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Abstract

Summary

This paper discusses the possibility of using neural networks to classify seismic data in order to increase the efficiency of data processing, reduce the time for a geophysicist to perform routine tasks and have a positive impact on the economic efficiency of the project. The result of using deep learning for the classification of seismograms in the presence of non-stationary man-made noise in space is presented. The approach made it possible to achieve high classification accuracy. As a result of the work, an important conclusion was made about the possibility of using this approach to search for man-made noise in seismic records.

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/content/papers/10.3997/2214-4609.202156018
2021-08-04
2024-03-29
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References

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