Estimation of anisotropic hydraulic conductivity using geophysical data in a coastal aquifer of Karnataka, India

2021 ◽  
Vol 35 (10) ◽  
Author(s):  
Priyanka Bangalore Nagaraj ◽  
Mohan Kumar Mandalagiri Subbarayappa ◽  
Vouillamoz Jean‐Michel ◽  
Johan Hoareau
Keyword(s):  
Hydraulic Conductivity ◽  
Coastal Aquifer ◽  
Geophysical Data ◽  
Anisotropic Hydraulic Conductivity

scholarly journals Three-Dimensional Modelling of Heterogeneous Coastal Aquifer: Upscaling from Local Scale

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 421
Author(s):  
Priyanka B.N. ◽  
M.S. Mohan Kumar
Keyword(s):  
Hydraulic Conductivity ◽  
Coastal Aquifer ◽  
Transversely Isotropic ◽  
Local Scale ◽  
Model Output ◽  
Mean Square ◽  
Isotropic Model ◽  
Aquifer Parameters ◽  
Longitudinal Dispersivity ◽  
Anisotropic Hydraulic Conductivity

The aquifer heterogeneity is often simplified while conceptualizing numerical model due to lack of field data. Conducting field measurements to estimate all the parameters at the aquifer scale may not be feasible. Therefore, it is essential to determine the most significant parameters which require field characterization. For this purpose, the sensitivity analysis is performed on aquifer parameters, viz., anisotropic hydraulic conductivity, effective porosity and longitudinal dispersivity. The results of the sensitivity index and root mean square deviation indicated, that the longitudinal dispersivity and anisotropic hydraulic conductivity are the sensitive aquifer parameters to evaluate seawater intrusion in the study area. The sensitive parameters are further characterized at discrete points or at local scale by using regression analysis. The longitudinal dispersivity is estimated at discrete well points based on Xu and Eckstein regression formula. The anisotropic hydraulic conductivity is estimated based on established regression relationship between hydraulic conductivity and electrical resistivity with R2 of 0.924. The estimated hydraulic conductivity in x and y-direction are upscaled by considering the heterogeneous medium as statistically homogeneous at each layer. The upscaled model output is compared with the transversely isotropic model output. The bias error and root mean square error indicated that the upscaled model performed better than the transversely isotropic model. Thus, this investigation demonstrates the necessity of considering spatial heterogeneous parameters for effective modelling of the seawater intrusion in a layered coastal aquifer.

Simultaneous identification of contaminant source location, initial contaminant concentration, horizontal and vertical hydraulic conductivity in a three-dimensional coastal aquifer via ensemble Kalman filter 

2021 ◽  
Author(s):  
Arezou Dodangeh ◽  
Mohammad Mahdi Rajabi ◽  
Marwan Fahs
Keyword(s):  
Kalman Filter ◽  
Hydraulic Conductivity ◽  
Coastal Aquifers ◽  
Ensemble Kalman Filter ◽  
Coastal Aquifer ◽  
Three Dimensional ◽  
Source Location ◽  
Contaminant Concentration ◽  
Contaminant Source ◽  
Vertical Hydraulic Conductivity

<p>In coastal aquifers, we face the problem of salt water intrusion, which creates a complex flow field. Many of these coastal aquifers are also exposed to contaminants from various sources. In addition, in many cases there is no information about the characteristics of the aquifer. Simultaneous identification of the contaminant source and coastal aquifer characteristics can be a challenging issue. Much work has been done to identify the contaminant source, but in the complex velocity field of coastal aquifer, no one has resolved this issue yet. We want to address that in a three-dimensional artificial coastal aquifer.</p><p>To achieve this goal, we have developed a method in which the contaminant source can be identified and the characteristics of the aquifer can be estimated by using information obtained from observation wells. First, by assuming the input parameters required to simulate the contaminant transfer to the aquifer, this three-dimensional coastal aquifer that is affected by various phenomena such as seawater intrusion, tides, shore slope, rain, discharge and injection wells, is simulated and the time series of the output parameters including head, salinity and contaminant concentration are estimated. In the next step, with the aim of performing inverse modeling, random values ​​are added to the time series of outputs obtained at specific points (points belonging to observation wells) in order to rebuilt the initial conditions of the problem to achieve the desired unknowns (contaminant source and aquifer characteristics). The unknowns estimated in this study are the contaminant source location (x, y, z), the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. SEAWAT model in GMS software environment has been used to solve the equations of flow and contaminant transfer and simulate a three-dimensional coastal aquifer. Next, for reverse modeling, one of the Bayesian Filters subset (ensemble Kalman filter) has been used in the Python programming language environment. Also, to reduce the code run time, the neural network model is designed and trained for the SEAWAT model.</p><p>This method is able to meet the main purpose of the study, namely estimating the value ​​of unknown input parameters, including the contaminant source location, the initial contaminant concentration, the horizontal and vertical hydraulic conductivity of the aquifer. In addition, that makes it possible to achieve a three-dimensional numerical model of the coastal aquifer that can be used as a benchmark to examine more accurately the impact of different phenomena simultaneously. In conclusion, we have developed an algorithm which can be used in the world's coastal aquifers to identify the contaminant source and estimate its characteristics.</p><p> </p><p>Key words: coastal aquifer, seawater intrusion, contaminants, groundwater, flow field, parameter estimation, ensemble kalman filter</p>

THE ANISOTROPIC HYDRAULIC CONDUCTIVITY OF SOIL

1957 ◽  
Vol 8 (1) ◽  
pp. 42-47 ◽  
Author(s):  
E. C. CHILDS
Keyword(s):  
Hydraulic Conductivity ◽  
Anisotropic Hydraulic Conductivity

Spatialization of physical variables in soils by geophysical, geotechnical and geostatistic methods: the Bayesian maximum entropy data fusion approach

2021 ◽  
Author(s):  
Sara Rabouli ◽  
Vivien Dubois ◽  
Marc Serre ◽  
Julien Gance ◽  
Hocine Henine ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Data Fusion ◽  
Maximum Entropy ◽  
Test Data ◽  
Saturated Hydraulic Conductivity ◽  
Geophysical Data ◽  
Real Field ◽  
Bayesian Maximum Entropy ◽  
Near Surface ◽  
Infiltration Test

<p>The soil is considered as a biological reactor or an outlet for treated domestic wastewater, respectively to reduce pollutant concentrations in the flows or because the surface hydraulic medium is too remote. In these cases, the saturated hydraulic conductivity of the soil is a key is a quantitative measure to assess whether the necessary infiltration capacity is available. To our knowledge, there is no satisfactory technique for evaluating the saturated hydraulic conductivity Ks of a heterogeneous soil (and its variability) at the scale of a parcel of soil. The aim of this study is to introduce a methodology that associates geophysical measurements and geotechnical in order to better described the near-surface saturated hydraulic conductivity Ks. Here we demonstrate here the interest of using a geostatistical approach, the BME "Bayesian Maximum Entropy", to obtain a 2D spatialization of Ks in heterogeneous soils. This tool opens up prospects for optimizing the sizing infiltration structures that receive treated wastewater. In our case, we have Electrical Resistivity Tomography (ERT) data (dense but with high uncertainty) and infiltration test data (reliable but sparse). The BME approach provides a flexible methodological framework to process these data. The advantage of BME is that it reduces to kriging as its linear limiting cases when only Gaussian data is used, but can also integrate data of other types as might be considered in future works. Here we use hard and Gaussian soft data to rigorously integrate the different data at hand (ERT, and Ks measurement) and their associated uncertainties. Based on statistical analysis, we compared the estimation performances of 3 methods: kriging interpolation of infiltration test data, the transformation of ERT data, and BME data fusion of geotechnical and geophysical data. We evaluated the 3 methods of estimation on simulated datasets and we then do a validation analysis using real field data. We find that BME data fusion of geotechnical and geophysical data provides better estimates of hydraulic conductivity than using geotechnical or geophysical data alone.</p>

Structural gradients and anisotropic hydraulic conductivity in the enigmatic eel traps of carnivorous corkscrew plants ( Genlisea spp.)

2021 ◽  
Author(s):  
Cora F. Carmesin ◽  
Andreas S. Fleischmann ◽  
Matthias M. Klepsch ◽  
Anna S. Westermeier ◽  
Thomas Speck ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Anisotropic Hydraulic Conductivity

scholarly journals Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Lin Zhu ◽  
Huili Gong ◽  
Yun Chen ◽  
Xiaojuan Li ◽  
Xiang Chang ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Geophysical Data

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

2016 ◽  
Vol 20 (5) ◽  
pp. 1925-1946 ◽  
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.

Data-domain correlation approach for joint hydrogeologic inversion of time-lapse hydrogeologic and geophysical data

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. F127-F140 ◽  
Author(s):  
Timothy C. Johnson ◽  
Roelof J. Versteeg ◽  
Hai Huang ◽  
Partha S. Routh
Keyword(s):  
Parameter Estimation ◽  
Hydraulic Conductivity ◽  
Strong Correlation ◽  
Geophysical Methods ◽  
Time Lapse ◽  
Geophysical Data ◽  
Correlation Operator ◽  
Parameter Estimations ◽  
Lack Of Information ◽  
Information Gap

Inverse estimations of hydrogeologic properties often are highly uncertain because of the expense of collecting hydrogeologic data and the subsequent lack of information. Geophysical data potentially can help fill this information gap because geophysical methods can survey large areas remotely and relatively inexpensively. However, geophysical data are difficult to incorporate into hydrogeologic parameter estimations primarily because of a lack of knowledge concerning the petrophysical relationships between hydrogeologic and geophysical parameters. A method can be used that allows time-lapse geophysical data to be incorporated directly into a hydrogeologic parameter estimation when a strong correlation exists between changes in geophysical and hydrogeologic properties. This approach bypasses the need for an explicit petrophysical transform by formulating the geophysical part of the hydrogeologic inversion in terms of a data-domain correlation operator. A synthetic electrical resistivity monitoring application is used to estimate the hydraulic conductivity distribution. Including time-lapse resistivity data to supplement sparse hydrologic data appears to improve greatly the resolution of hydraulic conductivity in this case. More generally, the formulation and results suggest that geophysical monitoring data can be incorporated effectively into a hydrogeologic parameter estimation using a data-domain correlation operator, assuming a strong correlation exists between changes in hydrogeologic and geophysical properties.

scholarly journals A framework for testing the use of electric and electromagnetic data to reduce the prediction error of groundwater models

2015 ◽  
Vol 12 (9) ◽  
pp. 9599-9653 ◽  
Author(s):  
N. K. Christensen ◽  
S. Christensen ◽  
T. P. A. Ferre
Keyword(s):  
Hydraulic Conductivity ◽  
Tikhonov Regularization ◽  
Prediction Error ◽  
Model Calibration ◽  
Groundwater Modeling ◽  
Hydrologic Model ◽  
Geophysical Data ◽  
Model Parameters ◽  
Calibration Data ◽  
And Inversion

Abstract. Despite geophysics is being used increasingly, it is still unclear how and when the integration of geophysical data improves the construction and predictive capability of groundwater models. Therefore, this paper presents a newly developed HYdrogeophysical TEst-Bench (HYTEB) which is a collection of geological, groundwater and geophysical modeling and inversion software wrapped to make a platform for generation and consideration of multi-modal data for objective hydrologic analysis. It is intentionally flexible to allow for simple or sophisticated treatments of geophysical responses, hydrologic processes, parameterization, and inversion approaches. It can also be used to discover potential errors that can be introduced through petrophysical models and approaches to correlating geophysical and hydrologic parameters. With HYTEB we study 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. 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 regularization. For purely hydrologic inversion (HI, only using hydrologic data) we used Tikhonov regularization combined with singular value decomposition. For joint hydrogeophysical inversion (JHI) and sequential hydrogeophysical inversion (SHI) the resistivity estimates from TEM are used together with a petrophysical relationship to formulate the regularization term. In all cases, the regularization stabilizes the inversion, but neither the HI nor the JHI objective function could be minimized uniquely. SHI or JHI with regularization based on the use of TEM data produced estimated hydraulic conductivity fields that bear more resemblance to the reference fields than when using HI with Tikhonov regularization. However, for the studied system the resistivities estimated by SHI or JHI must be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. Much of the lack of value of the geophysical data arises from a mistaken faith in the power of the petrophysical model in combination with geophysical data of low sensitivity, thereby propagating geophysical estimation errors into the hydrologic model parameters. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysical data in the 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 a very poor predictor 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 the HYTEB 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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