Quantitative water content estimation in landfills through joint inversion of seismic refraction and electrical resistivity data considering surface conduction

Steiner, Matthias; Katona, Timea; Fellner, Johann; Orozco, Adrián Flores

doi:10.1016/j.wasman.2022.05.020

Cited by 13 publications

(6 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other attempts to predict permeability through TDIP data with different approaches used in recent years (e.g., Revil et al., 2020) required the estimation of petrophysical parameters such as cementation exponent and grain density. As these parameters are estimated through laboratory measurements on soil samples (Steiner et al., 2022), this method cannot be applied at the Circeo National Park, where drilling boreholes was unfeasible. Although the formation factor plays a critical role in the equations predicting hydraulic conductivity from IP measurements (Flores‐Orozco et al., 2022), it can be conveniently estimated in Equation (8) by the low‐frequency conductivity σ 0 (Weller et al., 2015).…”

Section: Discussionmentioning

confidence: 99%

Resistivity and full‐decay IP inversion for imaging a coastal aquifer prone to saline intrusion: The Pontina Plain case study (Central Italy)

Donno

Cercato

2023

Near Surface Geophysics

View full text Add to dashboard Cite

Many coastal areas are affected by groundwater salinization due to the unsustainable use of groundwater resources. For a cost-effective quantitative assessment of groundwater resources, electrical resistivity (ER) tomography is often used as a standalone geophysical technique. In this paper, we present an application of the integration of direct-current ER and full-decay-induced polarization (IP) method at the Pontina Plain (Central Italy). The case study is a coastal area in Central Italy prone to salinization due to both geological and anthropogenic factors. To achieve these goals, we inverted full-decay timedomain electrical data for Cole-Cole parameters. The resulting multi-parameter model provides a first approximation prediction of the permeability, employing well-established empirical relationships with the electrical parameters. We demonstrated that our approach: (i) can locate highly conductive zones directly related to saline intrusion inland using the resistivity as a fast proxy; (ii) can remove the ambiguity in the detection of clay/silt layers in the near-surface; and (iii) permit a prediction of the permeability, employing full-decay inversion of time-domain electrical data. However, the extremely conductive environment prevents the use of IP data for the reconstruction of deep layers or detection of the salt wedge front. Therefore, this approach can be used for hydro-geophysical screening and monitoring of salinization-prone sites, where strong limitations to direct inspection exist due to external constraints (e.g., protected lands).

show abstract

Section: Discussionmentioning

confidence: 99%

Resistivity and full‐decay IP inversion for imaging a coastal aquifer prone to saline intrusion: The Pontina Plain case study (Central Italy)

Donno

Cercato

2023

Near Surface Geophysics

View full text Add to dashboard Cite

show abstract

“…For example, Steiner et al. (2022) jointly inverted seismic refraction and ERT data collected at high and low frequencies (i.e., induced polarization) to yield spatially continuous estimates of porosity, cation exchange capacity, and VWC. Garré et al.…”

Section: Introductionmentioning

confidence: 99%

“…Because of this, moisture estimates from surface-based electrical geophysical methods (e.g., FDEM and ERT) may be limited to a qualitative interpretation of approximate zones of relatively high/low moisture unless an acceptable means for estimating variability in petrophysical properties has been established. For example, Steiner et al (2022) jointly inverted seismic refraction and ERT data collected at high and low frequencies (i.e., induced polarization) to yield spatially continuous estimates of porosity, cation exchange capacity, and VWC. Garré et al (2011) performed an ERT-based moisture monitoring and root water uptake experiment on a soil monolith using detailed time-domain reflectometry data to characterize different soil horizons in the monolith to calibrate a Waxman and Smits (1968) petrophysical function.…”

Section: Introductionmentioning

confidence: 99%

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Terry

Day‐Lewis

Lane

et al. 2023

Vadose Zone Journal

View full text Add to dashboard Cite

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025-0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

show abstract

“…Nonetheless, assessing VWC from TL-ERT can be challenging due to the many physical parameters also affecting subsurface bulk EC (such as temperature, pore water EC, porosity, grain size distribution and mineralogy 50 , 51 ). In this context, some studies have assessed the accuracy of the ERT-predicted VWC, for example, by comparing them with conventional gravimetric methods applied on samples collected on the field 52 – 54 or hydrogeological data from sensors installed on the field 2 , 55 – 58 .…”

Section: Introductionmentioning

confidence: 99%

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

Dimech,

Isabelle,

Sylvain

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Accurate and large-scale assessment of volumetric water content (VWC) plays a critical role in mining waste monitoring to mitigate potential geotechnical and environmental risks. In recent years, time-lapse electrical resistivity tomography (TL-ERT) has emerged as a promising monitoring approach that can be used in combination with traditional invasive and point-measurements techniques to estimate VWC in mine tailings. Moreover, the bulk electrical conductivity (EC) imaged using TL-ERT can be converted into VWC in the field using petrophysical relationships calibrated in the laboratory. This study is the first to assess the scale effect on the accuracy of ERT-predicted VWC in tailings. Simultaneous and co-located monitoring of bulk EC and VWC are carried out in tailings at five different scales, in the laboratory and in the field. The hydrogeophysical datasets are used to calibrate a petrophysical model used to predict VWC from TL-ERT data. Overall, the accuracy of ERT-predicted VWC is $$\pm 0.03~\textrm{m}^3/\textrm{m}^3$$ ± 0.03 m 3 / m 3 , and the petrophysical models determined at sample-scale in the laboratory remain valid at larger scales. Notably, the impact of temperature and pore water EC evolution plays a major role in VWC predictions at the field scale (tenfold reduction of accuracy) and, therefore, must be properly taken into account during the TL-ERT data processing using complementary hydrogeological sensors. Based on these results, we suggest that future studies using TL-ERT to predict VWC in mine tailings could use sample-scale laboratory apparatus similar to the electrical resistivity Tempe cell presented here to calibrate petrophysical models and carefully upscale them to field applications.

show abstract

Quantitative water content estimation in landfills through joint inversion of seismic refraction and electrical resistivity data considering surface conduction

Cited by 13 publications

References 99 publications

Resistivity and full‐decay IP inversion for imaging a coastal aquifer prone to saline intrusion: The Pontina Plain case study (Central Italy)

Resistivity and full‐decay IP inversion for imaging a coastal aquifer prone to saline intrusion: The Pontina Plain case study (Central Italy)

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

Contact Info

Product

Resources

About