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Possibilities of Seismic Data Preprocessing for Deep Neural Network Analysis

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Abstract

Algorithms for automated processing of seismic records are being constantly upgraded, and the tasks of data analysis are becoming more complex. Most algorithms require preliminary preparation of the data. This preprocessing is either very simple, such as frequency filtering, or highly sophisticated to extract specific properties of the signal. Adequate preprocessing can increase the efficiency of the further analysis by order and more. However, specialized preprocessing cannot be used for solving other tasks or with other postprocessing algorithms. We consider the solutions that do not result in the significant loss of information and that can be used for solving any tasks. The main goals of the preprocessing are to reduce the noise level, to remove the anthropogenic noise, and to reduce the dimensionality of the data. We assume that deep neural networks of certain architecture are used for further data processing; however, this does not preclude from the application of other algorithms. As the preprocessing of seismic data, in this paper we consider wavelet transform, autoencoder, and some other algorithms.

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Kislov, K.V., Gravirov, V.V. & Vinberg, F.E. Possibilities of Seismic Data Preprocessing for Deep Neural Network Analysis. Izv., Phys. Solid Earth 56, 133–144 (2020). https://doi.org/10.1134/S106935132001005X

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