Application of machine learning methods in modeling hydrolithospheric processes
One of the most urgent problems in the study and analysis of hydrolithospheric processes is the construction of verifiable mathematical and computer models that make it possible to predict the behavior of an object under various initial conditions and input influences. Recently, machine learning met...
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| Huvudupphovsmän: | , , , |
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| Materialtyp: | Статья |
| Språk: | English |
| Publicerad: |
Springer Science and Business Media Deutschland GmbH
2021
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| Ämnen: | |
| Länkar: | https://dspace.ncfu.ru/handle/20.500.12258/16027 |
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| Sammanfattning: | One of the most urgent problems in the study and analysis of hydrolithospheric processes is the construction of verifiable mathematical and computer models that make it possible to predict the behavior of an object under various initial conditions and input influences. Recently, machine learning methods have been increasingly used in geological research. This paper discusses machine learning methods used in geological exploration to automate data analysis, as well as used for neural network information modeling of geological objects |
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