DAS seismic signal recovery with non-uniform noise based on high-low level feature fusion model

Distributed Acoustic Sensing (DAS) is an effective exploration technology for acquiring Vertical Seismic Profile (VSP) data due to its characteristics of high-density collection and strong environmental adaptability. However, DAS-VSP is susceptible to various noises that distribute non-uniformly in both t-x and frequency domains. Existing denoising methods generally adopt single feature-extraction mechanisms (e.g. local convolutional operation or long-distance attention calculation), which are not sufficient for non-uniform feature extraction. Therefore, leveraging the advantages of Convolution (Conv) and Transformer, we propose a high-low level feature fusion model for DAS signal recovery. This model comprises three modules: low-level feature extraction (LFE), high-level feature extraction (HFE), and signal recovery (SR). First, LFE utilizes a Conv layer to extract the basic features, including energy, attributes, and fuzzy contours. The Conv utilizes small kernels to fitter the effective signal feature and introduce spatial information for the following layers. Second, HFE is the core module of the network to extract rich high-level features, such as sharper waveform features and high-dimension representation features. HFE consists of the Swin-Transformer blocks and the Conv blocks. The Swin-Transformer blocks utilize cross-window attention to extract the features between the windows and shift the window to continue recognizing the global features. Then, the Conv blocks further filter and enhance the high-attention features. The cross-use of these two blocks realizes the extract-enhance-extract-enhance process. Finally, the SR module employs a residual connection to create a direct mapping to add the low-level features to the last layer, achieving the fusion of the low-level and high-level features. Through the fusion, more complete and detailed features can be used to improve the accuracy of the recovering weak signals. The design of our model can combine long-distance and local detailed information to extract rich high-low level features, facilitating the recognition of weak signals and non-uniform noise in complex geological structures.