Inversion of Hydraulic Conductivity from Induced Polarisation, Part A: Methodology and Verification

Fiandaca, Gianluca; Madsen, Line Meldgaard; Olmo, M.; Römhild, L.; Maurya, Pradip Kumar

doi:10.3997/2214-4609.202120192

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

Imaging hydraulic conductivity in near-surface aquifers by complementing cross-borehole induced polarization with hydraulic experiments

Römhild

Fiandaca

et al. 2022

Advances in Water Resources

View full text Add to dashboard Cite

Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging

Römhild,

Fiandaca,

Bayer

2024

Geophysical Journal International

View full text Add to dashboard Cite

Summary For accurate modeling of groundwater flow and transport processes within an aquifer, precise knowledge about hydraulic conductivity K and its small-scale heterogeneities is fundamental. Methods based on pumping tests, such as hydraulic tomography (HT), allow for retrieving reliable K-estimates, but are limited in their ability to image structural features with high resolution, since the data from time-consuming hydraulic tests are commonly sparse. In contrast, geophysical methods like induced polarization (IP) can potentially yield structural images of much higher resolution, but depend on empirical petrophysical laws that may introduce significant uncertainties to the K-estimation. Therefore, this paper presents a joint inversion procedure for both HT and IP data, which allows for combining the complementary abilities of both methods. Within this approach, a travel time inversion is applied to the HT data, while the IP inversion is based on a full-decay time-domain forward response, as well as a re-parameterization of the Cole-Cole model to invert for K directly. The joint inversion is tested on a synthetic model mimicking horizontally layered sediments, and the results are compared with the individual HT and IP inversions. It is shown that jointly inverting both data sets consistently improves the results by combining the complementary sensitivities of the two methods, and that the inversion is more robust against changes in the experimental setups. Furthermore, we illustrate how a joint inversion approach can correct biases within the petrophysical laws by including reliable K-information from hydraulic tests and still preserving the high-resolution structural information from IP. The different inversion results are compared based on the structural similarity index (SSIM), which underlines the robustness of the joint inversion compared to using the data individually. Hence, the combined application of HT and IP within field surveys and a subsequent joint inversion of both data sets may improve our understanding of hydraulically relevant subsurface structures, and thus the reliability of groundwater modeling results.

show abstract

Inversion of Hydraulic Conductivity from Induced Polarisation, Part A: Methodology and Verification

Cited by 3 publications

References 0 publications

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

Imaging hydraulic conductivity in near-surface aquifers by complementing cross-borehole induced polarization with hydraulic experiments

Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging

Contact Info

Product

Resources

About