WO2021027026A1 - Procédé de reconnaissance de la nature de vibrations d'ondes sismiques, basé sur l'apprentissage automatique - Google Patents

Procédé de reconnaissance de la nature de vibrations d'ondes sismiques, basé sur l'apprentissage automatique Download PDF

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WO2021027026A1
WO2021027026A1 PCT/CN2019/107486 CN2019107486W WO2021027026A1 WO 2021027026 A1 WO2021027026 A1 WO 2021027026A1 CN 2019107486 W CN2019107486 W CN 2019107486W WO 2021027026 A1 WO2021027026 A1 WO 2021027026A1
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waveform
seismic
model
training
data
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PCT/CN2019/107486
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Chinese (zh)
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刘昕靓
任涛
王柳婷
杨丹丹
商冰冰
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东北大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology

Definitions

  • the present invention relates to the technical field of machine learning, in particular to a method for identifying seismic wave vibration properties based on machine learning.
  • Natural earthquakes are squeezing and collision between the earth's plates and plates, causing dislocations and ruptures on the edges and inside of the plates.
  • Unnatural earthquakes also known as induced earthquakes, refer to abnormal seismic activities in a local area caused by human activities, such as artificial nuclear explosion tests or collapses. Observing the seismic wave signals of natural and non-natural earthquakes recorded at each station of the Seismic Network Center, it is found that the waveforms of the two are very similar. If they are not distinguished and screened, the natural and artificial seismic waveforms will be mixed to study the earthquake. The scholars mislead and influence the research work of seismology.
  • Machine learning theory and application research began in 1986. With the rapid development of artificial intelligence, it has a wide range of applications in the fields of image recognition, signal processing, predictive evaluation, combinatorial optimization, and knowledge engineering. In recent years, machine learning methods have begun to be used to analyze and process seismic waveform data. How to apply the ever-changing computer frontier technology to the classification of seismic events so as to improve the accuracy and stability of recognition is still one of the topics that need to be studied. . Domestic experts and scholars combined genetic algorithm (GA) with BP neural network when identifying natural earthquakes and unnatural earthquakes, and established a genetic BP network (GA-BP network). The genetic algorithm is used to optimize the neural network globally, and then Use the BP backpropagation algorithm to accurately train the neural network. Dowla et al.
  • MLP multilayer perceptron
  • HHT Hilbert Huang Transform
  • the present invention provides a method for identifying seismic wave vibration properties based on machine learning.
  • the present invention classifies waveforms based on waveform data to identify natural earthquakes and non-natural earthquakes.
  • the Python programming language is mainly used to process the waveform data
  • the machine learning method-artificial neural network is used to classify and recognize seismic waves.
  • the invention adopts the supervised learning method to obtain the seismic property classifier, that is, the artificial neural network model is trained with data of known categories.
  • the input of the model is a feature vector, including features that can reflect the nature of the earthquake.
  • the present invention considers both the time domain and the frequency domain to ensure that comprehensive features of the waveform are obtained.
  • the technical solution adopted by the present invention is: a method for identifying the nature of seismic wave vibration based on machine learning; including the following steps:
  • Step 1 Read the original seismic waveform data and determine the epicenter distance of the seismic waveform that needs to be classified and identified;
  • step 1 The specific steps of step 1 are:
  • Step 1.1 Use the Python library obspy in Anaconda for the seismic field to read the original seismic waveform data, and select the waveform according to the spherical distance between the seismic source and the station;
  • Step 1.2 Use the STA/LTA algorithm to find the interval containing the earthquake starting point, and use the AIC algorithm to accurately locate the time of the first arrival of the P wave;
  • Step 1.3 After determining the first arrival of the P wave, intercept the seismic waveform to a uniform length to facilitate the extraction of waveform characteristics for subsequent analysis and processing;
  • Step 2 Perform time-frequency domain analysis on the natural earthquake and unnatural seismic waveforms respectively, the time-domain analysis obtains the waveform complexity characteristics, the frequency domain analysis obtains the spectral ratio characteristics, and the two characteristic values are calculated to obtain the time-frequency domain comprehensive characteristics Value waveform complexity/spectrum ratio;
  • the waveform complexity and spectral ratio characteristics are respectively the characteristic quantities that can characterize the waveform trend in the time domain and the frequency domain;
  • Step 3 Use the artificial neural network to train the model.
  • the model input is the feature vector of the waveform instance, that is, the feature vector composed of the three feature values obtained in step 2.
  • the output result is calculated by two hidden layers, and the output result is "1 "Indicates that the category of the waveform is natural earthquake, and the output result of "0" indicates that the category of the waveform is non-natural earthquake;
  • Step 4 Divide the seismic waveform data into two disjoint data sets, the training set and the test set, use the backpropagation algorithm to update the model parameters, continuously fit the training data, and reduce the value of the loss function; test must be used during model training Collect data to evaluate the effect of the model. After a specified number of iterations of training or the model's test accuracy reaches a certain standard, it means that the model training is completed;
  • Step 5 Save the model.
  • the output result of the saved model can be directly called for subsequent seismic waveform classification problems, without tedious training.
  • the beneficial effects of using the above technical solutions are: the intensity and impact of non-natural earthquakes are smaller than those of natural earthquakes. If the non-natural earthquakes cannot be identified and screened out in time, people will mistakenly believe that these events are strong earthquakes. The precursors of confuses the earthquake catalogue established based on this record, which will affect the future research work of seismology. Therefore, the classification and identification of natural earthquakes and non-natural earthquakes in seismic signals are helpful for monitoring and early warning of destructive tectonic earthquakes, for small-equivalent nuclear test reconnaissance, for the study of seismology, for protecting human property and safeguarding national interests. World peace has important meaning.
  • Fig. 1 is a flowchart of waveform complexity feature extraction according to the present invention
  • Figure 2 is a flow chart of the spectral ratio feature extraction of the present invention
  • Fig. 3 is a structural diagram of a model constructed by an artificial neural network used in the present invention.
  • the software environment of this embodiment is the WINDOWS 7 system, and the integrated development environment selects Pycharm IDE.
  • the identification method of seismic wave vibration properties based on machine learning includes the following steps:
  • Step 1 Read the original seismic waveform data and determine the epicenter distance of the seismic waveform that needs to be classified and identified;
  • the epicenter distance is the spherical distance between the station that recorded the waveform and the epicenter;
  • step 1 The specific steps of step 1 are:
  • Step 1.1 Use the Python library obspy in Anaconda for the seismic field to read the original seismic waveform data, and select the local seismic waveform according to the spherical distance between the source and the station;
  • Step 1.2 Use the STA/LTA algorithm to find the interval containing the earthquake starting point, and use the AIC algorithm to accurately locate the time of the first arrival of the P wave;
  • Step 1.3 After determining the first arrival of the P wave, intercept the seismic waveform to a uniform length to facilitate the extraction of waveform characteristics for subsequent analysis and processing;
  • the seismic wave transmission file ".mseed” is read through the obspy library and used to find the starting point by combining STA/LTA and AIC; the data is intercepted to a uniform length to facilitate the extraction of waveform features.
  • This embodiment determines that the seismic wave determines the P wave first arrival After the earthquake's starting point, the data of 3 seconds before the starting point and the data of 160 seconds after the starting point are intercepted, a total of 163s of data are processed for subsequent analysis;
  • Step 2 Perform time-frequency domain analysis on the natural earthquake and unnatural seismic waveforms respectively, the time-domain analysis obtains the waveform complexity characteristics, the frequency domain analysis obtains the spectral ratio characteristics, and the two characteristic values are calculated to obtain the time-frequency domain comprehensive characteristics Value waveform complexity/spectrum ratio;
  • the waveform complexity and spectral ratio characteristics are the characteristic quantities that can characterize the waveform trend at the time domain level and the frequency domain level, respectively, and the extraction process is shown in Figure 1 and Figure 2;
  • Step 3 Use the artificial neural network to train the model.
  • the model input is the feature vector of the waveform instance, that is, the feature vector composed of the three feature values obtained in step 2, and the output result is calculated through two hidden layers ,
  • the output result of "1" indicates that the category of the waveform is natural earthquake, and the output result of "0" indicates that the category of the waveform is non-natural earthquake;
  • Hidden layers 2 Number of hidden layer neurons 6 Prevent overfitting L2 regularization Learning rate Exponential decay algorithm update Loss function Binary cross entropy loss function Number of training iterations 500,000
  • Step 4 Divide the seismic waveform data into two disjoint data sets, the training set and the test set, use the backpropagation algorithm to update the model parameters, continuously fit the training data, and reduce the value of the loss function; test must be used during model training Collect data to evaluate the effect of the model. After a specified number of iterations of training or the model's test accuracy reaches a certain standard, it means that the model training is completed;
  • the setting standard for the number of training iterations in this embodiment is 500,000;
  • Step 5 Save the model.
  • the output result of the saved model can be directly called for subsequent seismic waveform classification problems, without tedious training.

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Abstract

L'invention concerne un procédé de reconnaissance de la nature de vibrations d'ondes sismiques, basé sur l'apprentissage automatique, ledit procédé ayant trait au domaine technique de l'apprentissage automatique. La mise en oeuvre du procédé selon l'invention comprend quatre étapes : le traitement de formes d'ondes sismiques, l'extraction de valeurs de caractéristiques, l'entraînement de modèle et l'application de modèle, ces étapes consistant : à calculer la distance entre une source sismique et une station, à examiner une distance épicentrale, à lire des données de formes d'ondes sismiques à trois composantes de la distance épicentrale examinée, au moyen d'un algorithme à fenêtre temporelle longue/fenêtre temporelle courte (STA/LTA) et d'un procédé AIC permettant de déterminer avec précision l'emplacement d'un premier mouvement d'ondes sismiques, puis d'intercepter une longueur de données sismiques ; à effectuer respectivement une analyse de domaine temporel et une analyse de domaine fréquentiel sur une forme d'onde sismique ; à extraire trois complexités de forme d'onde caractéristiques, un rapport de spectre et un rapport complexité de forme d'onde/spectre comme entrée d'un modèle de réseau neuronal artificiel ; et à entraîner un modèle de réseau neuronal artificiel avec deux couches cachées utilisées pour identifier un problème à deux classifications, et à émettre en sortie la probabilité d'une catégorie à laquelle appartient la forme d'onde. Le modèle entraîné par le procédé selon l'invention peut déterminer avec précision et efficacité une catégorie à laquelle appartient une forme d'onde.
PCT/CN2019/107486 2019-08-15 2019-09-24 Procédé de reconnaissance de la nature de vibrations d'ondes sismiques, basé sur l'apprentissage automatique WO2021027026A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210117737A1 (en) * 2019-10-18 2021-04-22 Korea University Research And Business Foundation Earthquake event classification method using attention-based convolutional neural network, recording medium and device for performing the method
CN113671564A (zh) * 2021-08-11 2021-11-19 西安石油大学 基于narx动态神经网络的微地震有效事件自动拾取方法
CN115963548A (zh) * 2023-01-16 2023-04-14 中国矿业大学 一种基于反绎学习的矿井微震p波到时拾取模型构建方法
CN116047604A (zh) * 2023-02-23 2023-05-02 成都理工大学 基于振幅统计和时频分析的深度震相快速拾取方法
CN116046307A (zh) * 2022-12-12 2023-05-02 中铁西北科学研究院有限公司 一种适用于振动台含隧道边坡地震破坏模式的识别方法
CN116502154A (zh) * 2023-04-25 2023-07-28 中国地震局地球物理研究所 基于多维特征提取和XGBoost的地震分类方法及系统

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5617513A (en) * 1992-03-06 1997-04-01 Schnitta; Bonnie S. Method for analyzing activity in a signal
CN102495425A (zh) * 2011-11-14 2012-06-13 北京科技大学 一种基于能量的微地震震源自动定位方法
CN107300715A (zh) * 2017-06-22 2017-10-27 禁核试北京国家数据中心 一种识别核爆炸地震事件的方法
CN107818309A (zh) * 2017-11-02 2018-03-20 中国地震局地壳应力研究所 一种基于信噪比分析的地震动p波在线自动提取方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108805269B (zh) * 2018-06-28 2021-11-30 杭州叙简科技股份有限公司 一种基于lstm循环神经网络拾取震相到时的方法
CN110082822B (zh) * 2019-04-09 2020-07-28 中国科学技术大学 利用卷积神经网络进行地震检测的方法
CN110032975B (zh) * 2019-04-15 2021-09-07 禁核试北京国家数据中心 一种地震震相的拾取方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5617513A (en) * 1992-03-06 1997-04-01 Schnitta; Bonnie S. Method for analyzing activity in a signal
CN102495425A (zh) * 2011-11-14 2012-06-13 北京科技大学 一种基于能量的微地震震源自动定位方法
CN107300715A (zh) * 2017-06-22 2017-10-27 禁核试北京国家数据中心 一种识别核爆炸地震事件的方法
CN107818309A (zh) * 2017-11-02 2018-03-20 中国地震局地壳应力研究所 一种基于信噪比分析的地震动p波在线自动提取方法

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210117737A1 (en) * 2019-10-18 2021-04-22 Korea University Research And Business Foundation Earthquake event classification method using attention-based convolutional neural network, recording medium and device for performing the method
US11947061B2 (en) * 2019-10-18 2024-04-02 Korea University Research And Business Foundation Earthquake event classification method using attention-based convolutional neural network, recording medium and device for performing the method
CN113671564A (zh) * 2021-08-11 2021-11-19 西安石油大学 基于narx动态神经网络的微地震有效事件自动拾取方法
CN116046307A (zh) * 2022-12-12 2023-05-02 中铁西北科学研究院有限公司 一种适用于振动台含隧道边坡地震破坏模式的识别方法
CN115963548A (zh) * 2023-01-16 2023-04-14 中国矿业大学 一种基于反绎学习的矿井微震p波到时拾取模型构建方法
CN115963548B (zh) * 2023-01-16 2024-01-23 中国矿业大学 一种基于反绎学习的矿井微震p波到时拾取模型构建方法
CN116047604A (zh) * 2023-02-23 2023-05-02 成都理工大学 基于振幅统计和时频分析的深度震相快速拾取方法
CN116047604B (zh) * 2023-02-23 2023-10-17 成都理工大学 基于振幅统计和时频分析的深度震相快速拾取方法
CN116502154A (zh) * 2023-04-25 2023-07-28 中国地震局地球物理研究所 基于多维特征提取和XGBoost的地震分类方法及系统

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