Robust scanning of AEM data for IP effects

Viezzoli, Andrea; Dauti, Francesco; Wijns, Chris

doi:10.1080/08123985.2020.1856624

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Its first application probably dates back to 1948 [5], but since then, many different helicopter and fixed-wing acquisition systems have become commercially available. Amongst the most internationally known examples are: EQUATOR [6][7][8], Xcite [9,10], VTEM [11,12], SkyTEM [13], AeroTEM [14], MULTIPULSE [15], MEGATEM/GEOTEM [16,17], TEMPEST [18,19], and Spectrem [20].…”

Section: Introductionmentioning

confidence: 99%

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

2021

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Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

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Section: Introductionmentioning

confidence: 99%

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

2021

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“…This has been driven by the fact that ground-based IP methods comprise some of the most widely used techniques in mineral exploration, not only for the discovery of many anomalous mineralisation prospects due to their chargeable response (Meju, 2002), but also for the increasing ability to identify subsurface materials and mineralogy (Merriam (2007), Qi et al (2018), Feng et al (2020)). Approaches using thresholds of re-parameterisations (Fiandaca et al, 2018), modelling of 3D IP effects (Nunes et al, 2019), and detection of IP using various approaches (Kang et al, 2019;Viezzoli et al, 2021) are some examples. However, Bayesian inference on the detectability of IP effects in AEM data has not been considered.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo

Davies¹,

Ley-Cooper²,

Sutton³

et al. 2021

Preprint

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We introduce a reduced model space approach via decoupled layered model parameters for inference of induced polarisation (IP) models versus conductive-only models fitted to airborne electromagnetic (AEM) data.• For model and parameter inference we develop an adaptive static sequential Monte Carlo algorithm with reversible jump Markov chain Monte Carlo proposals (RJSMC).• We successfully apply RJSMC to perform airborne induced polarisation (AIP) detectability on data from a large survey that demonstrates spatial continuity.

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Section: Introductionmentioning

confidence: 99%

Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo

Davies¹,

Ley-Cooper²,

Sutton³

et al. 2021

Preprint

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Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian approach to the IP-detectability problem using decoupled transdimensional layered models, and applies an approach novel to geophysics whereby transdimensional proposals are used within the embarrassingly parallelisable and robust static Sequential Monte Carlo (SMC) class of algorithms for the simultaneous inference of parameters and models. Henceforth referring to this algorithm as Reversible Jump Sequential Monte Carlo (RJSMC), the statistical methodological contributions to the algorithm account for adaptivity considerations for multiple models and proposal types, especially surrounding particle impoverishment in unlikely models. Methodological contributions to solid Earth geophysics include the decoupled model approach and proposal of a statistic that use posterior model odds for IP detectability. A case study is included investigating detectability of IP effects in AEM data at a broad scale.

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Robust scanning of AEM data for IP effects

Cited by 5 publications

References 12 publications

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo

Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo

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