Abstract:
Due to the precision of the first break, time-variant wavelet, and strata dip angle, the popular iterative vertical seismic profiling (VSP) wavefield separation method ma...Show MoreMetadata
Abstract:
Due to the precision of the first break, time-variant wavelet, and strata dip angle, the popular iterative vertical seismic profiling (VSP) wavefield separation method may not yield high-precision wavefield separation results. The single-stage multitask U-Network (SUN) VSP wavefield separation method can avoid the impact of the first break, time-variant wavelet, and the strata dip angle, but it faces challenge in complex VSP wavefield due to its network performance. In this letter, based on the iterative VSP wavefield separation method (ISM), the U-Network and multitask deep learning, we propose a two-stage multitask U-Network VSP wavefield separation method (TSM). The TSM comprises the two-stage multitask U-Network (TUN), the loss function, and the synthetic VSP training data automatic generation (STG). The TUN aims to simultaneously output high-precision downgoing and upgoing wavefield, as well as the residual wavefield, while the STG aims to automatically generate numerous and various VSP training data. Applications of both synthetic and actual VSP data demonstrate that the TSM can be widely used for high-precision VSP wavefield separation.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)