Fuzzy regression for electrical resistivity-hydraulic conductivity relationships

The efficacy of the hyporheic zone (HZ) — where surface water and groundwater mix — for processing nutrients or uptake of metals is dependent on streambed hydraulic conductivity and stream discharge, among other characteristics. Here, we explore electrical resistivity tomography (ERT) of hyporheic exchange in Cement Creek near Silverton, Colorado, which is affected by ferricrete precipitation. To quantify flows through the HZ, we conducted four-hour salt injection tracer tests and collected time-lapse ERT of the streambed and banks of Cement Creek at high and low flow. We installed piezometers to conduct slug tests, which suggested a low permeability zone at 44-cm depth likely comprised of ferricrete that cemented cobbles together. Based on the ERT, the tracer released into the stream was constrained within the shallow streambed with little subsurface flow through the banks. Tracer was detected in the HZ for a longer time at high flow compared to low flow, suggesting that more flow paths were available to connect the stream to the HZ. Tracer was confined above the ferricrete layer during both the high- and low-flow tests. Mass transfer and storage area parameters were calculated from combined analysis of apparent bulk conductivity derived from ERT and numerical modeling of the tracer breakthrough curves. The hyporheic storage area estimated at low discharge (0.1 m2) was smaller than at high discharge (0.4 m2) and residence times were 2.7 h at low discharge and 4.1 h at high discharge. During high discharge, in-stream breakthrough curves displayed slower breakthrough and longer tails, which was consistent with the time-lapse electrical inversions and One-dimensional Transport with Inflow and Storage (OTIS) modeling. Our findings indicate that ferricrete reduces the hydraulic conductivity of the streambed and limits the areal extent of the HZ, which may lower the potential for pollutant attenuation from the metal-rich waters of Cement Creek.

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Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1925-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1925-1946 ◽

Cited By ~ 4

Author(s):

Nikolaj Kruse Christensen ◽

Steen Christensen ◽

Ty Paul A. Ferre

Keyword(s):

Electrical Resistivity ◽

Hydraulic Conductivity ◽

Prediction Error ◽

Model Calibration ◽

Hydrologic Model ◽

Geophysical Data ◽

Model Parameters ◽

Calibration Data ◽

Groundwater Model ◽

And Inversion

Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization. For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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Hydraulic conductivity characterization of a karst recharge area using water injection tests and electrical resistivity logging

Engineering Geology ◽

10.1016/j.enggeo.2010.10.008 ◽

2011 ◽

Vol 117 (1-2) ◽

pp. 90-96 ◽

Cited By ~ 20

Author(s):

Bárbara Angulo ◽

Tomás Morales ◽

Jesús A. Uriarte ◽

Iñaki Antigüedad

Keyword(s):

Electrical Resistivity ◽

Hydraulic Conductivity ◽

Water Injection ◽

Recharge Area ◽

Resistivity Logging

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Modeling Baseflow from an Alluvial Aquifer Using Hydraulic-Conductivity Data Obtained from a Derived Relation with Apparent Electrical Resistivity

Hydrogeology Journal ◽

10.1007/s100400050125 ◽

1997 ◽

Vol 5 (3) ◽

pp. 97-108 ◽

Cited By ~ 9

Author(s):

A. Dassargues

Keyword(s):

Electrical Resistivity ◽

Hydraulic Conductivity ◽

Alluvial Aquifer ◽

Conductivity Data

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Discontinuity Predictions of Porosity and Hydraulic Conductivity Based on Electrical Resistivity in Slopes through Deep Learning Algorithms

Sensors ◽

10.3390/s21041412 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1412 ◽

Cited By ~ 1

Author(s):

Seung-Jae Lee ◽

Hyung-Koo Yoon

Keyword(s):

Electrical Resistivity ◽

Deep Learning ◽

Hydraulic Conductivity ◽

Short Term Memory ◽

Learning Algorithms ◽

Electrode Spacing ◽

Types Of Information ◽

Learning Analysis ◽

Gated Recurrent Unit ◽

Deep Learning Model

Electrical resistivity is used to obtain various types of information for soil strata. Hence, the prediction of electrical resistivity is helpful to predict the future behavior of soil. The objective of this study is to apply deep learning algorithms, including deep neural network (DNN), long-short term memory (LSTM), and gated recurrent unit (GRU), to determine the reliability of electrical resistivity predictions to find the discontinuity of porosity and hydraulic conductivity. New DNN-based algorithms, i.e., LSTM-DNN and GRU-DNN, are also applied in this study. The electrical resistivity values are obtained using 101 electrodes installed at 2 m intervals on a mountaintop, and a Wenner array is selected to simplify the electrode installation and measurement. A total of 1650 electrical resistivity values are obtained for one measurement considering the electrode spacing, and accumulated data measured for 15 months are used in the deep learning analysis. A constant ratio of 6:2:2 among the training, validation, and test data, respectively, is used for the measured electrical resistivity, and the hyperparameters in each algorithm are moderated to improve the reliability. Based on the deep learning model results, the distributions of porosity and hydraulic conductivity are deduced, and an average depth of 25 m is estimated for the discontinuity depth. This paper shows that the deep learning technique is well used to predict electrical resistivity, porosity, hydraulic conductivity, and discontinuity depth.

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The Estimation of Hydraulic Properties from Geophysical Measurement of Subsoil Depend on Regression Equation

Modern Applied Science ◽

10.5539/mas.v13n6p12 ◽

2019 ◽

Vol 13 (6) ◽

pp. 12

Author(s):

Safaa F. Yasir ◽

Janmaizatulriah Jani ◽

Mazidah Mukri

Keyword(s):

Electrical Resistivity ◽

Hydraulic Conductivity ◽

Regression Equation ◽

Array Configuration ◽

Non Invasive ◽

Subsurface Zone ◽

Resistivity Survey ◽

Survey Line ◽

Geophysical Measurement ◽

Non Destructive

This paper illustrated an establishment relationship between electrical resistivity by using electrical resistivity imaging (ERI) technique and hydraulic conductivity. The test conducted in two locations (Kampung Semerbok, Rembau and Felda Bukit Rokan Utara, Gemencheh) in Malaysia. Schlumberger array configuration was adopted by using ABEM SAS 4000 with eighty-one (81) electrodes for first site and forty-one (14) electrodes for second site. The total length of resistivity survey line was 400 m and 200 m for site one and two respectively. Statistical analysis based on regression equation was involved to find the relationship between hydraulic conductivity and resistivity. This result was compared with the hydraulic conductivity obtained from pumping tests for the well which is located within the resistivity survey line range. This study showed a good relationship between resistivity and hydraulic conductivity and can be used as preliminary tool to assess subsurface zone with non- invasive non-destructive for the soil with reducing time and cost.

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