Doing tomography differently: building the imaging tools of tomorrow

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Imaging what is inaccessible to direct observation, based on elastic waves, is a major issue with a wide range of applications of high societal and economical impact. In this project we aim at drastically improving the resolution of seismic tomography to produce enhanced finely-resolved images in two domains with high societal and economical interests: Regional tomography at unprecedented resolution, and oil industry passive seismic imaging. We go beyond classical passive imaging approaches such as ambient noise tomography or receiver function migration, by performing high-frequency full waveform imaging of the shallow or deep Earth, to help investigate the deep roots of continental orogens or the extended fault sources of large earthquakes. To achieve this goal, we extend our imaging techniques to high frequencies, and derive data-driven simulation schemes and novel techniques for highly unstructured irregular problems in high-performance computing. One of the ground-breaking steps that we propose is to abandon typical approximations in wave propagation models, and to include the full contribution of shear waves. In recent preliminary but promising results we have shown that this can sometimes lead to a ten-fold resolution increase locally. To do so we will address two technological gaps jointly: developing improved hybrid calculation techniques, and using well designed and tuned high-performance data-driven computing approaches for unstructured and/or imbalanced problems. Their combined use is an innovative concept, which opens a new avenue of research in the two targeted applications. Our approach can be seen as complementary to adjoint inversion techniques, and offers an important advantage: By restricting the inversion to regional tomographic boxes, our tools will not require leadership-class machines. We thus design flexible and semi-automated inversion workflows with quality-control metrics, so that the community can use them in daily research on Tier-2 class machines. To achieve this goal, data-driven high-performance computing techniques are essential, in particular the orchestration, marshalling and coordination of the involved huge amounts of data. In summary, we bridge the gap between data analytics and high-performance computing.The expected scientific impact is high, because we propose a new paradigm in the field of imaging methods. It will bring these tomographic problems to physical resolutions that have never been accessible before. If successful, the proposal can also be a breakthrough with mid-term consequences on industrial applications because highly-accurate tomographic images are of crucial importance for exploration of energy resources e.g. in the oil industry. The expected societal impact is also high because we will help develop new capabilities to monitor populated areas to assess their safety and also study the source of large earthquakes.