Multistation seismic location via machine learning: application to Oklahoma and Southern California

DOI number:10.1093/gji/ggaf135

Affiliation of Author(s):中国科学技术大学地球和空间科学学院

Journal:Geophysical Journal International

Place of Publication:英国

Abstract:Locating earthquakes plays an important role in the study of seismic activity and geological structures. Traditional methods for locating earthquakes mainly rely on waveform matching and traveltime fitting. With the development of artificial intelligence technology, machine learning methods have often been applied to locate earthquakes. However, current machine learning approaches may face challenges related to physical constraints. In this study, we build a 3-D U-Net network with station distribution constraints to locate earthquakes. To improve the generalizability of the network model, we a

First Author:Ji Zhang

Co-author:Huiyu Zhu,Zefeng Li,Xinming Wu, Jie Zhang

Indexed by:Journal paper

Correspondence Author:Ji Zhang

Document Code:39

Discipline:Natural Science

First-Level Discipline:Geophysics

Document Type:SCI

Volume:241

Issue:3

Page Number:1853-1867

ISSN No.:0956-540X

Translation or Not:no

Date of Publication:2025-04-10

Links to published journals:https://doi.org/10.1093/gji/ggaf135