“Machine learning over the last few years has become revolutionary. But we still need, and maybe even more so, the constraint and knowledge of rock physics and QI as an integral part of those workflows.”
Stephan Gelinsky describes quantitative interpretation as the place “where science meets business impact.” That idea anchors this conversation with Stephan and Per Avseth, built around the May quantitative interpretation special section of The Leading Edge.
Elastic FWI and machine learning are moving quickly into QI, but both guests argue that better algorithms make rock physics, geology, and judgment more important, not less.
Per also points to a broader challenge. Some students are turning away from reservoir geophysics because they associate it with petroleum, even though the same science is needed for CCS, geothermal, critical minerals, natural hydrogen, and other subsurface problems tied to the energy transition.
The question is not whether QI still matters. It is how the field keeps its physics, communicates uncertainty, and shows the next generation where that science can take them.
Read May's The Leading Edge special section at https://pubs.geoscienceworld.org/tle/issue/45/5.
QUESTIONS THIS CONVERSATION ANSWERS
> How are elastic FWI and machine learning changing the role of QI specialists?
> What makes a QI result trustworthy enough to support a real decision?
> Why does QI remain foundational to CCS, geothermal, critical minerals, and other energy-transition problems?
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Seismic Soundoff showcases conversations addressing the challenges of energy, water, and climate. Produced by the Society of Exploration Geophysicists (SEG) and hosted by Andrew Geary of 51 features, these episodes celebrate and inspire the geophysicists of today and tomorrow. Three new episodes monthly. See the full archive at https://seg.org/resources/podcast/.