Podcast Tag: FWI

Episode 224: The Physics Behind Land Seismic Noise (Christof Stork)

“With ​every ​new ​acquisition, ​spend ​2% ​of ​your ​budget ​to ​do ​research ​to ​understand the ​noise ​and ​signal ​drivers ​in ​your ​area. ​It ​might ​help ​any ​future ​neighboring ​acquisition, ​or ​it ​might ​just ​help ​the ​field ​in ​general, ​​which ​you ​and ​your ​career ​will ​benefit ​from.”

Dr. Christof Stork delves into the intricate world of land seismic noise. Despite the advancements in marine processing and pre-stack depth migration, land seismic methods have yet to see comparable progress. He emphasizes the need for more attention and funding in this area, highlighting how crucial it is for improving seismic data quality. Listeners will better understand why land seismic noise remains a complex problem and the potential pathways to progress.

Episode 190: The unique power of combining GPR and FWI

Anja Klotzsche discusses her Near-Surface Global Lecture, “Unlocking the potential of GPR for subsurface characterization by using full-waveform inversion.” Anja describes the recent developments in FWI that have impacted how to apply GPR. She outlines a few of her favorite GPR applications, the impact of AI on GPR, and her lightbulb moment when she realized her method was special. This episode will challenge you to consider GPR in a new way and, in so doing, put FWI in a new perspective as well.

Episode 155: Removing the starting model for FWI

Arnab Dhara discusses his paper, "Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion," in the June issue of The Leading Edge. Arnab proposes employing deep learning as a regularization in full-waveform inversion. He explains why physics-based solutions with machine learning are challenging to develop, how he made it possible to train the network without known answers, and why he tested his approach with the Marmousi and SEAM models. Arnab also shares why this research took over 20 years to build on the initial idea and how he used full-waveform inversion without a starting model. This is a cutting-edge conversation that may represent the future of FWI.

Episode 12: Full-waveform inversion

Dr. Michal Malinowski, special section lead editor for Interpretation and Jyoti Behura, special section lead editor for The Leading Edge join Andrew Geary to discuss full-waveform inversion.

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