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

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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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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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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