The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Neural networks in geophysical applications
Abstract
Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.
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Cited by
Machine learning for data-driven discovery in solid Earth geoscience
Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019) · Science · doi:10.1126/science.aau0323 · Cited by 1,249
Araya-Polo, M., Jennings, J., Adler, A., & Dahlke, T. (2018) · The Leading Edge · doi:10.1190/tle37010058.1 · Cited by 558
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Metadata
- Authors
- van der Baan, M., & Jutten, C.
- Venue
- Geophysics
- Publisher
- Society of Exploration Geophysicists
- Published
- 2000-01-01
- Volume
- 65
- Issue
- 4
- Pages
- 1032-1047
- ISSN
- 0016-8033 (ISSN-L)
- 1942-2156
- OpenAlex
- W2113143455
- Cited by
- 342
- References
- 71
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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