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CREDIT-X1local:A reference dataset for machine learning seismology from ChinArray in Southwest China

Earthquake Science 2024年第2期37卷 139-157页

作者： Lu Li Weitao Wang ziye yu Yini Chen Institute of Geophysics China Earthquake AdministrationBeijing 100081China Key Laboratory of Earthquake Source Physics China Earthquake AdministrationBeijing 100081China Zhejiang Earthquake Agency HangzhouZhejiang 310013China

High-quality datasets are critical for the development of advanced machine-learning algorithms in ***,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic ***-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 *** 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each *** types of phase labels are *** first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn *** second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to *** phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn ***,first-motion polarities are included for 31,273 Pg *** event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and *** CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography ***,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries.

关键词： earthquake dataset machine learning Pg/Sg/Pn/Sn phase picking P-wave first-motion polarity

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基于迁移学习的岩石边坡微地震事件检测算法

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地震地磁观测与研究 2024年第2期45卷 20-27页

作者：蔡育埼于子叶中国地震局地球物理研究所中国北京100081

基于迁移学习,设计一套岩石边坡微地震事件检测算法流程,用于自动化处理岩石边坡数据。基于海量人工标注的天然地震数据进行训练,得到深度学习预训练模型,并利用少量人工标注的微地震数据进行微调,使得模型可以适用于滑坡体微地震数据... 详细信息

基于迁移学习,设计一套岩石边坡微地震事件检测算法流程,用于自动化处理岩石边坡数据。基于海量人工标注的天然地震数据进行训练,得到深度学习预训练模型,并利用少量人工标注的微地震数据进行微调,使得模型可以适用于滑坡体微地震数据。采用实际标注数据进行测试,结果表明,基于迁移学习模型的查准率和查全率分别可达0.884和0.91。分析认为,在迁移学习流程中,深度学习模型减少了对于标注数据的依赖,同时可以仅经少量迭代即可得到鲁棒的、高精度结果。该模型部分程序是开源的,可以将其迁移到更多区域的微地震事件检测工作中。

关键词：迁移学习微地震事件检测深度学习边坡

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Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

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Earthquake Science 2023年第2期36卷 113-131页

作者： ziye yu Weitao Wang Yini Chen Institute of Geophysics China Earthquake AdministrationBeijing 100081China Key Laboratory of Earthquake Source Physics China Earthquake AdministrationBeijing 100081China Zhejiang Earthquake Agency Hangzhou 310013China

Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and ***,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore ***,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU *** based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive *** performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most *** RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and ***++requires the most computational effort among the *** all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their *** beginners,we provide a tutorial on training and validating the pickers using the DiTing *** also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by *** of our models are open-source and publicly accessible.

关键词： neural network deep learning seismic phase picking earthquake detection open-source science

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Template matching for simple waveforms with low signal-to-noise ratio and its application to icequake detection

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Earthquake Science 2020年第5期33卷 256-263页

作者： Haichao Ma Risheng Chu Minhan Sheng ziye yu State Key Laboratory of Geodesy and Earth’s Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Institute of Geophysics China Earthquake AdministrationBeijing 100081China

Template matching is a useful method to detect seismic events through waveform similarity between two *** traditional template matching method works well in detecting small tectonic ***,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal *** this study,we modify the traditional template matching approach for this *** first construct a virtual three-component seismic station using vertical-component waveforms recorded by three ***,we select a template event from the virtual station,and apply the traditional template *** then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching *** can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs.

关键词： template matching icequake detection Urumqi Glacier No.1

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The high-resolution community velocity model V2.0 of southwest China,constructed by joint body and surface wave tomography of data recorded at temporary dense arrays

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中国科学:地球科学英文版 2023年第10期66卷 2368-2385页

作者： Ying LIU ziye yu Zhiqi ZHANG Huajian YAO Weitao WANG Haijiang ZHANG Hongjian FANG Lihua FANG Laboratory of Seismology and Physics of Earth’s Interior School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei230026China Institute of Geophysics China Earthquake AdministrationBeijing100081China Mengcheng National Geophysical Observatory Mengcheng233500China CAS Center for Excellence in Comparative Planetology Mengcheng233500China School of Earth Sciences and Engineering Sun Yat-Sen UniversityZhuhai519080China

The Sichuan-yunnan area is located at the southeastern margin of the Tibetan Plateau,where tectonic movement is strong with deep and large faults distributed in a staggered manner,which results in strong seismic activities and severe earthquake *** the 21st century,several earthquakes of magnitude 7.0 or above occurred in this region,which have caused huge casualties and economic losses,especially the 2008 M_(s)8.0 Wenchuan *** present,earthquake monitoring and source parameter inversion,strong earthquake hazard analysis and disaster assessment are still the focus of seismological researches in the Sichuan-yunnan *** high-precision 3D community velocity models are fundamental for these *** this paper,by assembling seismic observations at permanent seismic stations and several temporary dense seismic arrays in this region,we obtained about 7.06 million body wave travel time data(including absolute and differential travel times)using a newly developed artificial intelligence body wave arrival time picking method and about 100,000 Rayleigh wave phase velocity dispersion data in the period range of 5-50 s from ambient noise cross-correlation *** on this abundant dataset,we obtained the three-dimensional high resolution V_p and V_(s)model in the crust and uppermost mantle of southwest(SW)China by adopting the joint body and surface wave travel time tomography method considering the topography effect starting from the first version of community velocity model in SW China(SWChina CVM-1.0).Compared to SWChina CVM-1.0,this newly determined velocity model has higher resolution and better data *** is accepted by the China Seismic Experimental Site as the second version of the community velocity model in SW China(SWChina CVM-2.0).The new model shows strong lateral heterogeneities in the shallow *** disconnected low velocity zones are observed in the middle to lower crust,which is located in the Songpan-Ganzi block

关键词： Southwest China Joint tomography Community velocity model Seismic activity Fault zone

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Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes

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Earthquake Research in China 2020年第3期34卷 343-357页

作者： HU Jiupeng yu ziye KUANG Wenhuan WANG Weitao RUAN Xiang DAI Shigui Key Laboratory of Earthquake Source Physics Institute of GeophysicsChina Earthquake AdministrationBeijing 100081China The School of Earth Sciences and Engineering Nanjing UniversityNanjing 210023China State Key Laboratory of Geodesy and Earths Dynamics Institute of Geodesy and GeophysicsChinese Academy of SciencesWuhan 430077China Department of Geophysics Stanford UniversityCA 94305USA Sichuan Earthquake Agency Chengdu 610041China

Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional ***,methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time *** present study establishes a deep learning network model combining a convolutional neural network(CNN) and recurrent neural network(RNN).The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival *** neural network automatically picks the P-wave arrival time,providing a strong constraint for small earthquake *** model is shown to achieve an accuracy rate of 90.7 % in picking P waves of microseisms in the reservoir area,with a recall rate reaching 92.6% and an error rate lower than 2%.The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes,thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.

关键词： Deep Learning Phase Pick Reservoir Microseismic

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Controllable large-scale processing of temperature regulating sheath-core fibers with high-enthalpy for thermal management

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Nano Materials Science 2024年第3期6卷 337-344页

作者： ziye Chen Zexu Hu Shining Chen Senlong yu Liping Zhu Hengxue Xiang Meifang Zhu Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai201620China College of Mechanical and Engineering Donghua UniversityShanghai201620China

Temperature regulating fibers(TRF_(s)) with high enthalpy and high form stability are the key factors for thermal management. However, the enthalpies of most TRFsare not high, and the preparation methods are still at the laboratory scale. It remains a great challenge to use industrial spinning equipment to achieve continuous processing of TRF_(s) with excellent thermal and mechanical properties. Here, polyamide 6(PA6) based TRF_(s) with a sheath-core structure were prepared by bicomponent melt-spinning. The sheath-core TRF(TRF_(sc)) are composed of PA6 as sheath and functional PA6 as core, which are filled with the shape stable phase change materials(ssPCM),dendritic silica@polyethylene glycol(SiO_(2)@PEG). With the aid of the sheath structure, the filling content of SiO_(2)@PEG can reach 30 %, so that the enthalpy of the TRF_(s) can be as high as 21.3 J/g. The ultra-high enthalpy guarantees the temperature regulation ability during the alternating process of cooling and heating. In hot environment, the temperature regulation time is 6.59 min, and the temperature difference is 12.93℃. In addition, the mechanical strength of the prepared TRF_(sc) reaches 2.26 cN/dtex, which can fully meet its application in the field of thermal management textiles and devices to manage the temperature regulation of the human body or precision equipment, etc.

关键词： Thermal management Hybrid fibers Polyamides Bicomponent melt spinning Temperature regulating fibers

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层状多孔介质的地震电磁异常数值模拟方法研究

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地震学报 2016年第6期38卷 869-877页

作者：于子叶中国地震局地球物理研究所中国北京100081

本文针对电激发模型在各向同性分层介质中利用传播矩阵方法对电磁异常产生过程的数值模拟.层状模型中一部分为饱和多孔介质,另一部分为普通线弹性介质,数值模拟过程中将固体场作为主要场单独进行计算,而将其形变作为等效流体源计算耦合... 详细信息

本文针对电激发模型在各向同性分层介质中利用传播矩阵方法对电磁异常产生过程的数值模拟.层状模型中一部分为饱和多孔介质,另一部分为普通线弹性介质,数值模拟过程中将固体场作为主要场单独进行计算,而将其形变作为等效流体源计算耦合电磁场.模拟结果显示,地震波在经过饱和多孔介质边界的时候会产生电磁场,而其在流体层内部传播的过程中则未产生电磁异常.模拟结果可以用于解释一些伴随地震产生的电磁异常,包括地震发生时以及地震波到达时所产生的电磁异常现象,另一方面对于前震及其它一些无感地震所产生的电磁异常的解释也具有一定的参考价值.

关键词：电激发效应电磁异常饱和多孔介质电磁场模拟

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深度神经网络拾取地震P和S波到时

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地球物理学报 2018年第12期61卷 4873-4886页

作者：于子叶储日升盛敏汉中国科学院测量与地球物理研究所大地测量与地球动力学国家重点实验室武汉430077 中国科学院大学北京100049

从地震波形数据中快速准确地提取各个震相的到时是地震学中的基础问题.本文针对上述问题提出了利用深度神经网络拾取到时的新方法,建立了用于地震到时提取的17层Inception深度网络模型,在对原始三分量数据进行高通滤波和归一化处理后输... 详细信息

从地震波形数据中快速准确地提取各个震相的到时是地震学中的基础问题.本文针对上述问题提出了利用深度神经网络拾取到时的新方法,建立了用于地震到时提取的17层Inception深度网络模型,在对原始三分量数据进行高通滤波和归一化处理后输入网络直接输出到时信息.整个过程基于神经网络自适应提取波形特征,自动输出结果.通过对100组加了不同强度的噪声数据进行了可靠性检验,相比于其他方法神经网络方法对于噪声具有较高的容忍度以及稳定性,并且与地震目录数据有较高的相似性.相比于AR-AIC+STA/LTA,深度神经网络虽然运算速度稍慢,但整个过程不需设定时窗与阈值,同时具有更高的可用性,并且可以迭代升级以提高精度.此方法作为人工智能方法,为波形到时拾取提供了新思路.

关键词：震相拾取深度神经网络

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CRPN: A cascaded classification and regression DNN framework for seismic phase picking

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Earthquake Science 2020年第2期33卷 53-61页

作者： ziyeyu Risheng Chu Weitao Wang Minhan Sheng State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China Key Laboratory of Seismic Observation and Geophysical Imaging Institute of GeophysicsChina Earthquake AdministrationBeijing 100081China

Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy *** this study,we introduce a cascaded classification and regression framework for seismic phase picking,named as the classification and regression phase net(CRPN),which contains two convolutional neural network(CNN)models with different complexity to meet the requirements of accuracy and *** first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0,*** second stage of CRPN is used for high precision classification and *** regression is designed to reduce the time difference between the probability maximum and the real arrival *** being trained using 500,000 P and S phases,the CRPN can process 400 hours’seismic data per second,whose sampling rate is 1 Hz and 25 Hz for the two stages,respectively,on a Nvidia K2200 GPU,and pick 93%P and 89%S phases with the error being reduced by 0.1s after regression correction.

关键词： phase picking DNN efficiency

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