Informed experimental design for electrical resistivity imaging

Abstract

Electrical resistivity imaging has been successfully used to monitor near‐surface hydrologic processes but use of standard measurement arrays may not provide the greatest data sensitivity to the imaged region. We present a method of experimental design based on the concept of informed imaging for creating an electrical resistivity imaging experiment to monitor flow beneath a recharge pond. Informed imaging is the integration of all available data about a site into the acquisition, inversion and interpretation of electrical resistivity data. Informed experimental design uses all available information to develop an a priori model of the subsurface conductivity structure that guides the selection of measurement arrays for an electrical resistivity imaging experiment given spatial and temporal constraints on the acquisition. Selection of arrays focuses on maximizing the amount of unique information acquired with each source pair. We apply the method to the selection of arrays for imaging the top 5 m of the subsurface beneath a recharge pond in Northern California, which is part of an aquifer storage and recovery project. Decreasing infiltration rates over time reduce the effectiveness of the recharge pond. We seek to monitor infiltration processes at the contact between a fines‐rich sand layer and coarser sand layer in an effort to understand the hydrologic controls on infiltration. The performance of the arrays selected using informed experimental design relative to two standard arrays (Wenner and dipole‐dipole) is validated on two synthetic subsurface conductivity models, which are representative of conductivity structures that may arise during an infiltration event. Performance is evaluated in terms of a singular value decomposition of the sensitivity matrix produced by the three types of arrays, as well as a measure of the region of investigation. Results demonstrate that arrays selected using informed experimental design provide independent information about the imaged region and are robust in the presence of noise, improving the ability to image changes in a conductivity structure that result from infiltration processes.

A new coupled model for simulating the mapping of dense nonaqueous phase liquids using electrical resistivity tomography

“…Thus, it is not surprising that the detailed distribution of pools characteristic of a complex DNAPL may be challenging to resolve. Several methods can be used to provide optimum subsurface information, including informed ERT survey design (e.g., Nenna et al, 2011), and future work will explore these techniques in the context of DNAPL investigations. However, overall, Figure 6 suggests that, at favorable sites, ERT may be suitable for determining the outer boundaries of the location and depth of a DNAPL Figure 6.…”

“…Moreover, this envelope of favorable conditions is likely dependent on the ERT survey design and processing methodology, neither of which have been optimized for DNAPL sites. It is possible that recent advances in ERT imaging (e.g., data acquisition and timelapse monitoring) may be beneficial in this context (e.g., Ogilvy et al, 2009;Karaoulis et al, 2011a;Nenna et al, 2011). The systematic, controlled studies required to explore these questions at the field scale are not available from field trials or laboratory studies.…”

Optimized arrays for 2D cross‐borehole electrical tomography surveys

“…There have been many significant developments in algorithms to automatically select arrays to maximize the resolution of the inversion model for linear surface arrays and cross-borehole surveys (Stummer et al 2004;Wilkinson et al 2006aWilkinson et al , 2006bLoke et al 2010a;Nenna et al 2011;Hagrey 2012). A non-linear method that calculates the model resolution (the 'Compare R' method) by Wilkinson et al (2006b) proved to be the best method (Loke et al 2010a).…”

Inversion of capacitively coupled resistivity (line-antenna) measurements

“…As a matter of fact, calculating the sensitivity in terms of homogeneous grounds is frequently used in practice to solve problems related to the DIC of resistivity arrays (e.g., Neukirch and Klitzsch, 2010;Nenna et al, 2011). For example, most of the algorithms for optimizing the survey design are based on sensitivity and current flow of homogenous ground (e.g., Furman et al, 2004;Stummer et al, 2004), and it has been found that the assumption of a homogeneous ground does not essentially limit the applicability of the optimized resistivity array (Stummer et al, 2004;Wilkinson et al, 2012).…”