Geophysical imaging techniques are widely used to characterize and monitor structures and processes in the shallow subsurface. Active methods include seismic, electrical resistivity, induced polarization, electromagnetic, and ground-penetrating radar, whereas passive approaches draw on ambient noise and electrical self-potential measurements. Advances in experimental design, instrumentation, acquisition, processing, numerical modelling, open hardware and software, and inversion continue to push the limits of spatial and temporal resolution. Nonetheless, the interpretation of geophysical images often remains ambiguous. Challenges addressed in this session include optimal acquisition strategies, automated processing and associated error quantification, spatial and temporal regularization of model parameters, integration of non-geophysical data and geological/process realism into imaging workflows, joint inversion, as well as quantitative interpretation through suitable petrophysical relations, and uncertainty quantification throughout the workflow.

We invite submissions spanning the full spectrum of near-surface geophysical imaging, from methodological innovation to diverse applications at different scales. Contributions on combining complementary methods, machine learning, and process monitoring are particularly encouraged.