Title: Learning-assisted empirical mode decomposition algorithm for pre-stack seismic wave impedance prediction

Authors: Shu-cheng Sun; Bo Tang; Xue-song Yan

Addresses: School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430078, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430078, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430078, China

Abstract: Among numerous oil and gas exploration technologies, pre-stack seismic inversion technology based on reservoir elastic parameter information that can reflect more stratigraphic characteristics has become a popular technology in seismic inversion due to its ability to improve the accuracy of exploration. This paper proposes a learning-assisted empirical mode decomposition (EMD) algorithm. This algorithm addresses the problems of over envelope and under envelope in EMD algorithms, using segmented cubic Hermite interpolation algorithm instead of cubic spline interpolation, and using feature scale extension to reduce endpoint influence. To eliminate invalid components, correlation coefficients are used to remove some invalid components and reduce modal aliasing. In order to verify the performance of the proposed algorithm, traditional data processing methods and improved methods were compared with other algorithms to verify the improvement of wave impedance parameter prediction accuracy of the proposed algorithm.

Keywords: pre-stack seismic inversion; elastic parameters; EMD; empirical mode decomposition; CNN; convolutional neural network.

DOI: 10.1504/IJCSM.2025.151298

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.4, pp.322 - 331

Received: 05 Aug 2024
Accepted: 01 Nov 2024
Published online: 22 Jan 2026 *

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