Rapid inversion of data from 2D resistivity surveys with electrode displacements
M.H. Loke, P.B. Wilkinson, J.E. Chambers and P.I. Meldrum
Journal name: Geophysical Prospecting
Issue: Vol 66, No 3, March 2018 pp. 579 - 594
Info: Article, PDF ( 4.29Mb )
Resistivity monitoring surveys are used to detect temporal changes in the subsurface using repeated measurements over the same site. The positions of the electrodes are typically measured at the start of the survey program and possibly at occasional later times. In areas with unstable ground, such as landslide-prone slopes, the positions of the electrodes can be displaced by ground movements. If this occurs at times when the positions of the electrodes are not directly measured, they have to be estimated. This can be done by interpolation or, as in recent developments, from the resistivity data using new inverse methods. The smoothness-constrained least squares optimisation method can be modified to include the electrode positions as additional unknown parameters. The Jacobian matrices with the sensitivity of the apparent resistivity measurements to changes in the electrode positions are then required by the optimisation method. In this paper, a fast adjoint-equation method is used to calculate the Jacobian matrices required by the least squares method to reduce the calculation time. In areas with large near-surface resistivity contrasts, the inversion routine sometimes cannot accurately distinguish between electrode displacements and subsurface resistivity variations. To overcome this problem, the model for the initial time-lapse dataset (with accurately known electrode positions) is used as the starting model for the inversion of the later-time dataset. This greatly improves the accuracy of the estimated electrode positions compared to the use of a homogeneous halfspace starting model. In areas where the movement of the electrodes is expected to occur in a fixed direction, the method of transformations can be used to include this information as an additional constraint in the optimisation routine.