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:
-
- Harvard Univ., Cambridge, MA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- 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:
- Journal Article: 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. https://doi.org/10.1785/0220190018
Bergen, Karianne J., Chen, Ting, and Li, Zefeng. 2019.
"Preface to the Focus Section on Machine Learning in Seismology". United States. https://doi.org/10.1785/0220190018. https://www.osti.gov/servlets/purl/1511263.
@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},
url = {https://www.osti.gov/biblio/1511263},
journal = {Seismological Research Letters},
issn = {0895-0695},
number = 2A,
volume = 90,
place = {United States},
year = {Wed Feb 13 00:00:00 EST 2019},
month = {Wed Feb 13 00:00:00 EST 2019}
}
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