The combined use of hydrogeological, geophysical, and biogeochemical data has been identified as a promising approach to integrate information across scales and improve the predictive accuracy of subsurface models. These approaches can also aid in uncertainty assessment of subsurface parameters and enhance the monitoring, understanding, and control of subsurface processes. This session solicits research that integrates any combination of hydrogeological, geophysical, and biogeochemical data to improve the understanding, characterization, and monitoring of subsurface parameters and processes. Topics of interest include but are not limited to: innovative coupled or joint inversion strategies; multi-point geostatistical approaches; hydraulic or pneumatic tomography; physics-informed machine learning strategies, reinforcement learning approaches; data errors and uncertainty quantification methods; and proxy-modeling strategies.

• NS - Near Surface Geophysics