The inversion of data from very large three‐dimensional electrical resistivity tomography mobile surveys

Loke, M.H.; Papadopoulos, N.; Wilkinson, P.B.; Oikonomou, D.; Simyrdanis, K.; Rucker, D.F.. 2020 The inversion of data from very large three‐dimensional electrical resistivity tomography mobile surveys. Geophysical Prospecting, 68 (8). 2579-2597. https://doi.org/10.1111/1365-2478.13008

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Official URL: http://dx.doi.org/10.1111/1365-2478.13008

Abstract/Summary

New developments in mobile resistivity meter instrumentation have made it possible to survey large areas with dense data coverage. The mobile system usually has a limited number of electrodes attached to a cable that is pulled along behind an operator so that a large area can be covered within a short time. Such surveys can produce three‐dimensional datasets with hundreds of thousands of electrodes positions and data points. Similarly, the inverse model used to interpret the data can have several hundred thousand cells. It is impractical to model such large datasets within a reasonable time on microcomputers used by many small companies employing standard inversion techniques. We describe a model segmentation technique that subdivides the finite‐element mesh used to calculate the apparent resistivity and Jacobian matrix values into a number of smaller meshes. A fast technique that optimizes the calculation of the Jacobian matrix values for multi‐channel systems was also developed. A one‐dimensional wavelet transform method was then used to compress the storage of the Jacobian matrix, in turn reducing the computer time and memory required to solve the least‐squares optimization equation to determine the inverse model resistivity values. The new techniques reduce the calculation time and memory required by more than 80% while producing models that differ by less than 1% from that obtained using the standard inversion technique with a single mesh. We present results using a synthetic model and a field dataset that illustrates the effectiveness of the proposed techniques.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/1365-2478.13008
ISSN:	0016-8025
Date made live:	14 Oct 2020 09:13 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/528712

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1111/1365-2478.13008

ISSN:

0016-8025

Date made live:

14 Oct 2020 09:13 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/528712