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earthquake detection in the Jiangsu region, China using graphics-processing-unit-based Match & Locate and rapid earthquake association and location

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earthquake Science 2020年第1期33卷 23-33页

作者： Yafen Huang Shengzhong Zhang Yuejun Lv Yanzhen Li Yuting Zhang Min Liu School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China Shanghai Sheshan National Geophysical Observatory Shanghai201602China Information Network Center China University of GeosciencesBeijing 100083China Institute of Crustal Dynamics China Earthquake AdministrationBeijing 100085China

earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based *** evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for ***-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time ***-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&*** number of newly detected and located events is about 2.8 times more than those listed in the local *** both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the *** then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,*** newly detected events are mainly clustered and show steeply dipping fault *** analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local *** REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.

关键词： earthquake detection rapid earthquake association and location graphics-processing-unit-based Match and Locate

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Comparison of the earthquake detection abilities of PhaseNet and EQTransformer with the Yangbi and Maduo earthquakes

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earthquake Science 2021年第5期34卷 425-435页

作者： Ce Jiang Lihua Fang Liping Fan Boren Li Institute of Geophysics China Earthquake AdministrationBeijing 100081China Guangdong earthquake Agency Guangzhou 510070China Key Laboratory of earthquake Source Physics China Earthquake AdministrationBeijing 100081China Institute of Disaster Prevention Sanhe 065201China

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied *** evaluate the generaliz-ation ability of the two models and provide insights for the development of new models,this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic *** has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions,the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo *** has a higher recall than EQTransformer,but the recall of both models is reduced by 13%-56%when compared with the results rep-orted in the original *** analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection *** designing earthquake detection models,attention should be paid to not only the balance of depth,width,and architecture but also to the quality and quantity of the training *** addition,noise datasets should be incorporated during *** to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes,the Yangbi earthquake exhibited foreshock,while the Maduo earthquake showed no foreshock activity,indicating that the two earthquakes’nucleation processes were different.

关键词： earthquake detection deep learning PhaseNet EQTransformer Yangbi earthquake Maduo earth-quake

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Blockly earthquake transformer:A deep learning platform for custom phase picking

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Artificial Intelligence in Geosciences 2023年第1期4卷 84-94页

作者： Hao Mai Pascal Audet H.K.Claire Perry S.Mostafa Mousavi Quan Zhang Department of Earth and Environmental Sciences University of OttawaOttawaCanadaK1N 6N5 Canadian Hazards Information Service Natural Resources CanadaOttawaCanadaK1A 0E7 Department of Geophysics Stanford UniversityStanford94305USA

Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival *** many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize *** such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing *** address this challenge,we present Blockly earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase *** implements earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning *** provides an interactive dashboard to customize a model based on a particular *** the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base *** the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model *** no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.

关键词： earthquake detection Seismic phase identification Deep learning Seismology

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Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model

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Artificial Intelligence in Geosciences 2023年第1期4卷 150-163页

作者： Satyam Pratap Singh Vipul Silwal EarthByte Group School of GeosciencesThe University of SydneyNSW2006Australia Department of Earth Science Indian Institute of TechnologyRoorkeeUttarakhand247667India

The Hindu Kush-Pamir region(HKPR)is characterized by complex ongoing deformation,unique slab geometry,and intermediate seismic *** availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable *** this study,we present a fully automated approach for augmenting earthquake catalogue within the *** method leverages an attention mechanism-based deep learning architecture to simultaneously detect events,perform phase picking,and estimate *** applied this model to a ten-month dataset(January 2013-October 2013)from 83 stations in the *** a robust criterion to evaluate the model’s probabilities,we associated phases at different stations and pinpointed earthquake locations in the *** results demonstrate a significant enhancement,revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center(ISC)catalogue.A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes(earthquake catalogue facilitates a deeper understanding of the interplay between crustal and intermediate seismic activity in the HKPR,shedding light on the deformation and active faulting resulting from Eurasian-Indian plate interactions.

关键词： Deep-learning earthquake detection STEAD Hindu kush-pamir Seismicity

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A study on small magnitude seismic phase identification using 1D deep residual neural network

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Artificial Intelligence in Geosciences 2022年第1期3卷 115-122页

作者： Wei Li Megha Chakraborty Yu Sha Kai Zhou Johannes Faber Georg Rümpker Horst Stöcker Nishtha Srivastava Frankfurt Institute for Advanced Studies Frankfurt am Main60438Germany Institute of Geosciences Goethe-University FrankfurtFrankfurt am Main60438Germany Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Academy of Advanced Interdisciplinary ResearchXidian UniversityXian710071China Xidian-FIAS international Joint Research Center Giersch Science CenterFrankfurt am Main60438Germany Institut für Theoretische Physik Goethe Universität FrankfurtFrankfurt am Main60438Germany GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt64291Germany

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise *** improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been *** makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable *** this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase *** method is trained and tested on the dataset recorded by the Southern California Seismic *** demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic *** to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California earthquake Data *** model generalizability is also tested further on the STanford earthquake *** addition,the experimental result on the same subset of the STanford earthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.

关键词： Deep learning Residual neural network earthquake detection Seismic phase identification

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South China Sea Typhoon Hagibis enhanced Xinfengjiang Reservoir seismicity

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earthquake Science 2024年第3期37卷 210-223页

作者： Peng Zhang Xinlei Sun Yandi Zeng Zhuo Xiao Runqing Huang State Key Laboratory of Isotope Geochemistry Guangzhou Institute of GeochemistryGuangzhou 510640China University of Chinese Academy of Sciences Beijing 100049China College of Geosciences Chengdu University of TechnologyChengdu 610059China College of Artificial Intelligence Guangxi Minzu UniversityNanning 530006China

There was an evident increase in the number of earthquakes in the Xinfengjiang Reservoir from June to July 2014 after the landing of Typhoon *** understand the spatial and temporal evolution of this microseismicity,we built a high-precision earthquake catalog for 2014 and relocated 2275 events using recently developed methods for event picking and catalog *** occurred in the southeastern part of the reservoir,with the preferred fault plane orientation aligned along the Heyuan *** total seismic energy peaked when the typhoon passed through the reservoir,and seismicity correlated with typhoon *** contrast,a limited seismic response was observed during the later Typhoon *** data regarding the water level in the Xinfengjiang Reservoir and seismicity frequency changes in the Taiwan region during these two typhoon events,we suggest that typhoon activity may increase microseism energy by impacting fault stability around the Xinfengjiang *** a fault can be activated also depends on how close the stress accumulation is to its failure point.

关键词： typhoon seismicity analysis earthquake detection spatio-temporal evolution characteristics microseisms

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A high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

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earthquake Science 2021年第5期34卷 390-398页

作者： Yijian Zhou Abhijit Ghosh Lihua Fang Han Yue Shiyong Zhou Youjin Su Institute of Geophysics China Earthquake AdministrationBeijing 100081China Department of Earth Sciences University of CaliforniaRiversideCalifornia 92521USA Institute of Theoretical and Applied Geophysics Peking UniversityBeijing 100871China Yunnan earthquake Agency Yunnan 650224China

We present a high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi *** catalog has a time range of 2021-05-01 to 2021-05-28,and contains~8,000 well located *** captures the features of the whole foreshock sequence and the early *** designed a detection strategy incorporating both an artificial intelligent(AI)picker and a matched filter ***,we adopt a hybrid AI method incorporating convolutional and recurrent neural network(CNN&RNN)for event detection and phase picking respectively(***),a light-weight AI picker that can be trained with small volume of *** is first trained with detections from a STA/LTA and Kurtosis-based method called PAL,and then construct a rather complete template set of~4,000 ***,the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation,which finally results in precise *** process gives 9,026 detections,among which 7,943 events can be well *** catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity *** main features are:(1)the foreshock sequence images simple fault geometry with consistent strike,but also show a variable event depth along strike;(2)the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast;(3)complex seismicity are developed in the post-seismic period,indicating complex triggering ***,our catalog provides a reliable basis for further investigations,such as b-value studies,rupture process,and triggering relations.

关键词： Yangbi earthquake seismic catalog earthquake detection AI picker matched filter.

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DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

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earthquake Science 2023年第2期36卷 84-94页

作者： Ming Zhao Zhuowei Xiao Shi Chen Lihua Fang Institute of Geophysics China Earthquake AdministrationBeijing 100081China Beijing Baijiatuan Earth Sciences National Observation and Research Station Beijing 100095China Institute of Geology and Geophysics Chinese Academy of SciencesBeijing 100029China Key Laboratory of earthquake Source Physics China Earthquake AdministrationBeijing 100081China

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of *** amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology *** this study,based on the 2013–2020 seismic cataloging reports of the China earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time *** were recorded using broadband and short-period *** obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity *** waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an *** three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise *** magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,*** dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion *** research will further promote the development and application of artificial intelligence in seismology.

关键词： artificial intelligence benchmark dataset earthquake detection seismic phase identification first-motion polarity

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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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Monitoring the Technical Condition of Dams of Hydroelectric Power Plants with an Automated Monitoring and earthquake Registration System

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Journal of Energy and Power Engineering 2014年第8期8卷 1468-1474页

作者： Alexander Kuzmenko Daria Korolenko Evgeniy Zolotukhin Information System Laboratory Design Technological Institute of Digital Techniques Siberian Branch of the Russian Academy ofSciences Novosibirsk 630090 Russia

An approach is described that has been developed for auxiliary monitoring of technical condition of hydropower plant dams. It is based on analysis of changes in dynamic characteristics of dams obtained by an automated monitoring and earthquake registration system that records microseismic vibrations of structures. The configuration of the system as well as the results of seismometric monitoring of the dam of Krasnoyarsk hydroelectric power plant are described. To study behavior of the dam under normal and extreme loads it was proposed to develop a model of the dam with the use of the finite element method.

关键词： Seismometric monitoring earthquake detection dynamic characteristics of dams microseismic vibrations finite element analysis.

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