Hydrogeophysical Characterization in a Volcanic Context From Local to Regional Scales Combining Airborne Electromagnetism and Magnetism

Dumont, Marc; Reninger, Pierre-Alexandre; Aunay, Bertrand; Pryet, Alexandre; Jougnot, Damien; Join, Jean-Lambert; Michon, Laurent; Martelet, Guillaume

doi:10.1002/essoar.10505311.2

Cited by 2 publications

(2 citation statements)

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“…HAC is a technique that uses bottom-up approaches which start with single data as a single cluster and merge them until one big cluster remains. The necessary procedure in order to accomplish the goal involves data preparation, similarity measures, and linkage criteria (Dumont et al, 2018;Delforge et al, 2021;Puntu et al, 2021). HAC exhibits several advantages over other algorithms.…”

Section: Hierarchical Agglomerative Clustering (Hac)mentioning

confidence: 99%

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

Puntu

Chang

Amania

et al. 2023

Front. Environ. Sci.

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This paper presents an alternative method for monitoring groundwater levels and estimating specific yields of an unconfined aquifer under different seasonal conditions. The approach employs the Time-Lapse Electrical Resistivity Imaging (TL-ERI) method and machine learning-based time series clustering. A TL-ERI survey was conducted at ten sites (WS01-WS10 sites) throughout the dry and wet seasons, with five-time measurements collected for each site, in the Taichung-Nantou Basin along the Wu River, Central Taiwan. The obtained resistivity raw data was inverted and converted into normalized water content values using Archie’s law, followed by applying the Van Genuchten (VG) model for the Soil Water Characteristic Curve to estimate the Groundwater Level (GWL), and estimated the theoretical specific yield (Sy) by computing the difference between the saturated and residual water contents of the fitted VG model. In addition, the specific yield capacity (Sc), representing the nature of the storage capacity in the aquifer, was also calculated. The results showed that this approach was able to estimate those hydrogeological parameters. The spatial distribution of the GWL reveals that during the dry-wet seasons from February to July, there was a high GWL that extended from southeast to northwest. Conversely, during the wet-dry seasons from July to October, the high GWL shrank, which can be attributed to recharge variations from rainfall events. The determined spatial distribution of Sy and Sc fall within the range of 0.03–0.24 and 0.14–0.25, respectively. To quantitatively establish areas of similar groundwater level changes along with the VG model parameter variations during the study period, a Time series Clustering analysis (TSC) was performed by utilizing Hierarchical Agglomerative Clustering (HAC). The findings suggest that the WS03 site is a promising area for further investigation due to its highest Sc value with a slight change in groundwater levels during the dry and wet seasons. This study brings an advanced development of the geoelectrical method to estimate regional hydrogeological parameters in an area with limited available groundwater observation wells, in different seasonal conditions for groundwater management purposes.

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Section: Hierarchical Agglomerative Clustering (Hac)mentioning

confidence: 99%

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

Puntu

Chang

Amania

et al. 2023

Front. Environ. Sci.

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“…Lower down, the aprons become alluvial floodplains, with alternating laharic formations and fine-grained soils, which can prevent the upwelling of underlying groundwater, creating artesian aquifers [Toulier et al, 2022]. This layering of geological formations related to the distribution of volcanic formations spatially disconnects the surface watersheds from underground ones [Charlier et al, 2011, Dumont et al, 2021, Vittecoq et al, 2014. At the same time, geological discontinuities along slopes may allow the release of groundwater at spatially-restricted emergence points, which may substantially contribute to surface water flows [Vittecoq et al, 2019].…”

Section: Introductionmentioning

confidence: 99%

Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem

Dumont¹,

Plagnes²,

Lachassagne³

et al. 2024

Comptes Rendus. Géoscience

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Andesitic volcanic hydrosystems in Indonesia are mostly hydrogeologically unknown despite their socio-economic importance. The development of robust and easy-to-implement methodologies to conceptualize and quantify the water cycle components becomes a prerequisite for their sustainable management.We developed a lumped hydrological model to mimic the structure and functioning of a previously unknown hydrosystem located on the flanks of the Salak volcano (West Java). The structure of the aquifers was revealed with electrical resistivity tomography. The distinction between springs fed by the extensive artesian aquifer and others fed by shallow perched aquifers was obtained mostly using hydrochemistry. The elevation of the recharge area was identified using isotopic analysis of spring water.After designing the hydrological model structure, we carried out a probabilistic parameters exploration using the multiple-try differential evolution adaptive Metropolis algorithm to calibrate aquifer discharge. Multiple Markov chains allow a better exploration of the parameter values. The Bayesian approach provides the best water cycle simulation with a parameter uncertainty analysis, improving the accuracy and representation of the water cycle appropriate for previously unknown hydrosystems.

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Hydrogeophysical Characterization in a Volcanic Context From Local to Regional Scales Combining Airborne Electromagnetism and Magnetism

Cited by 2 publications

References 0 publications

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

Groundwater monitoring and specific yield estimation using time-lapse electrical resistivity imaging and machine learning

Water cycle modelling strengthened by probabilistic integration of field data for groundwater management of a quite unknown tropical volcanic hydrosystem

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