Special Issue: Geostatistics and Machine Learning
Abstract
Abstract Recent years have seen a steady growth in the number of papers that apply machine learning methods to problems in the earth sciences. Although they have different origins, machine learning and geostatistics share concepts and methods. For example, the kriging formalism can be cast in the machine learning framework of Gaussian process regression. Machine learning, with its focus on algorithms and ability to seek, identify, and exploit hidden structures in big data sets, is providing new tools for exploration and prediction in the earth sciences. Geostatistics, on the other hand, offers interpretable models of spatial (and spatiotemporal) dependence. This special issue on Geostatistics and Machine Learning aims to investigate applications of machine learning methods as well as hybrid approaches combining machine learning and geostatistics which advance our understanding and predictive ability of spatial processes.
- Publication Date:
- Research Org.:
- Purdue Univ., West Lafayette, IN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF); US Army Research Office (ARO)
- OSTI Identifier:
- 1855870
- Alternate Identifier(s):
- OSTI ID: 1976696
- Grant/Contract Number:
- Subcontract 382247; W911NF-15-1-0562; SC0021142; DMS-1555072, DMS-1736364; CMMI-1634832; CMMI-1560834; 382247
- Resource Type:
- Published Article
- Journal Name:
- Mathematical Geosciences
- Additional Journal Information:
- Journal Name: Mathematical Geosciences Journal Volume: 54 Journal Issue: 3; Journal ID: ISSN 1874-8961
- Publisher:
- Springer Science + Business Media
- Country of Publication:
- Netherlands
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; Geology; Mathematics; Geostatistics; Statistical learning; Machine learning; Spatial process; Gaussian process regression
Citation Formats
De Iaco, Sandra, Hristopulos, Dionissios T., and Lin, Guang. Special Issue: Geostatistics and Machine Learning. Netherlands: N. p., 2022.
Web. doi:10.1007/s11004-022-09998-6.
De Iaco, Sandra, Hristopulos, Dionissios T., & Lin, Guang. Special Issue: Geostatistics and Machine Learning. Netherlands. https://doi.org/10.1007/s11004-022-09998-6
De Iaco, Sandra, Hristopulos, Dionissios T., and Lin, Guang. Mon .
"Special Issue: Geostatistics and Machine Learning". Netherlands. https://doi.org/10.1007/s11004-022-09998-6.
@article{osti_1855870,
title = {Special Issue: Geostatistics and Machine Learning},
author = {De Iaco, Sandra and Hristopulos, Dionissios T. and Lin, Guang},
abstractNote = {Abstract Recent years have seen a steady growth in the number of papers that apply machine learning methods to problems in the earth sciences. Although they have different origins, machine learning and geostatistics share concepts and methods. For example, the kriging formalism can be cast in the machine learning framework of Gaussian process regression. Machine learning, with its focus on algorithms and ability to seek, identify, and exploit hidden structures in big data sets, is providing new tools for exploration and prediction in the earth sciences. Geostatistics, on the other hand, offers interpretable models of spatial (and spatiotemporal) dependence. This special issue on Geostatistics and Machine Learning aims to investigate applications of machine learning methods as well as hybrid approaches combining machine learning and geostatistics which advance our understanding and predictive ability of spatial processes.},
doi = {10.1007/s11004-022-09998-6},
journal = {Mathematical Geosciences},
number = 3,
volume = 54,
place = {Netherlands},
year = {Mon Mar 21 00:00:00 EDT 2022},
month = {Mon Mar 21 00:00:00 EDT 2022}
}
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