Maarten de Hoop
Rice University
Earth Inside Out
Spectacular advances in seismic data acquisition by dense, areal arrays driven by the need for new information and converging interests in academic and private sector applications have been producing fast growing volumes of (broadband) waveform data. Moreover, new sensors are being developed, including ones based on ring-laser technology that measure rotational motions, generating new types of data. These developments, in combination with GPS and new InSAR data, are well aligned with the mathematical analysis of seismology- and geodesy-based inverse problems. I will present the dramatic progress that has been made in this analysis enabling new views of Earth’s interior through extreme-scale computing capabilities and providing exciting opportunities for advancing the knowledge of planetary dynamics, chemistry and mineral physics. Inverse problems are at the intersection of different areas of mathematics reflected by different features in the (continuous) recordings that represent hierarchical data complexity associated with different scattering regimes and scales, while honoring real-earth material (mechanical) behaviors. Inverse spectral, geometric inverse and dynamic/static inverse boundary value problems and their interconnections with the inverse source problems pertaining to unknown faults, dislocations, ruptures and microseismicity — founded on (non)linear elastodynamics, self-gravitation and earthquake dynamics — come into play while reaching into emerging relations between these and deep learning.
Maarten de Hoop is the Simons Chair in Computational and Applied Mathematics and Earth Science at Rice University. He is also a continuing visiting faculty member in the Department of Earth, Atmospheric and Planetary Sciences at the Massachusetts Institute of Technology and at the Graduate University of the Chinese Academy of Sciences. De Hoop received his Ph.D. from Delft University of Technology, the Netherlands. His research interests are in inverse problems, microlocal analysis and large-scale computation as well as in seismology and geodesy. He is well-known for his work in imaging revealing Earth’s interior, including studies of Hawaii and phase transitions in the mantle down to the core. Most recently, his research has branched out into connections between inverse problems and deep learning. As the Simons Chair, De Hoop has continued to work to promote interaction between mathematicians and geoscientists, and collaboration among academia and industry.