Laterally Constrained Inversion of Fixed-Wing Frequency-Domain AEM Data

Tartaras, Efthimios; Beamish, David

doi:10.3997/2214-4609.201402625

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…The altitude‐error effect on a few layer model assessment is considered first. The inversion method used is the constrained conjugate gradient inversion described by Tartaras and Beamish (). Although developed for the application of lateral constraints to model calculations, the method is inherently stable and does not require regularization even when lateral constraints are not used.…”

Section: Influence Of Altitude Errors On 1d Conductivity Modelsmentioning

confidence: 99%

“…A few layer inversion with lateral constraints can also be applied to the data, to reduce the point‐to‐point variability in the conductivity models and to better demonstrate the canopy effect. The inversion method used is the constrained conjugate gradient inversion described by Tartaras and Beamish (). Following the previous Occam analysis, a 5 layer model configuration was assumed appropriate.…”

Section: Survey Case Studymentioning

confidence: 99%

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The canopy effect in AEM revisited: investigations using laser and radar altimetry

Beamish

Leväniemi

2009

Near Surface Geophysics

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This study considers a specific issue, often termed the canopy effect that relates to our ability to provide accurate conductivity models from airborne electromagnetic (AEM) data. The central issue is one of the correct determination of sensor height(s) above the ground surface (terrain clearance) to the appropriate accuracy. The present study uses the radar and laser systems installed on a fixed-wing AEM system to further investigate the effect. The canopy effect can arise due to a variety of elevated features below, and in the vicinity of, the flight line. The most obvious features are welldefined forest and copse zones together with domestic, commercial and agricultural buildings. Such features may cause the terrain clearance to be underestimated and this has the potential to introduce resistive artefacts and incorrect interface depths into conductivity models. Correct determination of terrain clearance is also important for the accurate processing of the other geophysical data sets acquired by airborne surveys. Radar and laser altimetry offer two very different physical measurements of height above ground. Airborne radars detect the range to the nearest reflecting object.They do this over a cone of influence that may have a radius (at the ground surface) of ~55 m (assuming a survey height of 60 m). Reflections from objects that are off-line are thus a distinct possibility. In direct contrast, a laser ranging device with low beam divergence provides a highly focussed measurement. Laser accuracies (typically < 2cm) are far greater than those of radars (typically ~0.5 m). In addition, the rapid sampling of laser ranging devices (e.g. up to 2 kHz) allows both real-time and post-3 processing algorithms to be applied to estimate the maximum range recorded across appropriate time/spatial windows. In our case a 2 kHz (maximum) dual-pulse laser

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Section: Influence Of Altitude Errors On 1d Conductivity Modelsmentioning

confidence: 99%

Section: Survey Case Studymentioning

confidence: 99%

The canopy effect in AEM revisited: investigations using laser and radar altimetry

Beamish

Leväniemi

2009

Near Surface Geophysics

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“…Recently, a promising low cost numerical strategy for full 2.5D modelling was published with application to CSEM systems (Streich, Becken and Ritter 2011). Constrained codes capable of utilizing the spatial coherence of a layered earth also exist (Santos 2004;Tartaras and Beamish 2005;Vallée and Smith 2009) but scalability issues severely limit the size of the problem that can be inverted at a time. However, we are unaware of any AEM implementations or applications in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…This includes relatively simple data transform techniques (Macnae et al 1998;Reid and Fullagar 1998;Sattel 2005), inversions utilizing approximate forward models (Farquharson, Oldenburg and Li 1999;Christensen 2002;Christensen, Reid and Halkjaer 2009) and also full forward models (Chen and Raiche 1998;Farquharson, Oldenburg and Routh 2003). Constrained codes capable of utilizing the spatial coherence of a layered earth also exist (Santos 2004;Tartaras and Beamish 2005;Vallée and Smith 2009) but scalability issues severely limit the size of the problem that can be inverted at a time.…”

Section: Introductionmentioning

confidence: 99%

A parallel, scalable and memory efficient inversion code for very large‐scale airborne electromagnetics surveys

Kirkegaard

Auken

2014

Geophysical Prospecting

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Over the past decade the typical size of airborne electromagnetic data sets has been growing rapidly, along with an emerging need for highly accurate modelling. One‐dimensional approximate inversions or data transform techniques have previously been employed for very large‐scale studies of quasi‐layered settings but these techniques fail to provide the consistent accuracy needed by many modern applications such as aquifer and geological mapping, uranium exploration, oil sands and integrated modelling. In these cases the use of more time‐consuming 1D forward and inverse modelling provide the only acceptable solution that is also computationally feasible. When target structures are known to be quasi layered and spatially coherent it is beneficial to incorporate this assumption directly into the inversion. This implies inverting multiple soundings at a time in larger constrained problems, which allows for resolving geological layers that are undetectable using simple independent inversions. Ideally, entire surveys should be inverted at a time in huge constrained problems but poor scaling properties of the underlying algorithms typically make this challenging. Here, we document how we optimized an inversion code for very large‐scale constrained airborne electromagnetic problems. Most importantly, we describe how we solve linear systems using an iterative method that scales linearly with the size of the data set in terms of both solution time and memory consumption. We also describe how we parallelized the core region of the code, in order to obtain almost ideal strong parallel scaling on current 4‐socket shared memory computers. We further show how model parameter uncertainty estimates can be efficiently obtained in linear time and we demonstrate the capabilities of the full implementation by inverting a 3327 line km SkyTEM survey overnight. Performance and scaling properties are discussed based on the timings of the field example and we describe the criteria that must be fulfilled in order to adapt our methodology for similar type problems.

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“…Chen and Raiche, 1998;Farquharson et al, 2003;Christensen et al, 2010), laterally constrained layered earth inversions (e.g. Auken et al, 2005, Tartaras andBeamish, 2005;Vallée and Smith, 2009;Viezzoli et al, 2009) or holistic inversions (e.g. Sambridge, 2006, 2009).…”

Section: Introductionmentioning

confidence: 99%

3D inversion of airborne electromagnetic data using a moving footprint

Cox

Wilson

Zhdanov

2010

Exploration Geophysics

202

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Abstract. It is often argued that 3D inversion of entire airborne electromagnetic (AEM) surveys is impractical, and that 1D methods provide the only viable option for quantitative interpretation. However, real geological formations are 3D by nature and 3D inversion is required to produce accurate images of the subsurface. To that end, we show that it is practical to invert entire AEM surveys to 3D conductivity models with hundreds of thousands if not millions of elements. The key to solving a 3D AEM inversion problem is the application of a moving footprint approach. We have exploited the fact that the area of the footprint of an AEM system is significantly smaller than the area of an AEM survey, and developed a robust 3D inversion method that uses a moving footprint. Our implementation is based on the 3D integral equation method for computing data and sensitivities, and uses the re-weighted regularised conjugate gradient method for minimising the objective functional. We demonstrate our methodology with the 3D inversion of AEM data acquired for salinity mapping over the Bookpurnong Irrigation District in South Australia. We have inverted 146 line km of RESOLVE data for a 3D conductivity model with 310 000 elements in 45 min using just five processors of a multi-processor workstation.

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Laterally Constrained Inversion of Fixed-Wing Frequency-Domain AEM Data

Cited by 4 publications

References 10 publications

The canopy effect in AEM revisited: investigations using laser and radar altimetry

The canopy effect in AEM revisited: investigations using laser and radar altimetry

A parallel, scalable and memory efficient inversion code for very large‐scale airborne electromagnetics surveys

3D inversion of airborne electromagnetic data using a moving footprint

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