High-Accuracy Fault Identification Using U-Shape Transformer With
Improved Window Mechanism
Abstract
In the field of geophysical exploration, fault identification is an
important part. However, Due to the fixed identification mode,
conventional computing methods cannot adapt to complex fault
characteristics and environmental noise; And, the convolution neural
network is prone to loss of global information because the slow
convergence of global information. For those reasons, the previous fault
identification methods cannot achieve higher resolution and accuracy
fault identification, such as the identification results are very
coarse, the identification results are inaccurate and results are
discontinuous, especially in the case of severe environmental noise and
complex fault interlacing. So in order to solve these problems, we
proposed a deep learning network based on Transformer with improved
window mechanism. Starting from the features of the data itself, quickly
gather global information, and results on field data show that our
network effectively solve the above problems. This work has positive
significance for high-precision exploration work.