Title:  Adaptive Fusion of Information to Exploit Natural Stimuli for “CAT scanning” Groundwater Basins

Speaker:  Tian-Chyi Jim Yeh, Professor
Affiliation:  Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ 85721.
Email: yeh@hwr.arizona.edu.

Abstract

	Information about the Earth’s 3-D subsurface distributions of water and related properties is imperative for our 
understanding and management of water resources. Existing monitoring and characterization technologies can cover 
only a small fraction of the subsurface, and they cannot be used to reliably evaluate current and future water-availability issues. 
Therefore, we are taking on the challenge of developing a dynamic data driven system for subsurface characterization 
at the basin scale, the appropriate unit for water resources management issues. This basin-scale characterization aims to 
provide detailed knowledge of the variability and characteristics of geologic formations at scales from meters to kilometers 
throughout the basin.

Inverse modeling is a necessary tool for characterizing hydrologic properties of geologic media.  
Combined with geologic data, and hydrologic or geophysical tomographic surveys, inverse modeling has
become an information fusion technology that has proven to be a viable, high-resolution subsurface characterization 
tool for small-scale field problems.   “Seeing” into a basin however requires significant scaling up and integration of this 
current high-resolution tool. This task demands innovative approaches of data collection and analysis and unprecedented 
levels of computation and information processing.  Besides, current methods relying on locally induced artificial stimuli 
(e.g., pumping at wells, or ground penetrating radar, electromagnetic surveys) with a limited area of influence are economically 
and physically prohibitive to provide a dense coverage over a basin-size region. To overcome these impediments, we are 
investigating the possibility of exploiting naturally recurring stimuli (e.g., lightning, storms, floods, volcanic activities, 
landslides, earthquakes, precipitations, etc.) as energy sources for basin-scale hydraulic or geophysical tomographic surveys.  
These surveys will be dynamically driven by information obtained from satellites and smart ground sensors, and they are 
integrated with results from networked simulations and stochastic information fusion algorithms to continually adjust 
sampling strategies, and to reinterpret subsurface responses to these stimuli to reduce uncertainty and to increase resolution 
in the evolving basin characterization. This vision for basin-scale subsurface characterization faces many significant 
technological challenges and requires interdisciplinary collaborations (e.g., from surface and subsurface hydrology, 
geophysics, geology, geobiochemistry, environmental engineering, information and sensor technology, applied 
mathematics, physics, atmospheric science.).  We nevertheless contend that this should be a future direction for subsurface 
science as well as environmental engineering research.