Denoising and wavefield separation method for DAS VSP via deep learning

Abstract

Distributed acoustic sensing (DAS) vertical seismic profiling (VSP) is, a new combined method of borehole seismic exploration, that has the advantages of low cost, high resistance to temperature and pressure, and high sampling rate. However, the collected data generally suffer from high noise, detracting from the potential high-precision imaging benefits and limiting the full application of the technique. The quality of wavefield separation is also crucial to VSP processing. To mitigate the various problems, a regularized residual channel attention network (RRCAN) is proposed for improved denoising and wavefield separation. Based on two prediction tasks, the networks are built (i.e., RRCAN_1 and RRCAN_2) by adding a regularization term to the loss function. To reduce the loss of effective signals during the denoising process, a wavefield separation method that includes secondary effective information extraction is adopted. The final separation results reflect the denoised and removed noise data, a total of 500 geological models are constructed using a random method and RRCAN_1 is trained with synthetic and field noise data. The conventional plane-wave destruction method is improved by using velocity information, and high-precision separation results for upgoing and downgoing waves are obtained for RRCAN_2 training. The test results obtained for the synthetic and field data show that the proposed network effectively suppresses DAS VSP noise while yielding excellent separation results for downgoing and upgoing waves. Therefore, denoising and wavefield separation methods based on RRCAN provide effective high-precision DAS VSP imaging.