2-D NONLINEAR JOINT INVERSION OF VLF AND VLF-R DATA USING SIMULATED ANNEALING

Two-dimensional (2-D) individual and joint inversions of very low freq uency (VLF) data (the real and imaginary anomalies), and jointly with VLF-R data (the apparent resistivity and phase) using very fast simula ted annealing (VFSA) as a tool in global inversion are considered in t his paper. Synthetic responses from two different models, which repres ent typical subsurface structures in the shield areas, were studied. V arious models achieved after 10 VFSA runs were used to compute the mea n model and the corresponding covariance and correlation matrices whic h were then used to estimate the uncertainties in the mean model param eters and correlations between the model parameters. An individual inv ersion of either real or imaginary anomalies cannot yield a model that would fit both observations. However, the joint inversion of the real and imaginary data sets will yield a model, which fits both observati ons. The models obtained either by the individual or by joint inversio n of VLF data do not show good agreement with the VLF-R observations. Generally, the models obtained from VLF inversions show better agreeme nt with the apparent resistivity than with the phase data. Conversely, the joint inversion of the apparent resistivity and phase data yields models which give good agreement with the real and imaginary anomalie s. The results obtained after the joint inversion of all the VLF and V LF-R data give good agreement with all the data sets. Individual inver sion of any response cannot yield a reliable subsurface structure no m atter how well a particular observed response is fitted by the compute d responses. Field data from three profiles traversing different geolo gical structures were also inverted to demonstrate the efficacy of the approach for delineating the geometry and properties of 2-D structure s. The VLF-R inversions invariably give superior results in comparison with the VLF inversions. However, the most reliable models are obtain ed from the joint inversion of both VLF and VLF-R data sets. (C) 1998 Elsevier Science B.V. All rights reserved.