Time-Lapse Cross-Equalization by Deep Learning

Abstract

In salt-affected regions, conventional full-waveform inversion (FWI) is doomed to fail if there is no prior information of the salt body. Recent studies suggested regularizing the inversion by implementing an automatic flooding using total variation (TV) and Hinge loss functions. We generalize this approach and introduce a family of functions known as activation functions in the machine learning discipline that can be used to implement automatic flooding in a similar way. In particular, we investigate the automatic flooding using a sigmoid, tanh and exponential linear unit (Elu) functions and apply them for salt body reconstruction on the BP model and report their performance.