Multi-Scale Progressive Fusion Attention Network Based on Small Sample Training for DAS Noise Suppression

Distributed acoustic sensing (DAS), increasingly mature technology for signal acquisition, has been gradually applied in the field of environmental monitoring and seismic exploration. However, the usually strong noise in DAS data and the huge amplitude contrasts among direct waves, reflected waves, and converted waves significantly complicate subsequent data processing and interpretation, which would further limit the wide application and rapid development of DAS in the seismic exploration field. Taking the high-accuracy processing requirements of DAS data into consideration, we propose a multiscale progressive fusion attention network (MPFAN) with pyramidal structure trained by small samples, which explores the collaborative representation of complementary information between DAS noise and its multiscale versions in a multiscale direction to realize the fine modeling of DAS noise and then achieve the suppression of DAS noise. At each scale, MPFAN uses a recurrent calculation to explore the potential correlation of complex DAS noise and learn the global texture, and reassigns the weights of feature maps on each channel through an attention mechanism to focus on detailed information. At each hierarchy, the information flow converges from the bottom to the top layer of the network to finally achieve an accurate estimation of DAS noise. Experiments show that, after training on a forward DAS dataset, which is composed of synthetic noise-free DAS signals and some DAS noises from both synthetic and field data, MPFAN performs better in high degree than some classical methods—not only more noises are obviously suppressed but also more reflected signals are better preserved.