DCAF-Net: An Intelligent Denoising Method for Microseismic Signals Based on the Combination of Time and Wavelet Domains

Microseismic data are typically characterized by a low signal-to-noise ratio (SNR), which makes their processing challenging. Therefore, increasing the SNR value of microseismic data by attenuating noise is a critical step in data processing. Traditional denoising methods mostly rely on prior knowledge and have low efficiency in large-scale data scenarios. Recently, intelligent denoising methods have become a focal point in the field of microseismic denoising. However, the existing deep learning methods commonly focus on either the time or frequency domain, thus failing to adequately leverage the time-frequency information in the process of microseismic signal separation from noise. To address this shortcoming, this study proposes a dual cross-attention fusion network named DCAF-Net, which is designed by combining the time and wavelet domains. The proposed DCAF-Net replaces the pooling layers used for downsampling with a discrete wavelet transform, capturing frequency information from the decomposition coefficients and enhancing a microseismic signal’s ability to represent details across multiple scales. In addition, a spatial-transformer attention block based on the Transformer encoding structure is designed to improve the feature extraction capacity of microseismic signals prior to the skip connections. Further, an adaptive attention fusion block is introduced to the proposed network to enhance the fusion of microseismic data at different scales, thus effectively achieving signal-to-noise separation. The proposed model is verified and compared with the representative methods, such as bandpass filtering, wavelet transform, the DnCNN model, and the U-Net model. The results indicate that at a low SNR value, the proposed DCAF-Net model can improve the SNR by an average of 52.46% compared to the U-Net model. Meanwhile, the proposed approach is compared with two representative frequency-domain-based deep learning denoising methods, namely the DeepDenoiser and DeepSeg methods. The results of extensive experiments conducted on both synthetic and field microseismic data demonstrate that the proposed DCAF-Net model can enhance the amplitude preservation and noise suppression capabilities of microseismic signals compared to the existing models, ensuring optimal performance.