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{"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": "", "indexScript": "", "indexStyle": "", "bottomText": "\u8f6c\u8f7d\u8bf7\u6ce8\u660e\u51fa\u5904", "showPostSource": 1, "iconList": {}, "UTC": 8, "rssSplit": "sentence", "exlink": {}, "needComment": 1, "allHead": "", "title": "\u829c\u5c3d", "subTitle": "Only love can set us free", "avatarUrl": "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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": 10588, "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": "<script>MathJax = {tex: {inlineMath: [[\"$\", \"$\"]]}};</script><script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js\"></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": 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