Miguel Bosch discusses his Honorary Lecture, "The new paradigms in seismic inversion."
Miguel explains how elastic Full Waveform Inversion and the Markov Chain Monte Carlo approach improve seismic inversion, discusses if data analysis and machine learning are essential to practice inversion, and highlights new tools that will improve the accuracy of inversion. This conversation provides great value and insight into the essential work of inversion.
RELATED LINKS
* Watch Miguel's course: The New Paradigms in Seismic Inversion (https://www.knowledgette.com/p/the-new-paradigms-in-seismic-inversion)
* Discover SEG on Demand (https://seg.org/Education/SEG-on-Demand)
* The SEG podcast archive (https://seg.org/podcast)
BIOGRAPHY
Miguel Bosch’s expertise is in the field of geophysical inversion with a focus on advanced seismic inversion methods and data integration in complex reservoir models. He has worked on inference problems at different earth scales. In the topic of oil and gas reservoir description, he develops services and technology for the upstream oil and gas industry. Miguel has supervised a large number of projects on seismic inversion, reservoir characterization, and integration, and developed advanced technology and software for these fields. His recent research involves focused Full Waveform Inversion and quantitative Knowledge Networks for data integration. He graduated with a Ph.D. in Geophysics from the Institut de Physique du Globe de Paris, working with Albert Tarantola, and was a full professor and Head of the Applied Physics Department at the Universidad Central de Venezuela. He is an active member of the SEG, AGU, EAGE, IAMG, AAPG, GSH and serves as associate editor in the area of reservoir geophysics for the journal GEOPHYSICS. He is presently the founder and CEO of Info Geosciences Technology and Services.
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CREDITS
Original music by Zach Bridges.
This episode was hosted, edited, and produced by Andrew Geary at 51 features, LLC. Thank you to the SEG podcast team: Ted Bakamjian, Kathy Gamble, and Ally McGinnis.
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