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Title: Preface to the Focus Section on Machine Learning in Seismology

Abstract

Machine learning (ML) is a collection of algorithms and statistical models that enable computers to extract relevant patterns and information from large data sets. Unlike physical modeling approaches, in which scientists develop theories based on physical laws to compare with real–world observations, ML approaches learn directly from data without explicitly reasoning about the underlying physical mechanisms. ML algorithms are often categorized into supervised and unsupervised learning (see fig. 2 in Kong et al., 2018). Supervised learning algorithms build a model from existing labeled input data with the goal of predicting the labels of new data.

Authors:
 [1]; ORCiD logo [2];  [3]
  1. Harvard Univ., Cambridge, MA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1511263
Report Number(s):
LA-UR-19-20450
Journal ID: ISSN 0895-0695
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Seismological Research Letters
Additional Journal Information:
Journal Volume: 90; Journal Issue: 2A; Journal ID: ISSN 0895-0695
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Earth Sciences

Citation Formats

Bergen, Karianne J., Chen, Ting, and Li, Zefeng. Preface to the Focus Section on Machine Learning in Seismology. United States: N. p., 2019. Web. doi:10.1785/0220190018.
Bergen, Karianne J., Chen, Ting, & Li, Zefeng. Preface to the Focus Section on Machine Learning in Seismology. United States. doi:10.1785/0220190018.
Bergen, Karianne J., Chen, Ting, and Li, Zefeng. Wed . "Preface to the Focus Section on Machine Learning in Seismology". United States. doi:10.1785/0220190018.
@article{osti_1511263,
title = {Preface to the Focus Section on Machine Learning in Seismology},
author = {Bergen, Karianne J. and Chen, Ting and Li, Zefeng},
abstractNote = {Machine learning (ML) is a collection of algorithms and statistical models that enable computers to extract relevant patterns and information from large data sets. Unlike physical modeling approaches, in which scientists develop theories based on physical laws to compare with real–world observations, ML approaches learn directly from data without explicitly reasoning about the underlying physical mechanisms. ML algorithms are often categorized into supervised and unsupervised learning (see fig. 2 in Kong et al., 2018). Supervised learning algorithms build a model from existing labeled input data with the goal of predicting the labels of new data.},
doi = {10.1785/0220190018},
journal = {Seismological Research Letters},
number = 2A,
volume = 90,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
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This content will become publicly available on February 13, 2020
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