The workshop held in Boise, ID was a chance for HGI to demonstrate the latest technological developments in hardware acquisition and array optimization. From the abstract titled “Optimized Geoelectric Monitoring for 3D Arrays”:
Capturing the relevant spatiotemporal geoelectric detail of a hydrogeological event relies on fast acquisition hardware, adequate electrode coverage, and acquiring the best subset of data from the combination of electrical current and voltage potential electrode pairs. The definition of best subset, however, will likely be considered from a combination of important factors such as 1) signal strength, 2) depth of penetration, 3) the ability to sweep through the subset rapidly, and 4) the resolving capability of the subset. The pole-pole (or 2-pole) array offers the best advantage for the first three factors but is significantly hampered by its low resolving power. The bipole-bipole (or 4-pole) array has the best resolving capability but may suffer from long acquisition times based on the large number of possible combinations, as well as low signal strength. Even a small survey with 168 electrodes will have 14×103 2-pole non-reciprocal combinations, but an excessive 96×106 4-pole combinations. While we realize not all 4-pole combinations are viable, choosing the best 4-pole subset may be computationally daunting or difficult without knowing first the makeup of the subsurface. Additionally, 4-pole subsets created ad hoc by either maximizing signal strength (i.e., by collecting only combinations with low geometric factors) or through expert judgment may still have low resolving capabilities or worse.
We propose that the advantages of both 2-pole and 4-pole arrays can be combined to obtain an optimized subset for imaging dynamic events. The combined process would include the acquisition of a complete 2-pole dataset to ensure high quality data and the largest number of snapshots within a given monitoring period. Then, an optimized 4-pole subset would be calculated from the 2-pole dataset based on maximizing the resolution matrix using the Compare R method. The reconstruction of any 4-pole combination, including both voltage and measurement error, is conducted by superposition of four measurements within the 2-pole dataset. Furthermore, by calculating the optimized subset after data acquisition is complete, we can ensure that the resolution is maximized for the entire series of snapshots.