ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Solid Earth Geophysics
This article is part of the Research TopicArtificial Intelligence Computational Methods for Geophysical Subsurface Imaging and MonitoringView all articles
WTTnet: A network combining wavelet transform and transformer for denoising microseismic signal
Provisionally accepted
- Jilin University, Changchun, China
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Noise suppression is a key component in microseismic monitoring technology. Accurate denoising of microseismic signals is crucial for ensuring reliable data for locating mining-related seismic events and analyzing the state of rock mass during mining operations. This paper proposes a network combining wavelet transform and Transformer for denoising microseismic signals (WTTnet). WTTnet leverages discrete wavelet transform (DWT) to separate the high-and low-frequency components of the input signal. These components are concatenated to form full-frequency features, which are then used as query and value vectors in the Transformer, while the high-frequency features serve as keys. The multi-head self-attention mechanism captures cross-scale correlations. Finally, inverse discrete wavelet transform (IDWT) converts the frequency-domain output back to the time domain. The primary strength of this model is its ability to identify and distinguish noise components across varying frequencies. The proposed method is tested on synthetic data contaminated with various noise types and on field data. Its denoising performance is evaluated using appropriate metrics and compared with other denoising methods. Experimental results show that this method outperforms traditional denoising methods in terms of overall denoising performance across diverse noise conditions.
Keywords: deep learning, denoising, seismic, Signal processing, transformer
Received: 18 Jan 2026; Accepted: 09 Feb 2026.
Copyright: © 2026 Sun, Yu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junqiu Wang
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