhen-xiang-shi-shi-zi-dong-shi-bie-fang-fa-yan-jiu.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading]\u8fd1\u9707S\u6ce2\u9707\u76f8\u5b9e\u65f6\u81ea\u52a8\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76", "postUrl": "post/%5BLiterature%20Reading%5D-jin-zhen-S-bo-zhen-xiang-shi-shi-zi-dong-shi-bie-fang-fa-yan-jiu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/15", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1732772635, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-28", "dateLabelColor": "#bc4c00"}, "P16": {"htmlDir": "docs/post/[Literrture Reading]DeepPhasePick- a method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks.html", "labels": ["GJI"], "postTitle": "[Literrture Reading]DeepPhasePick: a method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks", "postUrl": "post/%5BLiterrture%20Reading%5DDeepPhasePick-%20a%20method%20for%20detecting%20and%20picking%20seismic%20phases%20from%20local%20earthquakes%20based%20on%20highly%20optimized%20convolutional%20and%20recurrent%20deep%20neural%20networks.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/16", "commentNum": 0, "wordCount": 798, "description": "## Summary\r\n\u76f8\u4f4d\u68c0\u6d4b\u3001\u8bc6\u522b\u548c\u521d\u81f3\u65f6\u95f4\u662f\u5206\u6790\u5730\u9707\u6570\u636e\u7684\u57fa\u7840\u4e14\u91cd\u8981\u7684\u5e38\u89c4\u5de5\u4f5c\u3002", "top": 0, "createdAt": 1732776828, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-28", "dateLabelColor": "#bc4c00"}, "P17": {"htmlDir": "docs/post/[Literature Reading]Pytheas- An open-source software solution for local shear-wave splitting studies .html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]Pytheas: An open-source software solution for local shear-wave splitting studies ", "postUrl": "post/%5BLiterature%20Reading%5DPytheas-%20An%20open-source%20software%20solution%20for%20local%20shear-wave%20splitting%20studies%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/17", "commentNum": 0, "wordCount": 6583, "description": "## Abstract\r\n\u63d0\u4f9b\u4e86\u5305\u62ec\u89c6\u89c9\u68c0\u67e5\u3001\u65cb\u8f6c\u76f8\u5173\u6cd5\u3001\u7279\u5f81\u503c\u6cd5\u548c\u6700\u5c0f\u80fd\u91cf\u6cd5\u5728\u5185\u7684\u591a\u79cd\u5206\u6790\u5de5\u5177,\u5e76\u901a\u8fc7\u805a\u7c7b\u5206\u6790\u5b9e\u73b0\u81ea\u52a8\u5316\u5904\u7406\u3002", "top": 0, "createdAt": 1733208364, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-03", "dateLabelColor": "#bc4c00"}, "P18": {"htmlDir": "docs/post/[Review]-li-yong-heng-bo-fen-lie-fen-xi-fang-fa-yan-jiu-di-ke-ge-xiang-yi-xing-zong-shu.html", "labels": ["other"], "postTitle": "[Review]\u5229\u7528\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u65b9\u6cd5\u7814\u7a76\u5730\u58f3\u5404\u5411\u5f02\u6027\u7efc\u8ff0", "postUrl": "post/%5BReview%5D-li-yong-heng-bo-fen-lie-fen-xi-fang-fa-yan-jiu-di-ke-ge-xiang-yi-xing-zong-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/18", "commentNum": 0, "wordCount": 3893, "description": " ## \r\n\u5404\u5411\u5f02\u6027\u5b9a\u4e49---\u8d77\u6e90---\u5730\u9707\u5404\u5411\u5f02\u6027\uff0c\u610f\u4e49---\u76f4\u89c2\u8868\u73b0(S\u6ce2\u5206\u88c2)---\u5206\u88c2\u53c2\u6570---\u5f71\u54cd\u56e0\u7d20(\u4e3b\u8981\u56e0\u7d20\uff0c\u5176\u4ed6\u56e0\u7d20)\r\n```\r\n\u4e3b\u8981\u56e0\u7d20\uff1a\u88c2\u7f1d\u6216\u4e3b\u538b\u5e94\u529b\u65b9\u5411\u3001\u6df1\u90e8\u7269\u8d28\u6d41\u52a8\u65b9\u5411\u3001\u77ff\u7269\u6676\u683c\u4f18\u52bf\u6392\u5217\u65b9\u5411\uff08LPO\uff09\r\n\u5176\u4ed6\u56e0\u7d20\uff1a\u5feb\u6162\u6ce2\u7684\u6ce2\u5f62\u5dee\u5f02\uff1a \u5feb\u6a2a\u6ce2\u548c\u6162\u6a2a\u6ce2\u6ce2\u5f62\u4e0d\u540c\uff0c\u6162\u6a2a\u6ce2\u7684\u8870\u51cf\u66f4\u660e\u663e\uff0c\u521d\u52a8\u8f83\u5f31\u3002", "top": 0, "createdAt": 1733412990, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-05", "dateLabelColor": "#bc4c00"}, "P19": {"htmlDir": "docs/post/[Review]A review of techniques for measuring shear-wave splitting above small earthquakes.html", "labels": ["other"], "postTitle": "[Review]A review of techniques for measuring shear-wave splitting above small earthquakes", "postUrl": "post/%5BReview%5DA%20review%20of%20techniques%20for%20measuring%20shear-wave%20splitting%20above%20small%20earthquakes.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/19", "commentNum": 0, "wordCount": 4740, "description": "## Abstrct\r\n\u4ece\u4f20\u7edf\u7684\u624b\u52a8\u89c6\u89c9\u6280\u672f\u5230\u81ea\u52a8\u5316\u6280\u672f\u7684\u53d1\u5c55\uff0c\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u79cd\u7ed3\u5408\u89c6\u89c9\u548c\u81ea\u52a8\u5316\u6280\u672f\u7684\u534a\u81ea\u52a8\u5316\u6d4b\u91cf\u65b9\u6cd5\r\n\r\n## 1. Introduction\r\n**\u526a\u5207\u6ce2\u5206\u88c2\u7684\u6210\u56e0\u4e0e\u7279\u5f81**\r\n \u5404\u5411\u5f02\u6027\u4ecb\u8d28\u4e2d\uff08\u5982\u5730\u4e0b\u7684\u5fae\u88c2\u7f1d\uff09\uff0c\u5176\u4e2d\u526a\u5207\u6ce2\u5206\u88c2\u6210\u4e24\u76f8\uff0c\u5206\u522b\u4e3a\u5feb\u6ce2\u548c\u6162\u6ce2\uff0c\u5e76\u4e14\u5b83\u4eec\u4ee5\u4e0d\u540c\u7684\u901f\u5ea6\u4f20\u64ad\u3002", "top": 0, "createdAt": 1733710968, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-09", "dateLabelColor": "#bc4c00"}, "P20": {"htmlDir": "docs/post/[Review]-li-yong-duo-zhong-heng-bo-fen-lie-fen-xi-fang-fa-ping-gu-que-ding-ge-xiang-yi-xing-can-shu.html", "labels": ["\u5730\u7403\u7269\u7406\u5b66\u62a5"], "postTitle": "[Review]\u5229\u7528\u591a\u79cd\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u65b9\u6cd5\u8bc4\u4f30\u786e\u5b9a\u5404\u5411\u5f02\u6027\u53c2\u6570", "postUrl": "post/%5BReview%5D-li-yong-duo-zhong-heng-bo-fen-lie-fen-xi-fang-fa-ping-gu-que-ding-ge-xiang-yi-xing-can-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/20", "commentNum": 0, "wordCount": 2738, "description": "## \u6458\u8981 \r\n\u80cc\u666f\uff1a\u6570\u636e\u7684\u566a\u58f0\u6c34\u5e73\u3001\u89c2\u6d4b\u65b9\u4f4d\u5206\u5e03\u4ee5\u53ca\u4ecb\u8d28\u7684\u590d\u6742\u7a0b\u5ea6\u90fd\u4f1a\u5f71\u54cd\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u7ed3\u679c\u7684\u7a33\u5b9a\u6027\u548c\u51c6\u786e\u6027\u3002", "top": 0, "createdAt": 1733723969, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-09", "dateLabelColor": "#bc4c00"}, "P21": {"htmlDir": "docs/post/[Literature Reading]Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements.html", "labels": ["SRL"], "postTitle": "[Literature Reading]Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements", "postUrl": "post/%5BLiterature%20Reading%5DUsing%20Convolutional%20Neural%20Network%20to%20Determine%20Time%20Window%20for%20Analyzing%20Local%20Shear-Wave%20Splitting%20Measurements.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/21", "commentNum": 0, "wordCount": 3951, "description": "## Abstract\r\n```\r\n\u7814\u7a76\u5229\u7528CNN\u6765\u786e\u5b9a\u65f6\u95f4\u7a97\u53e3\u7684\u7ed3\u675f\u4f4d\u7f6e(e)\uff0c\u5e76\u4e14\u8bbe\u5b9a\u65f6\u95f4\u7a97\u53e3\u4ecee\u524d0.5\u79d2\u5f00\u59cb\u3002", "top": 0, "createdAt": 1733838287, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-10", "dateLabelColor": "#bc4c00"}, "P22": {"htmlDir": "docs/post/[Literature Reading]Automatic measurement of shear wave splitting and applications to time varying anisotropy at Mount Ruapehu volcano, New Zealand.html", "labels": ["JGR"], "postTitle": "[Literature Reading]Automatic measurement of shear wave splitting and applications to time varying anisotropy at Mount Ruapehu volcano, New Zealand", "postUrl": "post/%5BLiterature%20Reading%5DAutomatic%20measurement%20of%20shear%20wave%20splitting%20and%20applications%20to%20time%20varying%20anisotropy%20at%20Mount%20Ruapehu%20volcano%2C%20New%20Zealand.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/22", "commentNum": 0, "wordCount": 9554, "description": "# MFAST\r\n\r\n## Abstract\r\n\u81ea\u52a8\u5316\u6d41\u7a0b\uff1a\u4ec5\u9700\u4eba\u5de5\u9009\u62e9S\u6ce2\u5230\u8fbe\u65f6\u95f4\uff0c\u5176\u4ed6\u6b65\u9aa4\u5b8c\u5168\u81ea\u52a8\u5316\u3002", "top": 0, "createdAt": 1733897728, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-11", "dateLabelColor": "#bc4c00"}, "P23": {"htmlDir": "docs/post/[Review]-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-di-zhen-zhen-xiang-shi-qu-fang-fa-yan-jiu.html", "labels": ["\u5730\u7403\u7269\u7406\u5b66\u62a5"], "postTitle": "[Review]\u57fa\u4e8e\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u5730\u9707\u9707\u76f8\u62fe\u53d6\u65b9\u6cd5\u7814\u7a76", "postUrl": "post/%5BReview%5D-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-di-zhen-zhen-xiang-shi-qu-fang-fa-yan-jiu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/23", "commentNum": 0, "wordCount": 5128, "description": "## \u6458\u8981\r\n1. \u7814\u7a76\u80cc\u666f\u4e0e\u95ee\u9898\r\n\u5730\u9707\u9707\u76f8\u62fe\u53d6\u662f\u5730\u9707\u6570\u636e\u81ea\u52a8\u5316\u5904\u7406\u4e2d\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u4e3b\u8981\u5305\u62ec\u4fe1\u53f7\u68c0\u6d4b\u3001\u9707\u76f8\u5230\u65f6\u4f30\u8ba1\u548c\u9707\u76f8\u8bc6\u522b\u7b49\u8fc7\u7a0b\u3002", "top": 0, "createdAt": 1734177907, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-14", "dateLabelColor": "#bc4c00"}, "P24": {"htmlDir": "docs/post/[Literature Reading]An automatized XKS-splitting procedure for large data sets- Extension package for SplitRacer and application to the USArray .html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]An automatized XKS-splitting procedure for large data sets: Extension package for SplitRacer and application to the USArray ", "postUrl": "post/%5BLiterature%20Reading%5DAn%20automatized%20XKS-splitting%20procedure%20for%20large%20data%20sets-%20Extension%20package%20for%20SplitRacer%20and%20application%20to%20the%20USArray%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/24", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1734589481, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-19", "dateLabelColor": "#bc4c00"}, "P25": {"htmlDir": "docs/post/[Literature Reading]-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-jian-qie-bo-fen-lie-zhi-liang-jian-ce.html", "labels": ["other"], "postTitle": "[Literature Reading]\u57fa\u4e8e\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u526a\u5207\u6ce2\u5206\u88c2\u8d28\u91cf\u68c0\u6d4b", "postUrl": "post/%5BLiterature%20Reading%5D-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-jian-qie-bo-fen-lie-zhi-liang-jian-ce.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/25", "commentNum": 0, "wordCount": 4575, "description": "## \u5f15\u8a00\r\n\r\n \u901a\u8fc7\u6d4b\u91cf\u5206\u88c2\u526a\u5207\u6ce2\u7684\u5feb\u6ce2\u6781\u5316\u65b9\u5411\uff08\u03c6\uff09\u548c\u6162\u6ce2\u5ef6\u8fdf\u65f6\u95f4\uff08\u03b4t\uff09\uff0c\u53ef\u4ee5\u63ed\u793a\u5730\u4e0b\u4ecb\u8d28\u7684\u5404\u5411\u5f02\u6027\u7279\u5f81\u3002", "top": 0, "createdAt": 1734616560, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-19", "dateLabelColor": "#bc4c00"}, "P26": {"htmlDir": "docs/post/[Review]-ji-yu-jian-qie-bo-fen-lie-de-di-qiu-nei-bu-ge-xiang-yi-xing-yan-jiu-zong-shu.html", "labels": ["other"], "postTitle": "[Review]\u57fa\u4e8e\u526a\u5207\u6ce2\u5206\u88c2\u7684\u5730\u7403\u5185\u90e8\u5404\u5411\u5f02\u6027\u7814\u7a76\u7efc\u8ff0", "postUrl": "post/%5BReview%5D-ji-yu-jian-qie-bo-fen-lie-de-di-qiu-nei-bu-ge-xiang-yi-xing-yan-jiu-zong-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/26", "commentNum": 0, "wordCount": 2910, "description": "\r\n## **1. \u5730\u7403\u5185\u90e8\u5404\u5411\u5f02\u6027**\r\n\r\n### **1.1 \u5b9a\u4e49**\r\n- **\u5404\u5411\u5f02\u6027**\u6307\u5730\u7403\u4ecb\u8d28\u7684\u7269\u7406\u548c\u5316\u5b66\u5c5e\u6027\u968f\u65b9\u5411\u7684\u4e0d\u540c\u800c\u53d8\u5316\u3002", "top": 0, "createdAt": 1734839763, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-22", "dateLabelColor": "#bc4c00"}, "P27": {"htmlDir": "docs/post/[Literature Reading]-jin-chang-di-zhen-kuai-man-heng-bo-dao-shi-cha-ce-liang-li-san-bian-xi-he-gai-zheng.html", "labels": ["other"], "postTitle": "[Literature Reading]\u8fd1\u573a\u5730\u9707\u5feb\u6162\u6a2a\u6ce2\u5230\u65f6\u5dee\u6d4b\u91cf\u79bb\u6563\u8fa8\u6790\u548c\u6539\u6b63", "postUrl": "post/%5BLiterature%20Reading%5D-jin-chang-di-zhen-kuai-man-heng-bo-dao-shi-cha-ce-liang-li-san-bian-xi-he-gai-zheng.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/27", "commentNum": 0, "wordCount": 2509, "description": "## 1. \u7814\u7a76\u80cc\u666f\u4e0e\u610f\u4e49\r\n\r\n### 1.1 \u526a\u5207\u6ce2\u5206\u88c2\u73b0\u8c61\r\n- \u526a\u5207\u6ce2\uff08S\u6ce2\uff09\u5206\u88c2\u662f\u6a2a\u6ce2\u5728\u901a\u8fc7\u5404\u5411\u5f02\u6027\u4ecb\u8d28\u65f6\u7684\u4e00\u79cd\u91cd\u8981\u73b0\u8c61\u3002", "top": 0, "createdAt": 1735019446, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-24", "dateLabelColor": "#bc4c00"}, "P28": {"htmlDir": "docs/post/[Literature Reading]splitracer.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]splitracer", "postUrl": "post/%5BLiterature%20Reading%5Dsplitracer.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/28", "commentNum": 0, "wordCount": 4744, "description": "## Abstract\r\n\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u578b\u7684\u81ea\u52a8\u5316\u5de5\u5177\uff0c\u65e8\u5728\u63d0\u9ad8\u5927\u89c4\u6a21\u5730\u9707\u6570\u636e\u96c6\u7684\u5206\u6790\u6548\u7387\u4e0e\u5ba2\u89c2\u6027\u3002", "top": 0, "createdAt": 1735046562, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-24", "dateLabelColor": "#bc4c00"}, "P29": {"htmlDir": "docs/post/[Literature Reading]SplitLab.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]SplitLab", "postUrl": "post/%5BLiterature%20Reading%5DSplitLab.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/29", "commentNum": 0, "wordCount": 1937, "description": "# SplitLab: \u526a\u5207\u6ce2\u5206\u88c2\u6570\u636e\u5904\u7406\u73af\u5883\u603b\u7ed3\r\n\r\n## \u80cc\u666f\u4e0e\u76ee\u6807\r\n- \u526a\u5207\u6ce2\u5206\u88c2\uff08Shear Wave Splitting, SWS\uff09\u662f\u7814\u7a76\u5730\u58f3\u548c\u5730\u5e54\u5404\u5411\u5f02\u6027\u7684\u91cd\u8981\u65b9\u6cd5\u3002", "top": 0, "createdAt": 1735220279, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-26", "dateLabelColor": "#bc4c00"}, "P30": {"htmlDir": "docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html", "labels": ["JGR"], "postTitle": "[Literature Reading]Multichannel analysis of shear wave splitting ", "postUrl": "post/%5BLiterature%20Reading%5DMultichannel%20analysis%20of%20shear%20wave%20splitting%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/30", "commentNum": 0, "wordCount": 1707, "description": "## Abstract\r\n\u79d1\u5b66\u95ee\u9898\uff1a\r\n\u5982\u4f55\u8054\u5408\u591a\u4e2a\u9707\u76f8\u63d0\u9ad8\u6d4b\u91cf\u7ed3\u679c\u7684\u9c81\u68d2\u6027\uff1f\r\n\u5982\u4f55\u5728\u590d\u6742\u533a\u57df\u6709\u6548\u533a\u5206\u4e0d\u540c\u7684\u5404\u5411\u5f02\u6027\u7279\u5f81\uff1f\r\n\u5982\u4f55\u5904\u7406\u4f4e\u4fe1\u566a\u6bd4\u6570\u636e\u5e76\u63d0\u9ad8\u7ed3\u679c\u7684\u7a33\u5065\u6027\uff1f\r\n\r\n## Introduction\r\nThe analysis of shear wave splitting is greatly simplified if the polarization of the incoming wave is known.\r\n\r\n#### \u65b9\u6cd5\u4e00\uff1a\u53e0\u52a0\u6a2a\u5411\u5206\u91cf\u65b9\u6cd5\uff08Stacking the transverse components method\uff09\r\n- **\u6838\u5fc3\u539f\u7406**\uff1a\r\n - \u901a\u8fc7\u53e0\u52a0\u591a\u4e2a\u9707\u76f8\u8bb0\u5f55\u7684\u6a2a\u5411\u5206\u91cf\uff0c\u627e\u5230\u6700\u5927\u632f\u5e45\u7684\u5feb\u6ce2\u65b9\u5411\u548c\u5ef6\u8fdf\u65f6\u95f4\u3002", "top": 0, "createdAt": 1735543251, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-30", "dateLabelColor": "#bc4c00"}}, "singeListJson": {}, "labelColorDict": {"bug": "#d73a4a", "Code": "#0E8A16", "Computers&Geosciences": "#fbca04", "documentation": "#0075ca", "enhancement": "#a2eeef", "GJI": "#d876e3", "GRL": "#7057ff", "JGR": "#0BF53A", "other": "#bfdadc", "point": "#5319E7", "SRL": "#7A6EA5", "wontfix": "#ffffff", "\u5730\u7403\u7269\u7406\u5b66\u62a5": "#008672", "\u5730\u9707\u5b66\u62a5": "#e4e669"}, "displayTitle": "\u829c\u5c3d", "faviconUrl": 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"https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "GMEEK_VERSION": "last", "postListJson": {"P1": {"htmlDir": "docs/post/From Here On.html", "labels": ["documentation"], "postTitle": "From Here On", "postUrl": "post/From%20Here%20On.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/1", "commentNum": 1, "wordCount": 18, "description": "My first blog.\r\n\r\n\u3002", "top": 0, "createdAt": 1730102454, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-10-28", "dateLabelColor": "#bc4c00"}, "P2": {"htmlDir": "docs/post/[Literature Reading] Classification of Teleseismic Shear Wave Splitting Measurements- A Convolutional Neural Network Approach.html", "labels": ["documentation", "GRL"], "postTitle": "[Literature Reading] Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach", "postUrl": "post/%5BLiterature%20Reading%5D%20Classification%20of%20Teleseismic%20Shear%20Wave%20Splitting%20Measurements-%20A%20Convolutional%20Neural%20Network%20Approach.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/2", "commentNum": 1, "wordCount": 6222, "description": "# Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach\r\n# Abstract\r\n \u526a\u5207\u6ce2\u5206\u88c2\r\n \u95ee\u9898\uff1a\u9700\u8981\u53ef\u9760\u7684\u5206\u88c2\u6d4b\u91cf\u6570\u636e\uff0c\u76ee\u89c6\u6548\u7387\u4f4e\r\n \u65b9\u6cd5\uff1aCNN \u4eba\u5de5\u8bc6\u522b\u6570\u636e\u8bad\u7ec3\uff0c\u5408\u6210\u6570\u636e\u6d4b\u8bd5\uff0c\u5b9e\u9645\u6570\u636e\u5bf9\u6bd4\r\n \u5e94\u7528\uff1aBoardband seismic data recorded in south central Alaska\r\n\r\n# 1.Introduction\r\n XKS\u6ce2\u5728\u5404\u5411\u5f02\u6027\u4ecb\u8d28\u4e2d\u4f1a\u5206\u88c2\u6210\u4e24\u4e2a\u6b63\u4ea4\u6781\u5316\u7684\u5feb\u6ce2\u548c\u6162\u6ce2\u3002", "top": 0, "createdAt": 1730108420, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-10-28", "dateLabelColor": "#bc4c00"}, "P3": {"htmlDir": "docs/post/[Literaturre Reading] Making Reliable Shear-Wave Splitting Measurements.html", "labels": ["documentation"], "postTitle": "[Literaturre Reading] Making Reliable Shear-Wave Splitting Measurements", "postUrl": "post/%5BLiteraturre%20Reading%5D%20Making%20Reliable%20Shear-Wave%20Splitting%20Measurements.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/3", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1730711179, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-04", "dateLabelColor": "#bc4c00"}, "P4": {"htmlDir": "docs/post/CNN-SWS.html", "labels": ["Code"], "postTitle": "CNN-SWS", "postUrl": "post/CNN-SWS.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/4", "commentNum": 0, "wordCount": 10762, "description": "## \u8bba\u6587\u201cClassification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach\u201d\u4ee3\u7801\u90e8\u5206\r\n\r\n### \u6587\u4ef6\u7ed3\u6784\r\n```\r\nCNN-SWS-main/\r\n\u251c\u2500\u2500 1_data/ # \u6570\u636e\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 Out_Bin/ # \u5b58\u50a8 XKS.out \u6587\u4ef6\uff0c.out\u6587\u4ef6\u5305\u542b\u4e09\u5217\uff0c\u53f0\u7ad9\u548c\u7f51\u7edc\u540d\u79f0\uff08stname_NW\uff09\u3001\u4e8b\u4ef6\u540d\u79f0\uff08EQ123456789\uff09\u3001\u6d4b\u91cf\u8d28\u91cf\uff08A \u548c B \u8868\u793a\u53ef\u63a5\u53d7\uff0c\u5176\u4f59\u8868\u793a\u4e0d\u53ef\u63a5\u53d7\uff09 \r\n\u2502 \u2502 \u251c\u2500\u2500 PKS.out \r\n\u2502 \u2502 \u251c\u2500\u2500 SKK.out\r\n\u2502 \u2502 \u2514\u2500\u2500 SKS.out\r\n\u2502 \u2514\u2500\u2500 PKSOut/ # \u5b58\u50a8\u4e0d\u540c\u53f0\u7ad9\u548c\u4e8b\u4ef6\u7684\u6ce2\u5f62\u6570\u636e\r\n\u2502 \u251c\u2500\u2500 109Cxx_TA/ # \u53f0\u7ad9\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u251c\u2500\u2500 EQ140250514/ # \u4e8b\u4ef6\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.rl # \u6821\u6b63\u5f84\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.ro # \u539f\u59cb\u5f84\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u251c\u2500\u2500 109Cxx_TA.tl # \u6821\u6b63\u6a2a\u5411\u5206\u91cf\r\n\u2502 \u2502 \u2502 \u2514\u2500\u2500 109Cxx_TA.to # \u539f\u59cb\u6a2a\u5411\u5206\u91cf\r\n\u2502 \u2502 \u251c\u2500\u2500 EQ********/ #\u5176\u4ed6\u4e8b\u4ef6\r\n\u2502 \u251c\u2500\u2500 ************** # \u5176\u4ed6\u53f0\u7ad9\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 PKS.list # Out_Bin/PKS.out\r\n\u2502 \u251c\u2500\u2500 SKK.list # Out_Bin/SKK.out\r\n\u2502 \u2514\u2500\u2500 SKS.list # Out_Bin/SKS.out\r\n\u2502\r\n\u251c\u2500\u2500 load/ # \u6570\u636e\u52a0\u8f7d\u548c\u9884\u6d4b\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 2_load/ # \u52a0\u8f7d\u8f93\u51fa\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u2514\u2500\u2500 Outp/ # \u5b58\u653e\u52a0\u8f7d\u9884\u6d4b\u7ed3\u679c\r\n\u2502 \u251c\u2500\u2500 load.py # \u6570\u636e\u52a0\u8f7d\u548c\u9884\u6d4b\u811a\u672c\r\n\u2502 \u2514\u2500\u2500 parameter.list # \u52a0\u8f7d\u8fc7\u7a0b\u53c2\u6570\r\n\u2502\r\n\u251c\u2500\u2500 model/ # \u6a21\u578b\u6587\u4ef6\u5939\r\n\u2502 \u2514\u2500\u2500 CNN_XKS.h5 # \u5df2\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6743\u91cd\r\n\u2502\r\n\u251c\u2500\u2500 train/ # \u6a21\u578b\u8bad\u7ec3\u6587\u4ef6\u5939\r\n\u2502 \u251c\u2500\u2500 2_train/ # \u8bad\u7ec3\u8f93\u51fa\u6587\u4ef6\u5939\r\n\u2502 \u2502 \u251c\u2500\u2500 CNN_XKS.h5 # \u8bad\u7ec3\u540e\u7684\u6a21\u578b\u6743\u91cd\r\n\u2502 \u2502 \u251c\u2500\u2500 parameters.list # \u8bad\u7ec3\u8fc7\u7a0b\u53c2\u6570(\u5355\u72ec\u5199\u51fa\u6765\uff0c\u65b9\u4fbf\u6539\u52a8)\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.acc # \u8bad\u7ec3\u7cbe\u5ea6\u8bb0\u5f55\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.loss # \u8bad\u7ec3\u635f\u5931\u8bb0\u5f55\r\n\u2502 \u2502 \u251c\u2500\u2500 train_64.val_acc # \u9a8c\u8bc1\u7cbe\u5ea6\u8bb0\u5f55\r\n\u2502 \u2502 \u2514\u2500\u2500 train_64.val_loss # \u9a8c\u8bc1\u635f\u5931\u8bb0\u5f55\r\n\u2502 \u2514\u2500\u2500 train.py # \u6a21\u578b\u8bad\u7ec3\u811a\u672c\r\n\u2502\r\n\u251c\u2500\u2500 test/ # \u6d4b\u8bd5\u6587\u4ef6\u5939\r\n\u2502 \u2514\u2500\u2500 test.py # \u6a21\u578b\u53ef\u89c6\u5316\u548c\u6d4b\u8bd5\u811a\u672c\r\n\u2502\r\n\u251c\u2500\u2500 Do_load.cmd # \u52a0\u8f7d\u547d\u4ee4\u811a\u672c\r\n\u251c\u2500\u2500 Do_train.cmd # \u8bad\u7ec3\u547d\u4ee4\u811a\u672c\r\n\u2514\u2500\u2500 README.txt # \u9879\u76ee\u8bf4\u660e\u6587\u4ef6\r\n\r\n\r\n```\r\n\r\n### load.py\r\n ```\r\nimport os\r\nimport numpy as np\r\nfrom obspy import read\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Conv1D, ZeroPadding1D, Flatten, Dense\r\n\r\nX = [] # \u6570\u636e\u5217\u8868\r\nY = [] # \u6807\u7b7e\u5217\u8868\r\nX_nst, Y_nev = [], [] # \u53f0\u7ad9\u540d\u548c\u4e8b\u4ef6\u540d\r\ninput_length = 1000\r\n\r\n# \u6570\u636e\u548c\u6a21\u578b\u52a0\u8f7d\r\nnrt = os.path.normpath('C:/Users/~/S-wave spliting/CNN-SWS-main/1_data')\r\nnmodel = os.path.normpath('C:/Users/~/S-wave spliting/CNN-SWS-main/model/CNN_XKS.h5')\r\n# \u8def\u5f84\u68c0\u67e5\r\nif not os.path.exists(nrt):\r\n raise FileNotFoundError(f'The data root path {nrt} does not exist.')\r\nif not os.path.exists(nmodel):\r\n raise FileNotFoundError(f'The model path {nmodel} does not exist.')\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6SAC\u6570\u636e\r\nXKS = ['PKS', 'SKS', 'SKK']\r\n\r\nfor k in range(3):\r\n XKS_rout = os.path.join(nrt, f'{XKS[k]}.list') # C:/Users/~/main/1_data/Out_Bin/*.out\r\n print(f'Reading {XKS_rout}')\r\n\r\n if not os.path.exists(XKS_rout):\r\n raise FileNotFoundError(f'The file {XKS_rout} does not exist.')\r\n\r\n# \u9010\u884c\u8bfb\u53d6\u6570\u636e\r\nwith open(XKS_rout, 'r') as Pl:\r\n for line_Pl in Pl:\r\n vals = line_Pl.split()\r\n P_rout = os.path.join(nrt, vals[0]) # \u6570\u636e\u6839\u76ee\u5f55nrt \uff0b .out\u6587\u4ef6\u7b2c\u4e00\u5217\r\n print(f'Doing: {XKS[k]} {vals[0]}')\r\n\r\n if not os.path.exists(P_rout):\r\n raise FileNotFoundError(f'The file {P_rout} does not exist.')\r\n```\r\n```\r\nPKS, y = [], [] # PKS\u7528\u4e8e\u50a8\u5b58\u6ce2\u5f62\u6570\u636e\uff0cy\u50a8\u5b58\u5206\u7c7b\u6807\u7b7e\r\nwith open(P_rout, 'r') as P:\r\n for line in P:\r\n vals = line.split()\r\n nst = vals[0] # station name\r\n nev = vals[1] # event name\r\n\r\n # \u5904\u7406\u5206\u7c7b\u6807\u7b7e\r\n if vals[2] in ['A', 'B']:\r\n y.append(1)\r\n y.append(0)\r\n else:\r\n y.append(0)\r\n y.append(1)\r\n\r\n```\r\n```\r\n\r\nncom = ['.ro', '.to', '.rl', '.tl'] # 4\u5206\u91cf\u5217\u8868 \r\ncomponents = []\r\nfor i in range(4):\r\n ro_rout = os.path.join(nrt, f'{XKS[k]}Out', nst, nev, f'{nst}{ncom[i]}')\r\n print(f'Reading file: {ro_rout}')\r\n\r\n if os.path.exists(ro_rout):\r\n st = read(ro_rout)\r\n components.append(st[0].data[:input_length]) # \u622a\u53d6\u524dinput_length\u4e2a\u6570\u636e\r\n else:\r\n raise FileNotFoundError(f'The file {ro_rout} does not exist.')\r\n\r\nfor i in range(input_length):\r\n PKS.append(np.array([comp[i] for comp in components]))\r\n\r\nX.append(np.array(PKS)) # \u5c064\u4e2a\u5206\u91cf\u7ec4\u6210\u7684PKS\u4fdd\u5b58\u5230\u5217\u8868X\u4e2d\r\nY.append(np.array(y)) # \u5206\u7c7b\u6807\u7b7e\u4fdd\u5b58\u5230Y\u4e2d\r\nX_nst.append(f'{nst}_{XKS[k]}_') # \u53f0\u7ad9\u4fe1\u606f\r\nY_nev.append(nev) # \u4e8b\u4ef6\u4fe1\u606f\r\n\r\n```\r\n\r\n```\r\n# \u5b9a\u4e49\u6a21\u578b\r\ninput_shape = (input_length, 4)\r\nmodel = Sequential()\r\n # \u6dfb\u52a0\u5377\u79ef\u5c42\r\nmodel.add(Conv1D(kernel_size=3, filters=32, input_shape=input_shape, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(ZeroPadding1D(padding=1))\r\nmodel.add(Conv1D(kernel_size=3, filters=32, strides=2, activation='relu'))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(units=2, activation='softmax'))\r\n\r\n# \u52a0\u8f7d\u6a21\u578b\u6743\u91cd\u5e76\u8fdb\u884c\u9884\u6d4b\r\nmodel.load_weights(nmodel)\r\nresult = model.predict(np.array(X))\r\n\r\n# \u4fdd\u5b58\u9884\u6d4b\u7ed3\u679c\r\noutput_dir = os.path.join('C:/Users/~/S-wave spliting/CNN-SWS-main/load/2_load/Outp')\r\nos.makedirs(output_dir, exist_ok=True)\r\nfor i in range(len(result)):\r\n nst = X_nst[i] \r\n nev = Y_nev[i] \r\n res_name = os.path.join(output_dir, f'{nst}_{nev}.res') \r\n y_name = os.path.join(output_dir, f'{nst}_{nev}.y')\r\n\r\n np.savetxt(res_name, result[i])\r\n np.savetxt(y_name, Y[i])\r\n\r\nprint('finish')\r\n```\r\n\r\n\r\n\r\n### train.py\r\n\r\n```\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport obspy\r\nimport csv\r\nfrom obspy import read\r\nfrom obspy.taup import TauPyModel\r\nimport os\r\nfrom pathlib import Path\r\nimport random\r\nimport keras\r\nfrom keras import regularizers\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, Conv1D, MaxPooling1D, UpSampling1D, ZeroPadding1D\r\n\r\n# \u521d\u59cb\u5316\r\nX_good, Y_good = [], []\r\nX_bad, Y_bad = [], []\r\nX, Y = [], []\r\nX_rand, Y_rand = [], []\r\nnst_good, nev_good = [], []\r\nnst_bad, nev_bad = [], []\r\nX_nst, Y_nev = [], []\r\nnst_rand, nev_rand = [], []\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6\u53c2\u6570\u6587\u4ef6\r\nn=0\r\nwith open('parameters.list') as p:\r\n for line in p:\r\n n += 1\r\n vals = line.split()\r\n if n == 1: nrt = str(vals[0])\r\n if n == 2: batch_size = int(vals[0])\r\n if n == 3: epochs = int(vals[0])\r\n if n == 4: byn = int(vals[0])\r\n if n == 5:\r\n ac = int(vals[0])\r\n uc = int(vals[1])\r\n\r\n```\r\n\r\n```\r\n# \u8bfb\u53d6SAC\u6570\u636e\r\ninput_length = 1000\r\nXKS = ['PKS', 'SKS', 'SKK']\r\n\r\nfor k in range(3):\r\n XKS_rout = nrt + str(XKS[k]) + '.list'\r\n with open(XKS_rout) as Pl:\r\n for line_Pl in Pl:\r\n vals = line_Pl.split()\r\n P_rout = nrt + str(vals[0])\r\n with open(P_rout) as P:\r\n for line in P:\r\n PKS, y = [], []\r\n vals = line.split()\r\n nst = vals[0] # station name\r\n nev = vals[1] # event name\r\n y = [1, 0] if vals[2] in ['A', 'B'] else [0, 1]\r\n \r\n ncom = ['.ro', '.to', '.rl', '.tl']\r\n for i in range(4):\r\n ro_rout = f'{nrt}{XKS[k]}Out/{nst}/{nev}/{nst}{ncom[i]}'\r\n st = read(ro_rout)\r\n if i == 0: ro = st[0].data\r\n if i == 1: to = st[0].data\r\n if i == 2: rl = st[0].data\r\n if i == 3: tl = st[0].data\r\n \r\n for i in range(input_length):\r\n PKS.append(np.array([ro[i], to[i], rl[i], tl[i]]))\r\n if y[0] == 1:\r\n X_good.append(PKS)\r\n Y_good.append(y)\r\n nst_good.append(f'{nst}_{XKS[k]}_')\r\n nev_good.append(nev)\r\n else:\r\n X_bad.append(PKS)\r\n Y_bad.append(y)\r\n nst_bad.append(f'{nst}_{XKS[k]}_')\r\n nev_bad.append(nev)\r\n```\r\n```\r\n# \u6570\u636e\u589e\u5f3a\uff08\u901a\u8fc7\u500d\u589e\u6765\u5e73\u8861\u6570\u636e\u96c6\u4e2d\u7684\u7c7b\u522b\u6570\u91cf\uff09\r\nnpts = int(len(X_bad) / len(X_good))\r\nclass_weight = {0: ac, 1: uc}\r\nif byn == 0: npts = 1\r\n\r\nfor i in range(npts):\r\n for ii in range(len(X_good)):\r\n X.append(X_good[ii])\r\n Y.append(Y_good[ii])\r\n X_nst.append(nst_good[ii])\r\n Y_nev.append(nev_good[ii])\r\n\r\nfor i in range(len(X_bad)):\r\n X.append(X_bad[i])\r\n Y.append(Y_bad[i])\r\n X_nst.append(nst_bad[i])\r\n Y_nev.append(nev_bad[i])\r\n\r\n# \u6570\u636e\u968f\u673a\u5316\u4e0e\u5212\u5206\r\nrann0 = random.sample(range(len(X)), len(X))\r\nX_rand = [X[i] for i in rann0]\r\nY_rand = [Y[i] for i in rann0]\r\nx_train, y_train = np.array(X_rand[:int(len(X) * 0.8)]), np.array(Y_rand[:int(len(Y) * 0.8)])\r\nx_test, y_test = np.array(X_rand[int(len(X) * 0.8):]), np.array(Y_rand[int(len(Y) * 0.8):])\r\n\r\n```\r\n```\r\nmodel = Sequential()\r\nmodel.add(Conv1D(32, kernel_size=3, strides=2, activation='relu', input_shape=(input_length, 4)))\r\nmodel.add(ZeroPadding1D(1))\r\n# \u6dfb\u52a0\u591a\u4e2a\u5377\u79ef\u5c42\uff0c\u6700\u7ec8\u5c55\u5e73\u6210\u5411\u91cf\u5e76\u8fde\u63a5\u5230 softmax \u8f93\u51fa\u5c42\r\nmodel.add(Flatten())\r\nmodel.add(Dense(2, activation='softmax'))\r\n\r\nmodel.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.001), metrics=['accuracy'])\r\nH = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, class_weight=class_weight, validation_data=(x_test, y_test))\r\n\r\n# \u53ef\u89c6\u5316\u8bad\u7ec3\u548c\u9a8c\u8bc1\u7cbe\u5ea6\r\nfig, ax = plt.subplots()\r\nplt.plot(H.history['acc'], label='train_acc')\r\nplt.plot(H.history['val_acc'], label='val_acc')\r\nplt.legend()\r\nplt.show()\r\n\r\nmodel.save_weights('CNN_XKS.h5')\r\nprint('Finish')\r\n\r\n```\r\n\r\n\u3002", "top": 0, "createdAt": 1730863802, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-06", "dateLabelColor": "#bc4c00"}, "P5": {"htmlDir": "docs/post/wei-shen-me-python-lei-zhong-yao-shi-yong-__init__()-te-shu-fang-fa.html", "labels": ["point"], "postTitle": "\u4e3a\u4ec0\u4e48python\u7c7b\u4e2d\u8981\u4f7f\u7528__init__()\u7279\u6b8a\u65b9\u6cd5", "postUrl": "post/wei-shen-me-python-lei-zhong-yao-shi-yong-__init__%28%29-te-shu-fang-fa.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/5", "commentNum": 0, "wordCount": 1694, "description": "\u4eca\u5929\u770b\u5230\u5982\u4e0b\u4ee3\u7801\uff08\u4e00\u4e2a\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\u7c7b\uff09\uff0c\u5f15\u53d1\u4e86\u7b14\u8005\u56f0\u6270\u5df2\u4e45\u7684\u95ee\u9898\uff1a__init__()\u7279\u6b8a\u65b9\u6cd5\u5230\u5e95\u6709\u4ec0\u4e48\u7528\uff0c\u4e3a\u4ec0\u4e48python\u7c7b\u4e2d\u8981\u4f7f\u7528__init__()\u7279\u6b8a\u65b9\u6cd5\uff1f\r\n```\r\nclass LRScheduler():\r\n\t'''\r\n\tLearning rate scheduler. If the validation loss does not decrease for the\r\n\tgiven number of `patience` epochs, then the learning rate will decrease by\r\n\tby given `factor`.\r\n\t'''\r\n\tdef __init__(self, optimizer, patience=7, min_lr=1e-6, factor=0.5):\r\n\t\t'''\r\n\t\tnew_lr = old_lr * factor\r\n\t\t:param optimizer: the optimizer we are using\r\n\t\t:param patience: how many epochs to wait before updating the lr\r\n\t\t:param min_lr: least lr value to reduce to while updating\r\n\t\t:param factor: factor by which the lr should be updated\r\n\t\t'''\r\n\t\tself.optimizer = optimizer\r\n\t\tself.patience = patience\r\n\t\tself.min_lr = min_lr\r\n\t\tself.factor = factor\r\n\t\tself.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\r\n\t\t\t\tself.optimizer,\r\n\t\t\t\tmode='min',\r\n\t\t\t\tpatience=self.patience,\r\n\t\t\t\tfactor=self.factor,\r\n\t\t\t\tmin_lr=self.min_lr,\r\n\t\t\t\tverbose=True\r\n\t\t\t)\r\n\tdef __call__(self, val_loss):\r\n\t\tself.lr_scheduler.step(val_loss)\r\n```\r\n\r\n__init__\u662f\u4e00\u4e2a\u7279\u6b8a\u65b9\u6cd5\uff0c\u89e3\u91ca\u4e3a\u7c7b\u7684\u521d\u59cb\u5316\u65b9\u6cd5\u6216\u6784\u9020\u5668\uff0c\u529f\u80fd\u4e5f\u5c31\u4e0d\u8a00\u800c\u55bb\u4e86\uff0c\u5f53\u521b\u5efa\u4e00\u4e2a\u7c7b\u7684\u5b9e\u4f8b\u65f6\uff0cpython\u4f1a\u81ea\u52a8\u8c03\u7528\u8fd9\u4e2a\u65b9\u6cd5\u3002", "top": 0, "createdAt": 1730981462, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-07", "dateLabelColor": "#bc4c00"}, "P6": {"htmlDir": "docs/post/[Literature Reading]Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution.html", "labels": ["documentation"], "postTitle": "[Literature Reading]Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution", "postUrl": "post/%5BLiterature%20Reading%5DFeasibility%20of%20Deep%20Learning%20in%20Shear%20Wave%20Splitting%20analysis%20using%20Synthetic-Data%20Training%20and%20Waveform%20Deconvolution.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/6", "commentNum": 0, "wordCount": 7359, "description": "## Abstract\r\n\r\n\u80cc\u666f\u4e0e\u4f20\u7edf\u65b9\u6cd5\uff1a\u4f20\u7edf\u65b9\u6cd5\u901a\u8fc7\u9006\u8f6c\u5206\u88c2\u8fc7\u7a0b\uff0c\u901a\u8fc7\u9891\u57df\u548c\u65f6\u57df\u64cd\u4f5c\uff0c\u6700\u5c0f\u5316\u6ce2\u5f62\u5207\u5411\u80fd\u91cf\uff0c\u5f97\u5230\u5206\u88c2\u53c2\u6570\u3002", "top": 0, "createdAt": 1731552896, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-14", "dateLabelColor": "#bc4c00"}, "P7": {"htmlDir": "docs/post/[Literature Reading]SWAS- A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes.html", "labels": ["documentation"], "postTitle": "[Literature Reading]SWAS: A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes", "postUrl": "post/%5BLiterature%20Reading%5DSWAS-%20A%20shear-wave%20analysis%20system%20for%20semi-automatic%20measurement%20of%20shear-wave%20splitting%20above%20small%20earthquakes.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/7", "commentNum": 1, "wordCount": 3381, "description": "# SWAS: A shear-wave analysis system for semi-automatic measurement of shear-wave splitting above small earthquakes\r\n## Abstract\r\n \u95ee\u9898\uff1a\u5c0f\u9707\u526a\u5207\u6ce2\u5230\u8fbe\u590d\u6742\uff0c\u526a\u5207\u6ce2\u6d4b\u91cf\u56f0\u96be\r\n \u65b9\u6cd5\uff1a\u5f00\u53d1SAWS\u4e13\u5bb6\u7cfb\u7edf\uff0c\u81ea\u52a8\u4f30\u8ba1\u5feb\u6ce2\u6781\u5316\u65b9\u5411\u548c\u526a\u5207\u6ce2\u5230\u65f6\uff0c\u4eba\u5de5\u8f85\u52a9\u8c03\u6574\uff08\u5728\u539f\u59cb\u5730\u9707\u56fe\u3001\u65cb\u8f6c\u5730\u9707\u56fe\u548c\u6781\u5316\u56fe\u4e4b\u95f4\u8fdb\u884c\u8c03\u6574\uff09\r\n \u6570\u636e\uff1a\u51b0\u5c9bSIL\u5730\u9707\u7f51\u7edc\u6570\u636e\r\n\r\n ```\r\n \u91cc\u6c0f\u9707\u7ea7\uff1a\u57fa\u4e8e\u5730\u9707\u6ce2\u632f\u5e45\uff0c\u5728\u8fd9\u4e2a\u6807\u5ea6\u4e2d\uff0c\u4e3a\u4e86\u4f7f\u7ed3\u679c\u4e0d\u4e3a\u8d1f\u6570\uff0c\u91cc\u514b\u7279\u5b9a\u4e49\u5728\u8ddd\u79bb\u9707\u4e2d100\u5343\u7c73\u5904\u4e4b\u89c2\u6d4b\u70b9\u5730 \r\n \u9707\u4eea\u8bb0\u5f55\u5230\u7684\u6700\u5927\u6c34\u5e73\u4f4d\u79fb\u4e3a1\u5fae\u7c73\uff08\u8fd9\u4e5f\u662f\u4f0d\u5fb7-\u5b89\u5fb7\u68ee\u626d\u529b\u5f0f\u5730\u9707\u4eea\u7684\u6700\u5927\u7cbe\u5ea6\uff09\u7684\u5730\u9707\u4f5c\u4e3a0\u7ea7\u5730\u9707\uff0c\u5f53\u5730\u9707 \r\n \u4eea\u8bb0\u5f55\u5230\u7684\u6700\u5927\u6c34\u5e73\u4f4d\u79fb\u5c0f\u4e8e1\u5fae\u7c73\uff0c\u9707\u7ea7\u4fbf\u4e3a\u8d1f\u3002", "top": 0, "createdAt": 1731647019, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-15", "dateLabelColor": "#bc4c00"}, "P8": {"htmlDir": "docs/post/[Literature Reading]ES for measuring SWS.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]ES for measuring SWS", "postUrl": "post/%5BLiterature%20Reading%5DES%20for%20measuring%20SWS.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/8", "commentNum": 2, "wordCount": 5321, "description": "## Abstract\r\n \u4e13\u5bb6\u7cfb\u7edf\u548c\u6a21\u5757\u5316\u8bbe\u8ba1\r\n\r\n## 1.Introduction\r\n ```\r\n\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff08Artificial Neural Networks, 1995\uff09\uff1a\r\n\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u8bc6\u522b\u526a\u5207\u6ce2\u5206\u88c2\uff0c\u80fd\u591f\u5904\u7406\u590d\u6742\u7684\u6ce2\u5f62\u3002", "top": 0, "createdAt": 1731895696, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-18", "dateLabelColor": "#bc4c00"}, "P9": {"htmlDir": "docs/post/[Literature Reading] -yi-zhong-shi-yong-yu-di-fang-zhen-shi-jian-de-S-bo-dao-shi-zi-dong-shi-qu-fang-fa.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading] \u4e00\u79cd\u9002\u7528\u4e8e\u5730\u65b9\u9707\u4e8b\u4ef6\u7684S\u6ce2\u5230\u65f6\u81ea\u52a8\u62fe\u53d6\u65b9\u6cd5", "postUrl": "post/%5BLiterature%20Reading%5D%20-yi-zhong-shi-yong-yu-di-fang-zhen-shi-jian-de-S-bo-dao-shi-zi-dong-shi-qu-fang-fa.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/9", "commentNum": 0, "wordCount": 3181, "description": "## \u5f15\u8a00\r\n\r\n S\u6ce2\u62fe\u53d6\u56f0\u96be:\u53d7\u5230P\u6ce2\u5c3e\u6ce2\u53ca\u5176\u4ed6\u8f6c\u6362\u6ce2\u9707\u76f8\u5f71\u54cd\uff0c\u56e0\u6b64S\u6ce2\u7684\u4fe1\u566a\u6bd4\u4e00\u822c\u4f4e\u4e8eP\u6ce2\uff0c\u62fe\u53d6\u7684\u51c6\u786e\u5ea6\u4e5f\u6bd4\u8f83\u4f4e\uff1b\r\n \u73b0\u6709\u624b\u52a8\u62fe\u53d6\u65b9\u6cd5\uff1a\u57fa\u4e8e\u6781\u5316\u7279\u5f81\uff0c\u5229\u7528\u539f\u59cb\u5730\u9707\u4e09\u5206\u91cf\u8bb0\u5f55\uff0c\u6839\u636eP\u6ce2\u548cS\u6ce2\u5728\u504f\u632f\u65b9\u5411\u4e0a\u7684\u4e0d\u540c\u7279\u5f81\uff08\u5982\u8d28\u70b9\u8fd0\u52a8\u7684\u504f\u632f\u5ea6\u3001\u7ebf\u6027\u5ea6\u7b49\uff09\uff0c\u627e\u5230\u9707\u76f8\u7a81\u53d8\u70b9\uff0c\u8fdb\u800c\u786e\u5b9aS\u6ce2\u5230\u65f6\u3002", "top": 0, "createdAt": 1732174245, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-21", "dateLabelColor": "#bc4c00"}, "P10": {"htmlDir": "docs/post/zhen-xiang-shi-qu.html", "labels": ["documentation", "enhancement"], "postTitle": "\u9707\u76f8\u62fe\u53d6", "postUrl": "post/zhen-xiang-shi-qu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/10", "commentNum": 0, "wordCount": 2902, "description": "[PhaseNet <\u8f6c\u8f7d>](https://blog.csdn.net/qq_40206371/article/details/129748282?utm_source=chatgpt.com)\r\n\r\n\r\n---\r\n\r\n### \u9707\u76f8\u62fe\u53d6\u4e0e\u81ea\u52a8\u5316\u6280\u672f\u7684\u7814\u7a76\u80cc\u666f\u4e0e\u53d1\u5c55\r\n\r\n#### \u603b\u7ed3\uff1a\r\n\u5730\u9707\u9707\u76f8\u4fe1\u606f\u662f\u5730\u9707\u5b66\u7814\u7a76\u4e2d\u7684\u91cd\u8981\u57fa\u7840\u6570\u636e\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5730\u9707\u5b9a\u4f4d\u3001\u9707\u6e90\u673a\u5236\u5206\u6790\u548c\u8d70\u65f6\u5c42\u6790\u6210\u50cf\u7b49\u9886\u57df\u3002", "top": 0, "createdAt": 1732175989, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-21", "dateLabelColor": "#bc4c00"}, "P13": {"htmlDir": "docs/post/[Literature Reading]-juan-ji-shen-jing-wang-luo-zai-yuan---jin-di-zhen-zhen-xiang-shi-qu-zhong-de-ying-yong-ji-mo-xing-jie-shi.html", "labels": ["documentation"], "postTitle": "[Literature Reading]\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5728\u8fdc-\u8fd1\u5730\u9707\u9707\u76f8\u62fe\u53d6\u4e2d\u7684\u5e94\u7528\u53ca\u6a21\u578b\u89e3\u91ca", "postUrl": "post/%5BLiterature%20Reading%5D-juan-ji-shen-jing-wang-luo-zai-yuan---jin-di-zhen-zhen-xiang-shi-qu-zhong-de-ying-yong-ji-mo-xing-jie-shi.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/13", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1732684092, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-27", "dateLabelColor": "#bc4c00"}, "P14": {"htmlDir": "docs/post/[Literature Reading]-jian-gu-su-du-he-jing-du-de-shen-du-shen-jing-wang-luo-zhen-xiang-shi-qu.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading]\u517c\u987e\u901f\u5ea6\u548c\u7cbe\u5ea6\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u9707\u76f8\u62fe\u53d6", "postUrl": "post/%5BLiterature%20Reading%5D-jian-gu-su-du-he-jing-du-de-shen-du-shen-jing-wang-luo-zhen-xiang-shi-qu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/14", "commentNum": 0, "wordCount": 4602, "description": "## \u6458\u8981\r\n \u6839\u636e\u5730\u9707\u6ce2\u5f62\u7684\u7279\u70b9\u8bbe\u8ba1\u4e86\u56db\u79cd\u5177\u6709\u4e0d\u540c\u590d\u6742\u5ea6\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u6539\u8fdb\u6a21\u578b\uff0c\u53ef\u4ee5\u7efc\u5408\u5177\u4f53\u7684\u7cbe\u5ea6\u548c\u901f\u5ea6\u9700\u6c42\u4ece\u4e2d\u9009\u53d6\u5408\u9002\u7684\u6a21\u578b\uff0c\u5c06\u6539\u8fdb\u6a21\u578b\u4e0e\u73b0\u6709\u56db\u79cd\u5230\u65f6\u62fe\u53d6\u7684\u6df1\u5ea6\u5b66\u6a21\u578b\u4f5c\u5bf9\u6bd4\u3002", "top": 0, "createdAt": 1732685235, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-27", "dateLabelColor": "#bc4c00"}, "P15": {"htmlDir": "docs/post/[Literature Reading]-jin-zhen-S-bo-zhen-xiang-shi-shi-zi-dong-shi-bie-fang-fa-yan-jiu.html", "labels": ["\u5730\u9707\u5b66\u62a5"], "postTitle": "[Literature Reading]\u8fd1\u9707S\u6ce2\u9707\u76f8\u5b9e\u65f6\u81ea\u52a8\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76", "postUrl": "post/%5BLiterature%20Reading%5D-jin-zhen-S-bo-zhen-xiang-shi-shi-zi-dong-shi-bie-fang-fa-yan-jiu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/15", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1732772635, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-28", "dateLabelColor": "#bc4c00"}, "P16": {"htmlDir": "docs/post/[Literrture Reading]DeepPhasePick- a method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks.html", "labels": ["GJI"], "postTitle": "[Literrture Reading]DeepPhasePick: a method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks", "postUrl": "post/%5BLiterrture%20Reading%5DDeepPhasePick-%20a%20method%20for%20detecting%20and%20picking%20seismic%20phases%20from%20local%20earthquakes%20based%20on%20highly%20optimized%20convolutional%20and%20recurrent%20deep%20neural%20networks.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/16", "commentNum": 0, "wordCount": 798, "description": "## Summary\r\n\u76f8\u4f4d\u68c0\u6d4b\u3001\u8bc6\u522b\u548c\u521d\u81f3\u65f6\u95f4\u662f\u5206\u6790\u5730\u9707\u6570\u636e\u7684\u57fa\u7840\u4e14\u91cd\u8981\u7684\u5e38\u89c4\u5de5\u4f5c\u3002", "top": 0, "createdAt": 1732776828, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-11-28", "dateLabelColor": "#bc4c00"}, "P17": {"htmlDir": "docs/post/[Literature Reading]Pytheas- An open-source software solution for local shear-wave splitting studies .html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]Pytheas: An open-source software solution for local shear-wave splitting studies ", "postUrl": "post/%5BLiterature%20Reading%5DPytheas-%20An%20open-source%20software%20solution%20for%20local%20shear-wave%20splitting%20studies%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/17", "commentNum": 0, "wordCount": 6583, "description": "## Abstract\r\n\u63d0\u4f9b\u4e86\u5305\u62ec\u89c6\u89c9\u68c0\u67e5\u3001\u65cb\u8f6c\u76f8\u5173\u6cd5\u3001\u7279\u5f81\u503c\u6cd5\u548c\u6700\u5c0f\u80fd\u91cf\u6cd5\u5728\u5185\u7684\u591a\u79cd\u5206\u6790\u5de5\u5177,\u5e76\u901a\u8fc7\u805a\u7c7b\u5206\u6790\u5b9e\u73b0\u81ea\u52a8\u5316\u5904\u7406\u3002", "top": 0, "createdAt": 1733208364, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-03", "dateLabelColor": "#bc4c00"}, "P18": {"htmlDir": "docs/post/[Review]-li-yong-heng-bo-fen-lie-fen-xi-fang-fa-yan-jiu-di-ke-ge-xiang-yi-xing-zong-shu.html", "labels": ["other"], "postTitle": "[Review]\u5229\u7528\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u65b9\u6cd5\u7814\u7a76\u5730\u58f3\u5404\u5411\u5f02\u6027\u7efc\u8ff0", "postUrl": "post/%5BReview%5D-li-yong-heng-bo-fen-lie-fen-xi-fang-fa-yan-jiu-di-ke-ge-xiang-yi-xing-zong-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/18", "commentNum": 0, "wordCount": 3893, "description": " ## \r\n\u5404\u5411\u5f02\u6027\u5b9a\u4e49---\u8d77\u6e90---\u5730\u9707\u5404\u5411\u5f02\u6027\uff0c\u610f\u4e49---\u76f4\u89c2\u8868\u73b0(S\u6ce2\u5206\u88c2)---\u5206\u88c2\u53c2\u6570---\u5f71\u54cd\u56e0\u7d20(\u4e3b\u8981\u56e0\u7d20\uff0c\u5176\u4ed6\u56e0\u7d20)\r\n```\r\n\u4e3b\u8981\u56e0\u7d20\uff1a\u88c2\u7f1d\u6216\u4e3b\u538b\u5e94\u529b\u65b9\u5411\u3001\u6df1\u90e8\u7269\u8d28\u6d41\u52a8\u65b9\u5411\u3001\u77ff\u7269\u6676\u683c\u4f18\u52bf\u6392\u5217\u65b9\u5411\uff08LPO\uff09\r\n\u5176\u4ed6\u56e0\u7d20\uff1a\u5feb\u6162\u6ce2\u7684\u6ce2\u5f62\u5dee\u5f02\uff1a \u5feb\u6a2a\u6ce2\u548c\u6162\u6a2a\u6ce2\u6ce2\u5f62\u4e0d\u540c\uff0c\u6162\u6a2a\u6ce2\u7684\u8870\u51cf\u66f4\u660e\u663e\uff0c\u521d\u52a8\u8f83\u5f31\u3002", "top": 0, "createdAt": 1733412990, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-05", "dateLabelColor": "#bc4c00"}, "P19": {"htmlDir": "docs/post/[Review]A review of techniques for measuring shear-wave splitting above small earthquakes.html", "labels": ["other"], "postTitle": "[Review]A review of techniques for measuring shear-wave splitting above small earthquakes", "postUrl": "post/%5BReview%5DA%20review%20of%20techniques%20for%20measuring%20shear-wave%20splitting%20above%20small%20earthquakes.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/19", "commentNum": 0, "wordCount": 4740, "description": "## Abstrct\r\n\u4ece\u4f20\u7edf\u7684\u624b\u52a8\u89c6\u89c9\u6280\u672f\u5230\u81ea\u52a8\u5316\u6280\u672f\u7684\u53d1\u5c55\uff0c\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\uff0c\u5e76\u63d0\u51fa\u4e86\u4e00\u79cd\u7ed3\u5408\u89c6\u89c9\u548c\u81ea\u52a8\u5316\u6280\u672f\u7684\u534a\u81ea\u52a8\u5316\u6d4b\u91cf\u65b9\u6cd5\r\n\r\n## 1. Introduction\r\n**\u526a\u5207\u6ce2\u5206\u88c2\u7684\u6210\u56e0\u4e0e\u7279\u5f81**\r\n \u5404\u5411\u5f02\u6027\u4ecb\u8d28\u4e2d\uff08\u5982\u5730\u4e0b\u7684\u5fae\u88c2\u7f1d\uff09\uff0c\u5176\u4e2d\u526a\u5207\u6ce2\u5206\u88c2\u6210\u4e24\u76f8\uff0c\u5206\u522b\u4e3a\u5feb\u6ce2\u548c\u6162\u6ce2\uff0c\u5e76\u4e14\u5b83\u4eec\u4ee5\u4e0d\u540c\u7684\u901f\u5ea6\u4f20\u64ad\u3002", "top": 0, "createdAt": 1733710968, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-09", "dateLabelColor": "#bc4c00"}, "P20": {"htmlDir": "docs/post/[Review]-li-yong-duo-zhong-heng-bo-fen-lie-fen-xi-fang-fa-ping-gu-que-ding-ge-xiang-yi-xing-can-shu.html", "labels": ["\u5730\u7403\u7269\u7406\u5b66\u62a5"], "postTitle": "[Review]\u5229\u7528\u591a\u79cd\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u65b9\u6cd5\u8bc4\u4f30\u786e\u5b9a\u5404\u5411\u5f02\u6027\u53c2\u6570", "postUrl": "post/%5BReview%5D-li-yong-duo-zhong-heng-bo-fen-lie-fen-xi-fang-fa-ping-gu-que-ding-ge-xiang-yi-xing-can-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/20", "commentNum": 0, "wordCount": 2738, "description": "## \u6458\u8981 \r\n\u80cc\u666f\uff1a\u6570\u636e\u7684\u566a\u58f0\u6c34\u5e73\u3001\u89c2\u6d4b\u65b9\u4f4d\u5206\u5e03\u4ee5\u53ca\u4ecb\u8d28\u7684\u590d\u6742\u7a0b\u5ea6\u90fd\u4f1a\u5f71\u54cd\u6a2a\u6ce2\u5206\u88c2\u5206\u6790\u7ed3\u679c\u7684\u7a33\u5b9a\u6027\u548c\u51c6\u786e\u6027\u3002", "top": 0, "createdAt": 1733723969, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-09", "dateLabelColor": "#bc4c00"}, "P21": {"htmlDir": "docs/post/[Literature Reading]Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements.html", "labels": ["SRL"], "postTitle": "[Literature Reading]Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements", "postUrl": "post/%5BLiterature%20Reading%5DUsing%20Convolutional%20Neural%20Network%20to%20Determine%20Time%20Window%20for%20Analyzing%20Local%20Shear-Wave%20Splitting%20Measurements.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/21", "commentNum": 0, "wordCount": 3951, "description": "## Abstract\r\n```\r\n\u7814\u7a76\u5229\u7528CNN\u6765\u786e\u5b9a\u65f6\u95f4\u7a97\u53e3\u7684\u7ed3\u675f\u4f4d\u7f6e(e)\uff0c\u5e76\u4e14\u8bbe\u5b9a\u65f6\u95f4\u7a97\u53e3\u4ecee\u524d0.5\u79d2\u5f00\u59cb\u3002", "top": 0, "createdAt": 1733838287, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-10", "dateLabelColor": "#bc4c00"}, "P22": {"htmlDir": "docs/post/[Literature Reading]Automatic measurement of shear wave splitting and applications to time varying anisotropy at Mount Ruapehu volcano, New Zealand.html", "labels": ["JGR"], "postTitle": "[Literature Reading]Automatic measurement of shear wave splitting and applications to time varying anisotropy at Mount Ruapehu volcano, New Zealand", "postUrl": "post/%5BLiterature%20Reading%5DAutomatic%20measurement%20of%20shear%20wave%20splitting%20and%20applications%20to%20time%20varying%20anisotropy%20at%20Mount%20Ruapehu%20volcano%2C%20New%20Zealand.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/22", "commentNum": 0, "wordCount": 9554, "description": "# MFAST\r\n\r\n## Abstract\r\n\u81ea\u52a8\u5316\u6d41\u7a0b\uff1a\u4ec5\u9700\u4eba\u5de5\u9009\u62e9S\u6ce2\u5230\u8fbe\u65f6\u95f4\uff0c\u5176\u4ed6\u6b65\u9aa4\u5b8c\u5168\u81ea\u52a8\u5316\u3002", "top": 0, "createdAt": 1733897728, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-11", "dateLabelColor": "#bc4c00"}, "P23": {"htmlDir": "docs/post/[Review]-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-di-zhen-zhen-xiang-shi-qu-fang-fa-yan-jiu.html", "labels": ["\u5730\u7403\u7269\u7406\u5b66\u62a5"], "postTitle": "[Review]\u57fa\u4e8e\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u5730\u9707\u9707\u76f8\u62fe\u53d6\u65b9\u6cd5\u7814\u7a76", "postUrl": "post/%5BReview%5D-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-di-zhen-zhen-xiang-shi-qu-fang-fa-yan-jiu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/23", "commentNum": 0, "wordCount": 5128, "description": "## \u6458\u8981\r\n1. \u7814\u7a76\u80cc\u666f\u4e0e\u95ee\u9898\r\n\u5730\u9707\u9707\u76f8\u62fe\u53d6\u662f\u5730\u9707\u6570\u636e\u81ea\u52a8\u5316\u5904\u7406\u4e2d\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u4e3b\u8981\u5305\u62ec\u4fe1\u53f7\u68c0\u6d4b\u3001\u9707\u76f8\u5230\u65f6\u4f30\u8ba1\u548c\u9707\u76f8\u8bc6\u522b\u7b49\u8fc7\u7a0b\u3002", "top": 0, "createdAt": 1734177907, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-14", "dateLabelColor": "#bc4c00"}, "P24": {"htmlDir": "docs/post/[Literature Reading]An automatized XKS-splitting procedure for large data sets- Extension package for SplitRacer and application to the USArray .html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]An automatized XKS-splitting procedure for large data sets: Extension package for SplitRacer and application to the USArray ", "postUrl": "post/%5BLiterature%20Reading%5DAn%20automatized%20XKS-splitting%20procedure%20for%20large%20data%20sets-%20Extension%20package%20for%20SplitRacer%20and%20application%20to%20the%20USArray%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/24", "commentNum": 0, "description": "", "wordCount": 0, "top": 0, "createdAt": 1734589481, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-19", "dateLabelColor": "#bc4c00"}, "P25": {"htmlDir": "docs/post/[Literature Reading]-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-jian-qie-bo-fen-lie-zhi-liang-jian-ce.html", "labels": ["other"], "postTitle": "[Literature Reading]\u57fa\u4e8e\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u526a\u5207\u6ce2\u5206\u88c2\u8d28\u91cf\u68c0\u6d4b", "postUrl": "post/%5BLiterature%20Reading%5D-ji-yu-shen-du-juan-ji-shen-jing-wang-luo-de-jian-qie-bo-fen-lie-zhi-liang-jian-ce.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/25", "commentNum": 0, "wordCount": 4575, "description": "## \u5f15\u8a00\r\n\r\n \u901a\u8fc7\u6d4b\u91cf\u5206\u88c2\u526a\u5207\u6ce2\u7684\u5feb\u6ce2\u6781\u5316\u65b9\u5411\uff08\u03c6\uff09\u548c\u6162\u6ce2\u5ef6\u8fdf\u65f6\u95f4\uff08\u03b4t\uff09\uff0c\u53ef\u4ee5\u63ed\u793a\u5730\u4e0b\u4ecb\u8d28\u7684\u5404\u5411\u5f02\u6027\u7279\u5f81\u3002", "top": 0, "createdAt": 1734616560, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-19", "dateLabelColor": "#bc4c00"}, "P26": {"htmlDir": "docs/post/[Review]-ji-yu-jian-qie-bo-fen-lie-de-di-qiu-nei-bu-ge-xiang-yi-xing-yan-jiu-zong-shu.html", "labels": ["other"], "postTitle": "[Review]\u57fa\u4e8e\u526a\u5207\u6ce2\u5206\u88c2\u7684\u5730\u7403\u5185\u90e8\u5404\u5411\u5f02\u6027\u7814\u7a76\u7efc\u8ff0", "postUrl": "post/%5BReview%5D-ji-yu-jian-qie-bo-fen-lie-de-di-qiu-nei-bu-ge-xiang-yi-xing-yan-jiu-zong-shu.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/26", "commentNum": 0, "wordCount": 2910, "description": "\r\n## **1. \u5730\u7403\u5185\u90e8\u5404\u5411\u5f02\u6027**\r\n\r\n### **1.1 \u5b9a\u4e49**\r\n- **\u5404\u5411\u5f02\u6027**\u6307\u5730\u7403\u4ecb\u8d28\u7684\u7269\u7406\u548c\u5316\u5b66\u5c5e\u6027\u968f\u65b9\u5411\u7684\u4e0d\u540c\u800c\u53d8\u5316\u3002", "top": 0, "createdAt": 1734839763, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-22", "dateLabelColor": "#bc4c00"}, "P27": {"htmlDir": "docs/post/[Literature Reading]-jin-chang-di-zhen-kuai-man-heng-bo-dao-shi-cha-ce-liang-li-san-bian-xi-he-gai-zheng.html", "labels": ["other"], "postTitle": "[Literature Reading]\u8fd1\u573a\u5730\u9707\u5feb\u6162\u6a2a\u6ce2\u5230\u65f6\u5dee\u6d4b\u91cf\u79bb\u6563\u8fa8\u6790\u548c\u6539\u6b63", "postUrl": "post/%5BLiterature%20Reading%5D-jin-chang-di-zhen-kuai-man-heng-bo-dao-shi-cha-ce-liang-li-san-bian-xi-he-gai-zheng.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/27", "commentNum": 0, "wordCount": 2509, "description": "## 1. \u7814\u7a76\u80cc\u666f\u4e0e\u610f\u4e49\r\n\r\n### 1.1 \u526a\u5207\u6ce2\u5206\u88c2\u73b0\u8c61\r\n- \u526a\u5207\u6ce2\uff08S\u6ce2\uff09\u5206\u88c2\u662f\u6a2a\u6ce2\u5728\u901a\u8fc7\u5404\u5411\u5f02\u6027\u4ecb\u8d28\u65f6\u7684\u4e00\u79cd\u91cd\u8981\u73b0\u8c61\u3002", "top": 0, "createdAt": 1735019446, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-24", "dateLabelColor": "#bc4c00"}, "P28": {"htmlDir": "docs/post/[Literature Reading]splitracer.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]splitracer", "postUrl": "post/%5BLiterature%20Reading%5Dsplitracer.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/28", "commentNum": 0, "wordCount": 4744, "description": "## Abstract\r\n\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u578b\u7684\u81ea\u52a8\u5316\u5de5\u5177\uff0c\u65e8\u5728\u63d0\u9ad8\u5927\u89c4\u6a21\u5730\u9707\u6570\u636e\u96c6\u7684\u5206\u6790\u6548\u7387\u4e0e\u5ba2\u89c2\u6027\u3002", "top": 0, "createdAt": 1735046562, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-24", "dateLabelColor": "#bc4c00"}, "P29": {"htmlDir": "docs/post/[Literature Reading]SplitLab.html", "labels": ["Computers&Geosciences"], "postTitle": "[Literature Reading]SplitLab", "postUrl": "post/%5BLiterature%20Reading%5DSplitLab.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/29", "commentNum": 0, "wordCount": 1937, "description": "# SplitLab: \u526a\u5207\u6ce2\u5206\u88c2\u6570\u636e\u5904\u7406\u73af\u5883\u603b\u7ed3\r\n\r\n## \u80cc\u666f\u4e0e\u76ee\u6807\r\n- \u526a\u5207\u6ce2\u5206\u88c2\uff08Shear Wave Splitting, SWS\uff09\u662f\u7814\u7a76\u5730\u58f3\u548c\u5730\u5e54\u5404\u5411\u5f02\u6027\u7684\u91cd\u8981\u65b9\u6cd5\u3002", "top": 0, "createdAt": 1735220279, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-26", "dateLabelColor": "#bc4c00"}, "P30": {"htmlDir": "docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html", "labels": ["JGR"], "postTitle": "[Literature Reading]Multichannel analysis of shear wave splitting ", "postUrl": "post/%5BLiterature%20Reading%5DMultichannel%20analysis%20of%20shear%20wave%20splitting%20.html", "postSourceUrl": "https://github.com/Lszidv/Lszidv.github.io/issues/30", "commentNum": 0, "wordCount": 5254, "description": "## Abstract\r\n\u79d1\u5b66\u95ee\u9898\uff1a\r\n\u5982\u4f55\u8054\u5408\u591a\u4e2a\u9707\u76f8\u63d0\u9ad8\u6d4b\u91cf\u7ed3\u679c\u7684\u9c81\u68d2\u6027\uff1f\r\n\u5982\u4f55\u5728\u590d\u6742\u533a\u57df\u6709\u6548\u533a\u5206\u4e0d\u540c\u7684\u5404\u5411\u5f02\u6027\u7279\u5f81\uff1f\r\n\u5982\u4f55\u5904\u7406\u4f4e\u4fe1\u566a\u6bd4\u6570\u636e\u5e76\u63d0\u9ad8\u7ed3\u679c\u7684\u7a33\u5065\u6027\uff1f\r\n\r\n## Introduction\r\nThe analysis of shear wave splitting is greatly simplified if the polarization of the incoming wave is known.\r\n\r\n#### \u65b9\u6cd5\u4e00\uff1a\u53e0\u52a0\u6a2a\u5411\u5206\u91cf\u65b9\u6cd5\uff08Stacking the transverse components method\uff09\r\n- **\u6838\u5fc3\u539f\u7406**\uff1a\r\n - \u901a\u8fc7\u53e0\u52a0\u591a\u4e2a\u9707\u76f8\u8bb0\u5f55\u7684\u6a2a\u5411\u5206\u91cf\uff0c\u627e\u5230\u6700\u5927\u632f\u5e45\u7684\u5feb\u6ce2\u65b9\u5411\u548c\u5ef6\u8fdf\u65f6\u95f4\u3002", "top": 0, "createdAt": 1735543251, "style": "", "script": "", "head": "", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "createdDate": "2024-12-30", "dateLabelColor": "#bc4c00"}}, "singeListJson": {}, "labelColorDict": {"bug": "#d73a4a", "Code": "#0E8A16", "Computers&Geosciences": "#fbca04", "documentation": "#0075ca", "enhancement": "#a2eeef", "GJI": "#d876e3", "GRL": "#7057ff", "JGR": "#0BF53A", "other": "#bfdadc", "point": "#5319E7", "SRL": "#7A6EA5", "wontfix": "#ffffff", "\u5730\u7403\u7269\u7406\u5b66\u62a5": "#008672", "\u5730\u9707\u5b66\u62a5": "#e4e669"}, "displayTitle": "\u829c\u5c3d", "faviconUrl": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "ogImage": "https://avatars.githubusercontent.com/u/160511559?s=400&u=68ec73daff523efd8652079b221d42e446d01cb6&v=4", "primerCSS": "", "homeUrl": "https://Lszidv.github.io", "prevUrl": "/index.html", "nextUrl": "disabled"} \ No newline at end of file diff --git a/docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html b/docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html index 23606dd..c0494f9 100644 --- a/docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html +++ b/docs/post/[Literature Reading]Multichannel analysis of shear wave splitting .html @@ -201,7 +201,285 @@

两种方法对比

单震相分析，高质量数据的精确测量 - + +
+

3. Effects of a Dipping Axis of Symmetry

+

剪切波在各向异性层中的传播总结

+

1. 剪切波分裂的物理本质

+ +
+

2. 数学模型

+ +

$$ +R(t) = w(t + \frac{\delta t}{2}) \cos^2\phi + w(t - \frac{\delta t}{2}) \sin^2\phi +$$

+

$$ +T(t) = \frac{1}{2} \left[ w(t + \frac{\delta t}{2}) - w(t - \frac{\delta t}{2}) \right] \sin 2\phi +$$

+ +

$$ +R(t) \approx w(t) +$$

+

$$ +T(t) \approx \frac{\delta t}{2} w'(t) \sin 2\phi +$$

+

其中 $w'(t)$ 是 $w(t)$ 的时间导数。

+ +

$$ +T = a \cdot s \otimes r +$$

+ +
+

3. 横向各向同性（TI-H）模型

+ +

$$ +s(\phi) = \delta t \sin[2(\phi - \phi_0)] +$$

+
+

4. 多震相的传播特性

+ +
+

5. 剪切波分裂的观测与测量

+ +
+

总结

+ +

3. Effects of a Dipping Axis of Symmetry

+

核心思想

+ +
+

分裂强度矩阵

+ +

$$ +\mathbf{S} = \sum_i \mathbf{T}_i \cdot \mathbf{T}_i^T +$$

+

其中：

+ +
+

奇异值分解（SVD）技术

+ +

$$ +\mathbf{S} = \mathbf{U} \Sigma \mathbf{V}^T +$$

+

其中：

+ +
+

Multichannel 方法的步骤

+
    +
  1. +数据准备： +
      +
    • 收集多个震相的横波分量数据（如 SKS 波和 SKKS 波）。
      • +
    +
    2. +
  2. +构建分裂强度矩阵： +
      +
    • 将所有震相的横波分量数据叠加，生成分裂强度矩阵 $\mathbf{S}$。
      • +
    +
    3. +
  3. +奇异值分解： +
      +
    • 对矩阵 $\mathbf{S}$ 进行奇异值分解，提取快波方向 $\phi$ 和延迟时间 $\delta t$。
      • +
    +
    4. +
  4. +参数优化： +
      +
    • 使用优化算法进一步调整结果，提高分裂参数的准确性。
      • +
    +
    5. +
  5. +结果验证： +
      +
    • 验证测量结果的稳定性和可靠性，例如通过残差分析或对比多个震相的独立结果。
      • +
    +
    6. +
+
+

方法局限

+ +
转载请注明出处
@@ -302,6 +580,6 @@

两种方法对比

- +