scholarly journals Processing of synthetic data for the treatment of geoelectric information for hydrogeological purposes of the Málaga town, Santander (Colombia)

2019 ◽  
Vol 41 (3) ◽  
pp. 145-161
Author(s):  
Jesús Leonardo Rincón-Jaimes ◽  
Carlos Alberto Ríos-Reyes ◽  
Carlos Alberto Chacón-Ávila ◽  
Luis Eduardo Moreno-Torres
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Synthetic Data ◽  
Point Of View ◽  
Optimal Conditions ◽  
Resistivity Tomography ◽  
Fractured Aquifers ◽  
Vertical Electric Sounding ◽  
Data Acquisition And Processing ◽  
Geophysical Prospection

Málaga town presents a serious problem caused by the insufficient environmental offer of the superficial water resource to supply the dotation corresponding to the needs of the urban area. Although the geographic and geological location generates optimal conditions for the occurrence of groundwater, studies on the existence, location and availability are incomplete and fragmented, and in general, there is no information about the hidryc resource. The main objective of the present paper pretends to build through the applied geophysical prospection Vertical Electric Sounding (VES) and Electrical Resistivity Tomography (ERT) the 2D model of electrical resistivity of the subsurface, from the processing of synthetic data to the treatment of geoelectrical information with hydrogeological purposes. The data acquisition and processing allowed the interpretation, from the geoelectric point of view and the basic analysis of the 1D profiles and the 2D models developed; demonstrating the presence of highly fractured aquifers that can provide the municipal supply in the future; results that establish the first step in the evaluation of Málaga’s water resources

scholarly journals CONTRIBUTION OF DEEP ELECTRICAL RESISTIVITY TOMOGRAPHY TECHNIQUE TO HYDROGEOLOGICAL STUDIES: CASES FROM AREAS IN KAVALA (NORTH GREECE)

2017 ◽  
Vol 43 (4) ◽  
pp. 1962
Author(s):  
G. Vargemezis ◽  
P. Tsourlos ◽  
I. Mertzanides
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Geological Structure ◽  
Resistivity Tomography ◽  
Near Surface ◽  
Resistivity Method ◽  
Area Of Interest ◽  
Vertical Electric Sounding ◽  
Hydrogeological Studies ◽  
2D And 3D

The most common geophysical method widely used in hydrogeological surveys concerning deep investigations (150-300m of depth) is the resistivity method and particularly the Vertical Electric Sounding (VES) using the Schlumberger array. VES interpretations assume 1D geoelectrical structure yet it is obvious that such an interpretation assumption is not valid in many cases where 2D and 3D geological features exist. In such cases the application of geoelectrical techniques which can provide both vertical and lateral information concerning the resistivity variations is required. Techniques such as the electrical resistivity tomography, mostly used for the 2D and 3D geoelectrical mapping of near surface applications can be adapted to be used for larger investigation depths provided that modified equipment (viz. cables) is used. In the present paper, the application of deep electrical resistivity tomography (ERT) techniques is applied. ERT array of 21 electrodes, at a distance of 50 meters between them (total length 1000 meters) has been used in several studied areas located in the prefecture of Kavala (North Greece). In several cases near surface structure has been compared with VLF data. The aim of the survey was to study in detail the geological-hydrogeological structure the area of interest in order to suggest the best location for the construction of hydrowells with the most promising results. The 2D images of the geological structure down to the depth of at least 200 meters allowed the better understanding of the behaviour of layered geological formations, since in several cases resistivity values have been calibrated with data from pre-existing boreholes.

scholarly journals Ensemble-Based Electrical Resistivity Tomography with Data and Model Space Compression

2021 ◽  
Author(s):  
Mattia Aleardi ◽  
Alessandro Vinciguerra ◽  
Azadeh Hojat
Keyword(s):  
Electrical Resistivity ◽  
Apparent Resistivity ◽  
Electrical Resistivity Tomography ◽  
Synthetic Data ◽  
Optimization Procedure ◽  
Model Space ◽  
Least Square ◽  
Resistivity Tomography ◽  
Initial Ensemble ◽  
The Difference

AbstractInversion of electrical resistivity tomography (ERT) data is an ill-posed problem that is usually solved through deterministic gradient-based methods. These methods guarantee a fast convergence but hinder accurate assessments of model uncertainties. On the contrary, Markov Chain Monte Carlo (MCMC) algorithms can be employed for accurate uncertainty appraisals, but they remain a formidable computational task due to the many forward model evaluations needed to converge. We present an alternative approach to ERT that not only provides a best-fitting resistivity model but also gives an estimate of the uncertainties affecting the inverse solution. More specifically, the implemented method aims to provide multiple realizations of the resistivity values in the subsurface by iteratively updating an initial ensemble of models based on the difference between the predicted and measured apparent resistivity pseudosections. The initial ensemble is generated using a geostatistical method under the assumption of log-Gaussian distributed resistivity values and a Gaussian variogram model. A finite-element code constitutes the forward operator that maps the resistivity values onto the associated apparent resistivity pseudosection. The optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble-based algorithm that performs a Bayesian updating step at each iteration. The main advantages of the proposed approach are that it can be applied to nonlinear inverse problems, while also providing an ensemble of models from which the uncertainty on the recovered solution can be inferred. The ill-conditioning of the inversion procedure is decreased through a discrete cosine transform reparameterization of both data and model spaces. The implemented method is first validated on synthetic data and then applied to field data. We also compare the proposed method with a deterministic least-square inversion, and with an MCMC algorithm. We show that the ensemble-based inversion estimates resistivity models and associated uncertainties comparable to those yielded by a much more computationally intensive MCMC sampling.

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