We have developed an inversion algorithm that finds smooth, three-dimensional models of the earth’s electrical<br>resistivity from d.c. pole-pole or dipole-dipole potential field data. The algorithm, based on Tikhonov’s<br>regularization method, computes models that fit the data and have minimum structure in the sense that second<br>spatial derivatives of resistivity are minimized. A nonlinear conjugate gradient (NLCG) algorithm with<br>pre-conditioning is employed to solve the minimization problem involved. Numerical experiments with synthetic<br>and real data show that our version of the NLCG algorithm is more efficient than the commonly used<br>Gauss-Newton method. We demonstrate the inversion algorithm on a large dipole-dipole data set collected<br>at a groundwater contamination site in the Aberjona Watershed in Massachusetts. Our Aberjona inversion<br>model correlates well with other geophysical results at the site (GPR sections and cone penetrometer logs)<br>and serves to extrapolate the sparse stratigraphic information previously available into a full 3-D model of<br>the study area.


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