Machine Learning in Earthquake Seismology

被引：113

作者：

Mousavi, S. Mostafa ^{[1
,2
]}

Beroza, Gregory C. ^{[2
]}

机构：

[1] Google Inc, Mountain View, CA 94043 USA

[2] Stanford Univ, Dept Geophys, Stanford, CA 94305 USA

来源：

ANNUAL REVIEW OF EARTH AND PLANETARY SCIENCES | 2023年 / 51卷

关键词：

machine learning; artificial intelligence; neural networks; earthquakes; seismology; CONVOLUTIONAL NEURAL-NETWORK; AUTOMATIC DISCRIMINATION; SEISMIC ARRIVALS; WAVE-FORMS; DEEP; PICKING; PHASE; CLASSIFICATION; PREDICTION; MODELS;

D O I：

10.1146/annurev-earth-071822-100323

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data mining techniques can significantly improve our capability for seismic data processing. In this review we provide a comprehensive overview of ML applications in earthquake seismology, discuss progress and challenges, and offer suggestions for future work.

引用

页码：105 / 129

页数：25

共 148 条

[1] A probabilistic neural network for earthquake magnitude prediction [J].

Adeli, Hojjat ;

Panakkat, Ashif .

NEURAL NETWORKS, 2009, 22 (07) :1018-1024

[2] An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs [J].

Aden-Antoniow, F. ;

Frank, W. B. ;

Seydoux, L. .

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 2022, 127 (02)

[3] Benchmarking Current and Emerging Approaches to Infrasound Signal Classification [J].

Albert, Sarah ;

Linville, Lisa .

SEISMOLOGICAL RESEARCH LETTERS, 2020, 91 (02) :921-929

[4] Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning [J].

Barkaoui, Salma ;

Lognonne, Philippe ;

Kawamura, Taichi ;

Stutzmann, Eleonore ;

Seydoux, Leonard ;

de Hoop, Maarten, V ;

Balestriero, Randall ;

Scholz, John-Robert ;

Sainton, Gregory ;

Plasman, Matthieu ;

Ceylan, Savas ;

Clinton, John ;

Spiga, Aymeric ;

Widmer-Schnidrig, Rudolf ;

Civilini, Francesco ;

Banerdt, W. Bruce .

BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2021, 111 (06) :2964-2981

[5] Machine learning and earthquake forecasting-next steps [J].

Beroza, Gregory C. ;

Segou, Margarita ;

Mostafa Mousavi, S. .

NATURE COMMUNICATIONS, 2021, 12 (01)

[6] Continuous Hidden Markov Models: Application to automatic earthquake detection and classification at Las Canadas caldera, Tenerife [J].

Beyreuther, Moritz ;

Carniel, Roberto ;

Wassermann, Joachim .

JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH, 2008, 176 (04) :513-518

[7] Continuous earthquake detection and classification using discrete Hidden Markov Models [J].

Beyreuther, Moritz ;

Wassermann, Joachim .

GEOPHYSICAL JOURNAL INTERNATIONAL, 2008, 175 (03) :1055-1066

[8] PINNeik: Eikonal solution using physics-informed neural networks [J].

bin Waheed, Umair ;

Haghighat, Ehsan ;

Alkhalifah, Tariq ;

Song, Chao ;

Hao, Qi .

COMPUTERS & GEOSCIENCES, 2021, 155

[9] PreSEIS:: A neural network-based approach to earthquake early warning, for finite faults [J].

Boese, Maren ;

Wenzel, Friedemann ;

Erdik, Mustafa .

BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2008, 98 (01) :366-382

[10] A new interpretation of seismic tomography in the southern Dead Sea basin using neural network clustering techniques [J].

Braeuer, Benjamin ;

Bauer, Klaus .

GEOPHYSICAL RESEARCH LETTERS, 2015, 42 (22) :9772-9780

← 1 2 3 4 5 6 7 8 9 10 →