Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network

作者：Tianwei Lan, Zhaofa Zeng, Liguo Han, Jingwen Zeng · 发表于：Applied Sciences · 年份：2023 · DOI：10.3390/app13010655 · 被引用次数：11 · 研究领域：Seismic Imaging and Inversion Techniques、Image and Signal Denoising Methods、Seismology and Earthquake Studies

The neural network denoising technique has achieved impressive results by being able to automatically learn the effective signal from the data without any assumptions. However, it has been found experimentally that the performance of the method using neural networks gradually decreases with increasing pollution levels when processing contaminated seismic data, and how to improve the performance will become the direction of further development of the method. As a traditional method widely used for tainted seismic data, the wavelet transform can effectively separate the signal from the noise. Thus, we propose a method combining wavelet transform and a residual neural network that achieves good results in suppressing random noise data.