A Novel CEEMD-Based Multichannel Denoising Autoencoder for Noise Attenuation of Surface Microseismic Data

Chuang Guan; Lujia Zheng; Chuang Wang

doi:10.53941/ijndi.2025.100026

Abstract

Surface microseismic data (SMD) are usually presented as weak signals affected by strong interference. In this paper, with the purpose of obtaining available SMD, a deep learning framework combining with the complete ensemble empirical mode decomposition (CEEMD) and the multichannel denoising autoencoder (MDAE) is established to strengthen weak signals and suppress strong interference. First of all, a sort of EMD algorithm referred to as CEEMD is employed to decompose each trace of the SMD into the intrinsic mode functions (IMFs) so as to reduce the interference of random noise. Then, an MDAE algorithm is put forward to extract the effective and robust features of the IMFs, where a novel loss function without any label information is designed to achieve unsupervised noise attenuation in the real-world scenario. After that, the decomposed IMFs are reconstructed from high frequency to low frequency by using the extracted features such that the high-frequency part of the microseismic signals is retained effectively. Finally, the proposed CEEMD-MDAE model is applied to the noise attenuation in both synthetic and real-world SMD datasets. Experimental results demonstrate that the CEEMD-MDAE algorithm significantly improves the signal-to-noise ratio and outperforms some existing popular denoising algorithms.

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