Technical Papers - Airborne Electromagnetics

Practical Inversions for Helicopter Electromagnetic Data

Greg Hodges, Fugro Airborne Surveys, Mississauga, ON, Canada

Abstract

Discrete-layer inversions are the most accurate method of converting geophysical data to a geoelectrical model of the earth. Smooth-model inversions and transforms (Occam's inversions and CDI sections) are more robust to execute, but only discrete-layer inversions can provide direct measurement of depth to a target layer, which is necessary for engineering purposes. However, there can be a problem with non-unique solutions - there may be two or more solutions that fit the data within the specified accuracy. Also, discrete-layer inversions tend to be model specific. They generally use a constant number of layers, similar conductivity contrast for the starting model, etc. This can present a problem when the geological model changes within a data set, changing the conditions or dropping data from high signal down to zero, for example.

An inversion process with built-in geological and geophysical "intelligence" can overcome many of these limitations. Input data are weighted based on the signal level. Questionable data, or data clearly influenced by non-target effects (e.g. power lines) are rejected. Existing data, such as drill holes, are used to generate the best possible starting model and the inversion process constrained to honour these data. Known geophysical parameters, such as the conductivity of bedrock, or conductivity of a water layer, can be fixed. Any of these factors can change across a data set or region, and the inversion will adjust itself to match the changes.

Layer extraction algorithms have been developed to measure the depth to specific layers from smooth sections, or to provide starting models from smoothed sections for discrete-layer inversions.

This "intelligent" inversion process was used to generate depth-to-bedrock maps over a sink-hole where the overburden changed from conductive clay to resistive sand. Drill and seismic information was used to help generate the starting model for each data point, and the inversion was constrained when close to these data points. Magnetically permeable geology and cultural interference were identified, and the input data were adjusted to minimise the effects of these non-conductivity effects.

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