Cosine Spectral Association Network for DAS VSP Data High-Precision Recovery

In recent years, distributed acoustic sensing (DAS) technology has attracted much attention and has been applied to vertical seismic profile (VSP). It has promoted the development of land exploration toward deep exploration and fine tectonic analysis. However, the acquired DAS VSP data are often disturbed by various types of noise generated by the environment and instruments. They exhibit quite different characteristics from traditional exploration noise, hindering the further development and application of DAS technology in the field of VSP. Frequency information plays an important role in the judgment and reduction of seismic noise. Since the noise and signals in DAS VSP data often have complex time-frequency relationships, it is difficult for traditional frequency-based methods to effectively separate them. Current deep learning DAS data processing methods did not consider the important frequency-domain features and affect the integrity of data feature extraction. This article presents a new deep learning algorithm for DAS VSP data processing, cosine spectral association network (CSANet). The novel algorithm considers both the original time-offset and frequency information to achieve better DAS weak signal recognition and recovery. Moreover, a feature fusion module is designed to make sure the entire network to better utilize the composite information. The experimental results show that our CSANet can obtain high-precision DAS VSP weak signal recognition and recovery results with strong practical application value.