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

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>

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.

Hydraulic conductivity explored by factor analysis of borehole geophysical data

2015 ◽  
Vol 23 (5) ◽  
pp. 869-882 ◽  
Author(s):  
Norbert Péter Szabó
Keyword(s):  
Factor Analysis ◽  
Hydraulic Conductivity ◽  
Geophysical Data

scholarly journals Structure-coupled joint inversion of geophysical and hydrological data

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. ID1-ID14 ◽  
Author(s):  
Tobias Lochbühler ◽  
Joseph Doetsch ◽  
Ralf Brauchler ◽  
Niklas Linde
Keyword(s):  
Hydraulic Conductivity ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Geophysical Data ◽  
Data Sets ◽  
Northern Switzerland ◽  
Hydrological Data ◽  
Spatial Coverage ◽  
Geophysical Imaging ◽  
Constraints Model

In groundwater hydrology, geophysical imaging holds considerable promise for improving parameter estimation, due to the generally high resolution and spatial coverage of geophysical data. However, inversion of geophysical data alone cannot unveil the distribution of hydraulic conductivity. Jointly inverting geophysical and hydrological data allows us to benefit from the advantages of geophysical imaging and, at the same time, recover the hydrological parameters of interest. We have applied a coupling strategy between geophysical and hydrological models that is based on structural similarity constraints. Model combinations, for which the spatial gradients of the inferred parameter fields are not aligned in parallel, are penalized in the inversion. This structural coupling does not require introducing a potentially weak, unknown, and nonstationary petrophysical relation to link the models. The method was first tested on synthetic data sets and then applied to two combinations of geophysical/hydrological data sets from a saturated gravel aquifer in northern Switzerland. Crosshole ground-penetrating radar (GPR) traveltimes were jointly inverted with hydraulic tomography data, as well as with tracer mean arrival times, to retrieve the 2D distribution of GPR velocities and hydraulic conductivities. In the synthetic case, incorporating the GPR data through a joint inversion framework improved the resolution and localization properties of the estimated hydraulic conductivity field. For the field study, recovered hydraulic conductivities were in general agreement with flowmeter data.

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

Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data

2019 ◽  
Vol 578 ◽  
pp. 124092 ◽  
Author(s):  
Xueyuan Kang ◽  
Xiaoqing Shi ◽  
André Revil ◽  
Zhendan Cao ◽  
Liangping Li ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Time Lapse ◽  
Geophysical Data ◽  
Non Gaussian

A New Approach to Predict Hydrogeological Parameters Using Shear Waves from the Multichannel Analysis of Surface Waves Method

2021 ◽  
Vol 26 (3) ◽  
pp. 195-208
Author(s):  
Antonio E. Cameron ◽  
Camelia C. Knapp
Keyword(s):  
Electrical Resistivity ◽  
Surface Wave ◽  
Hydraulic Conductivity ◽  
Seismic Reflection ◽  
Geophysical Data ◽  
Design Parameters ◽  
Hydraulic Parameters ◽  
Contaminant Plume ◽  
Near Surface ◽  
Hydrogeological Parameters

For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( Vs) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated Vs and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.

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