Machine learning and earthquake forecasting—next steps (2021-08-06T00:00:00.000000Z)

TL;DR

A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail and application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.

Abstract

A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.

Authors

G. Beroza

2 papers

M. Segou

1 papers

S. Mostafa Mousavi

1 papers

References18 items

Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence

Machine learning and earthquake forecasting—next steps

TL;DR

Abstract

Authors

References18 items

Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking

Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning

The Predictive Skills of Elastic Coulomb Rate-and-State Aftershock Forecasts during the 2019 Ridgecrest, California, Earthquake Sequence

A New Technique to Calculate Earthquake Stress Transfer and to Probe the Physics of Aftershocks

Real-time discrimination of earthquake foreshocks and aftershocks

Forecasting the magnitude of the largest expected earthquake

Improving Physics‐Based Aftershock Forecasts During the 2016–2017 Central Italy Earthquake Cascade

Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders

Machine learning for data-driven discovery in solid Earth geoscience

A Synoptic View of the Third Uniform California Earthquake Rupture Forecast (UCERF3)

Machine Learning Predicts Laboratory Earthquakes

Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes

Frequency of Earthquakes in California

On the aftershocks of earthquakes

All authors contributed to the development of this comment, and all were involved in revising it

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