SM4.2

Geophysical imaging techniques such as seismic, (complex) electrical resistivity, electromagnetic, and ground-penetrating radar methods are widely used to characterize structures and processes in the shallow subsurface. Advances in experimental design, instrumentation, data acquisition, data processing, numerical modeling, and inversion constantly push the limits of spatial and temporal resolution. Despite these advances, the interpretation of geophysical images often remains ambiguous. Persistent challenges addressed in this session include optimal data acquisition strategies, (automated) data processing and error quantification, appropriate spatial and temporal regularization of model parameters, integration of non-geophysical measurements and geological realism into the imaging process, joint inversion, as well as the quantitative interpretation of tomograms through suitable petrophysical relations.

In light of these topics, we invite submissions concerning a broad spectrum of near-surface geophysical imaging methods and applications at different spatial and temporal scales. Novel developments in the combination of complementary measurement methods, machine learning, and process-monitoring applications are particularly welcome.

Invited speaker: Andreas Fichtner (ETH Zurich)

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Convener: Florian WagnerECSECS | Co-conveners: Adam Booth, Andreas Kemna, Anja KlotzscheECSECS, Frédéric Nguyen
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| Attendance Wed, 06 May, 08:30–12:30 (CEST)

Geophysical imaging techniques such as seismic, (complex) electrical resistivity, electromagnetic, and ground-penetrating radar methods are widely used to characterize structures and processes in the shallow subsurface. Advances in experimental design, instrumentation, data acquisition, data processing, numerical modeling, and inversion constantly push the limits of spatial and temporal resolution. Despite these advances, the interpretation of geophysical images often remains ambiguous. Persistent challenges addressed in this session include optimal data acquisition strategies, (automated) data processing and error quantification, appropriate spatial and temporal regularization of model parameters, integration of non-geophysical measurements and geological realism into the imaging process, joint inversion, as well as the quantitative interpretation of tomograms through suitable petrophysical relations.

In light of these topics, we invite submissions concerning a broad spectrum of near-surface geophysical imaging methods and applications at different spatial and temporal scales. Novel developments in the combination of complementary measurement methods, machine learning, and process-monitoring applications are particularly welcome.

Invited speaker: Andreas Fichtner (ETH Zurich)

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