Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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From Differences to Inversion: A New Era for 4D Seismic
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Manage episode 496232699 series 1231780
Content provided by Seismic Soundoff and Society of Exploration Geophysicists (SEG). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Seismic Soundoff and Society of Exploration Geophysicists (SEG) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
“Repeatability is the biggest hurdle in time-lapse monitoring, but it’s also where the opportunity lies. 4D FWI can help address those repeatability challenges.” On this episode, Andrew Geary sits down with Madhav Vyas and Kris Innanen, guest editors for July’s The Leading Edge special section on 4D full-waveform inversion. They explain how 4D FWI goes beyond traditional differencing by fully inverting wavefields, making time-lapse seismic more robust against survey inconsistencies and complex overburden. Listeners will learn why now is the perfect time to adopt 4D FWI, the main technical hurdles around repeatability and uncertainty, and the first steps to take for reservoir monitoring and survey design. KEY TAKEAWAYS > 4D FWI inverts the full wavefield - reflections, refractions, diving waves, multiples - to detect reservoir changes, making it more robust than conventional 4D processing. > Repeatability of surveys and assessing uncertainty are the biggest challenges, but workflows like hypothesis testing, batch FWI, and null-space shuttling help mitigate inversion noise. > High-quality data and accurate physics (elasticity, attenuation, anisotropy) are essential; advances in GPU-driven computation enable faster iterations and richer uncertainty analysis. CALL TO ACTION Read July's The Leading Edge special section on 4D FWI at https://library.seg.org/toc/leedff/44/7, then explore an open-source FWI toolbox. LINKS * Visit https://seg.org/podcasts/episode-265-from-differences-to-inversion-a-new-era-for-4d-seismic for the complete show notes.
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271 episodes
MP3•Episode home
Manage episode 496232699 series 1231780
Content provided by Seismic Soundoff and Society of Exploration Geophysicists (SEG). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Seismic Soundoff and Society of Exploration Geophysicists (SEG) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
“Repeatability is the biggest hurdle in time-lapse monitoring, but it’s also where the opportunity lies. 4D FWI can help address those repeatability challenges.” On this episode, Andrew Geary sits down with Madhav Vyas and Kris Innanen, guest editors for July’s The Leading Edge special section on 4D full-waveform inversion. They explain how 4D FWI goes beyond traditional differencing by fully inverting wavefields, making time-lapse seismic more robust against survey inconsistencies and complex overburden. Listeners will learn why now is the perfect time to adopt 4D FWI, the main technical hurdles around repeatability and uncertainty, and the first steps to take for reservoir monitoring and survey design. KEY TAKEAWAYS > 4D FWI inverts the full wavefield - reflections, refractions, diving waves, multiples - to detect reservoir changes, making it more robust than conventional 4D processing. > Repeatability of surveys and assessing uncertainty are the biggest challenges, but workflows like hypothesis testing, batch FWI, and null-space shuttling help mitigate inversion noise. > High-quality data and accurate physics (elasticity, attenuation, anisotropy) are essential; advances in GPU-driven computation enable faster iterations and richer uncertainty analysis. CALL TO ACTION Read July's The Leading Edge special section on 4D FWI at https://library.seg.org/toc/leedff/44/7, then explore an open-source FWI toolbox. LINKS * Visit https://seg.org/podcasts/episode-265-from-differences-to-inversion-a-new-era-for-4d-seismic for the complete show notes.
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