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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/A6972116-A18A-4A70-B652-AF818CEFE75A | ||
| Year | 2022 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | S. De Iaco, D. T. Hristopulos and G. Lin, “Special issue: geostatistics and machine learning,” Math. Geosci., vol. 54, no. 3, pp. 459–465, Apr. 2022, doi: 10.1007/s11004-022-09998-6. https://doi.org/10.1007/s11004-022-09998-6 | ||
| Appears in Collections |
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.
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