Salt Body Flooding Using Activation Functions From Machine Learning

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 to regularize the inversion by implementing an automatic flooding using total variation (TV) and Hinge loss functions. We generlize 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 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.