A Universal Denoiser for DAS-VSP Data Based on Semantic Mask and Random Mask Training

Recent advancements in deep learning (DL) methods have demonstrated promising results in distributed acoustic sensing-vertical seismic profiling (DAS-VSP) denoising. However, the satisfying noise-attenuating performance requires the training dataset to be comprehensive of noise types, which is difficult to achieve in real-life scenarios. To tackle this problem, we propose a universal DL denoiser called triplet masking denoiser (Tri-MD) based on a triple different data masking scheme in training and interfering. In the training stage, Tri-MD applies random masking to the data to force the denoising model to perform the signal recovery task rather than simply separating the signal from the noise. The characteristic allows Tri-MD to transform the complex noise suppression and signal preservation tasks. Thus, the noise-attenuating performance of our method does not suffer from degradation due to the scarcity of noise types in the training set. Moreover, we introduce a subnetwork for segmentation to estimate the signal location and create a semantic mask of the seismic effective signal, which helps the network extract the signal features to supplement the original features lost during the random masking process and improve the ability to recover the signal. Furthermore, we propose a novel interfering paradigm that utilizes an attention mask to ensure the test procedure is consistent with the training to enhance the robustness of Tri-MD. Noise reduction experiments on synthetic and two field DAS-VSP data demonstrate that our method can achieve excellent noise-attenuating performance in tests even when training is done on training datasets with scarce noise variety, proving that the generalization ability of Tri-MD is satisfying.