Three‐dimensional source inversion of self‐potential data

Abstract

[ 1 ] The self-potential (SP) method has long been used for av ariety of geophysical applications because of its ease of acquisition, but has suffered from difficulty in interpretation of the data. Self-potential signals result from as ource term that is coupled with the earth resistivity and appropriate boundary conditions. This work describes an inversion methodology for determining the self-potential sources from measured SP and resistivity data. The SP source inversion is al inear problem, though it is complicated by nonuniqueness that is common to potential-field problems. The linear operator is also poorly conditioned because of the limited set of measurements, which are often constrained to the earth'ssurface. Our approach utilizes model regularization that selects a class of solutions which fit the data with sources that are spatially compact. Large variations in sensitivity due to distance and resistivity structure throughout the model are addressed through the use of ascaling term derived from the Green'sfunctions that define the linear operator.Asignificant benefit of these methods is the resolution of targets at depth from surface measurements alone. This inversion technique is first illustrated with a simple synthetic data set. In as econd example we apply this approach to af ield data set taken from previously published literature and investigate the effects of different resistivity structure assumptions on the inversion results. The spatial distribution of sources provides useful information that can subsequently be interpreted in terms of physical processes that generate the SP data.

A geochemical and geophysical investigation of the hydrothermal complex of Masaya volcano, Nicaragua

“…Another significant phenomenon is the effect of heterogeneous ground resistivity on the electrical potential (Sailhac and Marquis, 2001;Saracco et al, 2004). Previous studies (e.g., Minsley et al, 2007) have shown that resistivity contrasts, when not spatially associated with water flow (i.e., electrokinetic effect), may in some instances be of significant importance. In this study, however, and based on previous work, the main resistivity contrasts are considered to be associated with the pathway of the main ground water flow (MacNeil et al, 2007;Mauri et al, 2010).…”

Particle swarm optimization applied to solving and appraising the streaming-potential inverse problem

“…Although the details of this experiment are not available, it is a useful data set to model because it has been studied by other workers ͑Revil et al, 2003;Darnet et al, 2004;Minsley et al, 2007͒. Figure 5 shows the SP profile collected over the pumping well and the water-table elevations measured in several monitoring wells ͑in-verse triangles͒.…”

“…Many publications show the potential to obtain pertinent hydraulic information on subsurface flows from analyzing SP data ͑Bog-oslovsky and Ogilvy, 1973;Birch, 1998;Sailhac and Marquis, 2001;Revil et al, 2003;Darnet et al, 2004;Minsley et al, 2007;Maineult et al, 2008͒. These studies interpret directly the streaming-potential anomalies in data space or invert the signals to determine a source model that predicts the data using the problem's underlying physics ͑forward problem͒.…”

The Self-Potential Anomaly Produced by a Subsurface Flow at the Contact of Two Horizontal Layers and Its Quantitative Interpretation

“…An alternative approach on the inversion problem is to fit the SP curve measured at ground surface by an automated trial and error process, assuming different distributions of electric current sources at a nonhomogeneous ground for each iteration, till a satisfactory fit is achieved [21,22]. Such a process may work well if the resistivity distribution of the ground is known a priori.…”