Abstract
Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.
Highlights
In applied geophysics, subsurface images are derived from non-invasive geophysical measurements, for example, direct current (DC) resistivity or small-loop electromagnetic (EM)measurements at the earth’s surface
The objective of this paper is to apply the Kalman ensemble generator (KEG) for joint probabilistic inversion of DC resistivity and small-loop EM data. previously applied to the inversion of EM [12], yet, to the best of our knowledge, this is the first time that (1) the method is used for geophysical joint inversion and (2) that these types of data are jointly inverted in a probabilistic way
Joint inversion was performed for two synthetic data sets and one field data set, each consisting of direct current (DC) resistivity and frequency-domain electromagnetic (FDEM) data
Summary
Subsurface images are derived from non-invasive geophysical measurements, for example, direct current (DC) resistivity or small-loop electromagnetic (EM)measurements at the earth’s surface. Geophysical data acquired from such measurements can be translated into a subsurface image of the investigated physical property by inversion of the data. Geological units often differ in terms of their physical properties; for example, ore bodies can usually be distinguished from their surroundings by identifying the spatial distribution of electrical conductivities in the subsurface. For this reason, inverse images can be useful to geologists, for example, in identifying mineral and hydrocarbon resources, groundwater reservoirs, and geological structures. Even for data sets acquired in the most precise manner (no noise or systematic errors), only limited information about the subsurface can be inferred, usually being insufficient for resolving the non-uniqueness of the inverse problem
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