The Geo-Mathematical Imaging Group (GMIG) at Rice University has positioned itself as a leading research lab at the intersection of mathematics, geomechanics, and machine learning. The group’s work on earthquake dynamics, planetary exploration and mathematics has supported the discovery of new techniques in carbon sequestration and monitoring, geothermal energy, hydrogen storage, and forecasting. Under the direction of Maarten de Hoop, GMIG has pivoted from an initial interest in exploration seismology for hydrocarbons to focus on key elements of the academic and industrial effort to mitigate climate change.

GMIG spans the full range of (planetary) seismology, combining theoretical research with practical applications of algorithmic development and data processing. A foundation in mathematics, including harmonic analysis, differential geometry, algebraic geometry, and numerical linear algebra, has underpinned recent expansions into deep learning and seismic forecasting. GMIG’s various areas of interest are distinct but complementary, forming an integrated approach that has enabled breakthroughs that would be unobtainable within a narrower framework.

GMIG began publishing research in 2007, focusing primarily on seismic wave equations and imaging. By the mid 2010s, its research had expanded to include a wider range of topics and applications, most notably machine learning and inverse problems. This period also marked a shift in focus from fossil fuels to renewable energy, which continues to inform the direction of GMIG’s current projects.

Presently, GMIG takes an in-depth view of seismicity, flow, geomechanics, and inverse problems, paving the way for innovations in sustainable energy technology. Leveraging its location in Houston, Texas, the heart of America’s energy sector, GMIG receives financial backing from corporate partners as well as significant grant funding from the U.S. Department of Energy and the National Science Foundation. Industry leaders in energy and high-performance computing have joined a long list of academic collaborators in recognizing the importance of GMIG’s blended approach to research.

Some of GMIG’s successes include programs in microlocal analysis and seismic imaging, inverse boundary value problems for the Helmholtz equation, and inverse problems for the elastic wave equation. These legacy projects exemplify GMIG’s capacity for major industrial impact in carbon sequestration and beyond. At the 2018 Mathematics and Physical Sciences Annual Meeting of the Simons Foundation, Maarten de Hoop’s presentation Earth Inside Out detailed how GMIG’s new methods of seismic data acquisition have fueled advances in planetary dynamics, chemistry, and mineral physics. Improved seismic sensors have shed light on previously unseen views of Earth’s interior, unlocking data of equal interest in academia and the private sector.

Rice University’s engagement in energy transition research, including nuclear fusion, wind energy, and hydrogen fuel, has been bolstered by GMIG’s expertise in geophysics. Carbon sequestration and monitoring, and geothermal energy sources, hold significant promise for the future of sustainability. GMIG is deeply involved in research in these areas which has attracted attention from leaders in the energy transition.

With continued success developing technologies in EOR/EGR, carbon sequestration and monitoring, geothermal energy, deep learning, forecasting, uncertainty quantification, decision making, and data science, GMIG has found practical applications for its research in an increasingly wide range of fields. Rapid gains in the areas of machine learning, deep learning, and geoscience have made the lab a sought-after destination for work on earthquakes and planetary missions. Proceeding into the future of sustainability with broadening applications and influence, GMIG is well equipped to form new partnerships for high-impact research.

The Geo-Mathematical Imaging Group (GMIG) at Rice University is a highly respected source of research that synthesizes elements of mathematics, geomechanics, and machine learning. This interdisciplinary approach underpins an extensive body of published work that has found purchase in fields ranging from earthquake dynamics to planetary missions. Within the group’s broad range of interests and applications, key points of emphasis over the last three years include distributed acoustic sensing (DAS), deep learning, and inverse problems. GMIG has leveraged the advantages of its location amid Houston’s thriving energy sector, partnering with industry leaders to forge a path toward a sustainable future. Under the direction of de Hoop, GMIG has pivoted from an initial interest in exploration seismology for hydrocarbons to focus on carbon sequestration and its potential to mitigate climate change.