Multi-scale dual-path attention network for seismic background noise attenuation.

ABSTRACT: The background noise in seismic records severely interferes with the extraction of effective reflection events, particularly in complex exploration environments such as deserts. The non-Gaussian and nonlinear characteristics of background noise further exacerbate the difficulty of noise reduction, impacting the accuracy of subsequent processing such as inversion and migration. In recent years, deep learning has demonstrated excellent performance in the suppression of complex seismic noise, exhibiting notable advantages over traditional denoising algorithms. However, traditional deep learning networks often focus solely on feature extraction at a single scale, which proves inadequate when handling complex and variable seismic data. To address the aforementioned problem, we propose a Multi-s

SUBMITTER: Han L 

PROVIDER: S-EPMC12645041 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature