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Fast, laterally smooth inversion of airborne time‐domain electromagnetic data
Author(s) -
Christensen Niels B.,
Reid James E.,
Halkjær Max
Publication year - 2009
Publication title -
near surface geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.639
H-Index - 39
eISSN - 1873-0604
pISSN - 1569-4445
DOI - 10.3997/1873-0604.2009047
Subject(s) - inversion (geology) , geology , computation , covariance , spurious relationship , covariance matrix , inverse transform sampling , time domain , algorithm , inverse problem , hydrogeology , synthetic data , mathematics , computer science , mathematical analysis , meteorology , seismology , geotechnical engineering , physics , statistics , aerosol , computer vision , tectonics
We present a fully developed, fast approximate method for 1D inversion of time‐domain electromagnetic data. The method is applied to a helicopterborne transient electromagnetic data set from the Toolibin Lake area of Western Australia using the lateral parameter correlation method to ensure lateral smoothness of the inverted models. The method is based on fast approximate forward computation of transient electromagnetic step responses and their derivatives with respect to the model parameters of a 1D model. The inversion is carried out with multi‐layer models in an iterative, constrained least‐squares inversion formulation including explicit formulation of the model regularization through a model covariance matrix. The method is 50 times faster than conventional inversion for a layered earth model and produces model sections of concatenated 1D models and contoured maps of mean conductivity in depth intervals almost indistinguishable from those of conventional inversion. To ensure lateral smoothness of the model sections and to avoid spurious artefacts in the mean conductivity maps, the inversion is integrated with the lateral parameter correlation method. In this way, well determined parameters are allowed to influence the more poorly determined parameters in the survey area. Applied to the Toolibin data set, the inversion produces model sections and conductivity maps that reveal the distribution of conductivity in the area and thereby the distribution of salinity. This information is crucial for any remediation effort aimed at alleviating the salinization problems.