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Abstract

Summary

This article is aimed at analysis the applicability of machine learning (ML) algorithms in predicting discontinuous violations based on potential field data. The text discusses common methods for identifying discontinuous violations and alternative methods based on machine logic using data from potential fields and their transformants. The advantages and disadvantages of algorithms in comparison with classical methods for determining discontinuous structures are also described. Using the example of one of the license areas (the East Siberian sea), we present a solution to the problem of fault mapping using machine prediction using ML and the “Reana” anomaly axis selection algorithm. In conclusion, we describe the features of working with the methods of automatic prediction of discontinuous violations, the relevance of further development of the application of this direction. It is shown that the algorithm is efficient and suitable for practical use.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202054005
2020-10-19
2024-04-16

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References

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Machine learning in predicting discontinuous violations
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202054005
/content/papers/10.3997/2214-4609.202054005
/content/papers/10.3997/2214-4609.202054005
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