Our focus is not just to improve Earth imaging and inversion algorithms and fine-tune them, though we will end up doing so for cleaner more efficient results, but, as academicians, our real goal is big ideas that will help us better discover and monitor the Earth using seismic (and other) data, and those ideas may include utilizing artificial intelligence. In our group, such ideas fall into the following focus categories:

Recent advances in FWI

Our experience in developing solutions in waveform inversion led to many interesting results. From the optimal transport matching filter (in action below on the Chevron blind dataset) to automatic waveform inversion based salt flooding (a movie on revolving the salt body iterative as part of FWI) and much more, we have, as a group learned the possibilities in seismic data waveform inversion and many of the challenges. With our recent endeavor in machine learning algorithms (since 2016), we managed to carve out innovate applications of machine learning in waveform inversion including the ML-misfit, the ML-decent, the ML-prior, and much more.

A new paradigm using the optimal transport matching filter in FWI.

The case of automatic waveform inversion based salt flooding.