Research – Electrical Resistivity Tomography

Stochastic Fusion of Information

View a brief introduction to ERT

My research group is currently exploring the power of the ‘Stochastic Fusion’ (Yeh and Simunek, 2002) of different types of data in variably saturated geologic media utilizing a newly developed Sequential Successive Linear Estimator (SSLE) (Vargas-Guzman and Yeh, 2002 and Yeh et. al, 2002) approach. The main point of the stochastic infusion process is to include as many types of different information in the inversion process simultaneously. The process can benefit from including both geophysical (i.e. ERT, GPR and other standard or emerging geophysical technologies) and hydrological data (ie. pumping and tracer test results) to iteratively solve anisotropic and heterogeneous problems. Using the SSLE to invert the results of hydraulic tomography (Yeh and Liu, 2000), coupled with any geophysical survey, can yield astonishing detail and heterogeneity, for relatively large areas, while providing a measure of the uncertainty in the estimate as well.

You can explore the results of this unique ERT inversionResults of ERT inversion process, only using measurements from surface resistivity survey. process (that inverts the resistivity solution in 3 dimensions - no pseudo-sections), for a medium-scale synthetic problem, through the sequence of four steps illustrated in the next section.

The figures to the left lead to animations illustrating the marked difference in the estimated resistivity field, for a Results of ERT inversion process, using both surface and downhole resistivity survey results. medium-scale site, when downhole resistivity survey data are added to the basic surface survey results. The addition of the downhole data reveals the presence of a large, high-resistivity zone near the base of the domain. The addition of different types of data can greatly enhance the resolution.

Stochastic Fusion of Information

3D ERT Inversion of Heap Leaching Data

ERT inversion in heap leachingThis figure illustrates some results from our powerful SSLE inversion method for estimating the 3D distribution of resistivity anomalies from resistivity data. These anomalies in resistivity can often be related to anomalies in water content or the concentration of dissolved species in the pore water. More details about this problem can be found on here (including the evolution of these distributions over time). These results were obtained directly from the measured voltages from a resistivity survey, without the use of apparent resistivity or pseudo sections.

Work done jointly with Water Management Consultants, Tucson AZ