LAUSR

Distributed acoustic sensing (DAS) is a new downhole vertical seismic profile (VSP) acquisition technology, which has many advantages of low cost, sensitive signal capture capability, and high spatial–temporal resolution. It can provide dense wavefield information for subsequent processing. Although DAS has obvious advantages over geophones, some weakness may limit its application. The main challenge is that DAS data are polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. The noise brings great difficulties to the interpretation of seismic data. In order to suppress the noise and recover the buried weak effective signals, we design a new sparse parallel-subnet network (SPSNet) in this letter. It includes a parallel-subnet feature extraction module with sparse mechanism, simultaneously extracting global and local dual features. In this way, we can extract as much detailed information as possible from DAS seismic data. Then, the following enhancement module fuses these features for signals complement. Another outstanding advantage is its high efficiency owing to the parallel structure. Compared with the complex networks with the same noise suppression effect, SPSNet has higher work efficiency. We generate a large number of geologic structure models with different parameters to optimize SPSNet. The denoising results show that the proposed method can effectively suppress a variety of noise in DAS seismic data, and the deep layer signals with weak energy are also well recovered.