Fast AEM data processing and inversion

Abstract

When airborne electromagnetic (AEM) data is acquired as a streamed or time-series data set, the great redundancy in the data favours compression as a first step in processing. Traditional data compression schemes are time windowing and spatial averaging. An alternative, more efficient data compression scheme is to transform time or frequency domain data to time-constant tau space, which has the effect of removing the waveform dependence of the AEM response.When there are many local anomalies and a variable background, the next stage of rapid processing is to transform the response to a conductivity-depth image (CDI) to facilitate geological interpretation of the background response. Use of the full time range of recorded data, particularly the inclusion of on-time data, improves the stability of the CDI process.The final AEM data processing step for mineral explora tion is to assess the likelihood that any local anomaly corresponds to a desired economic target. This step involves the extraction of target geometry and conductivity informa tion from the AEM data. The only economically feasible route at the present time is to parameterise both the data (using inductive and resistive limits) and the model to allow inversion of the local anomalies. A fit to one or two plate like conductors can be achieved in seconds; fits to a block like body take minutes on a fast PC. A significant research challenge remains to speed up and stabilise this process. APPENDIX A: MATHEMATICAL BASIS OF ARBITRARY WAVEFORM DECOMPOSITIONThis brief summary includes some sign corrections from the article by Stolz and Macnae(1998): Representation of time-domain step and frequencydomain responseThe step function response of an isolated conductor can be exnressed as:

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“…The ratio of secondary fields as a function of geometry can be calculated using the first moment of a sphere (Smith and Lee, 2002) or the inductive limit of a thin sheet (Macnae et al, 1998). …”

“…Using the Maxwell receding image algorithm that is the basis for program EMFlow (Macnae et al, 1998), a very quick AEM forward modelling algorithm was developed that can simulate a three-layer earth with variable depth and conductivity of each of the layers. The results of this forward modelling then have some noise imposed.…”

3D inversion of airborne electromagnetic data using a moving footprint

“…Huang and Fraser, 2002), conductivity depth transforms (e.g. Wolfgram and Karlik, 1995;Macnae et al, 1998;Fullagar and Reid, 2001), S-inversion (Tartaras et al, 2000;Combrinck, 2008), Zohdy's method (Sattel, 2005), layered earth inversions (e.g. Chen and Raiche, 1998;Farquharson et al, 2003;Christensen et al, 2010), laterally constrained layered earth inversions (e.g.…”

Conductivity-depth Imaging of Large Loop TEM Sounding Data Acquired using Central loop Configuration

“…There are number of methods for deriving conductivity-depth sections from timedomain EM data ( [5]; [6]; [7]; [10]; [15]; [16]), based on different approaches. Of these various conductance-depth and/ or conductivity depth imaging techniques, each one is meant for the specific case and is suitable for a particular source excitation (i.e.…”

“…There are various techniques available in the literature for imaging of TEM measurement (voltage decay curve) timedomain central loop EM ([1]; [5]; [10]; [12]; [15]; [24]; [25]; [26]; [27]; [32]). In present research the continuous voltage versus time curve is converted into the conductivity-depth section using the concept as given in([1]).…”