A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography

2020 ◽  
Author(s):  
Mattia Aleardi ◽  
Alessandro Vinciguerra ◽  
Azadeh Hojat
Keyword(s):  
Monte Carlo ◽  
Electrical Resistivity ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Electrical Resistivity Tomography ◽  
Inversion Algorithm ◽  
Resistivity Tomography

scholarly journals MONITORING RECYCLED WATER INJECTION INTO A CONFINED AQUIFER IN SINDOS (THESSALONIKI) USING ELECTRICAL RESISTIVITY TOMOGRAPHY (ERT): INSTALLATION AND PRELIMINARY RESULTS

2007 ◽  
Vol 40 (2) ◽  
pp. 580 ◽  
Author(s):  
P. Tsourlos ◽  
G. Vargemezis ◽  
C. Voudouris ◽  
T. Spachos ◽  
A. Stampolidis
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Low Cost ◽  
Water Injection ◽  
General Setting ◽  
Confined Aquifer ◽  
Inversion Algorithm ◽  
Recycled Water ◽  
Resistivity Tomography ◽  
Spatial Coverage

This work describes the installation and preliminary measurements of an electrical resistivity tomography (ERT) system to monitor the injection of recycled water into a confined aquifer in the area of Sindos. The aim is to provide, through time-lapse electrical resistivity tomography (ERT) measurements and processing, geoelectrical images of rather increased volumetric sampling around and between the holes and to obtain improved understanding of the flow and transport of the injected water. The details about the general setting and the design of the injection utility are presented and the construction and installation of the ERT cables into the boreholes are explained in full. Preliminary measurements involving single-hole ERT measurements were obtained and processed with a 2D inversion algorithm to produce images of the subsurface. Results depict a very good correlation between ERT images and the lithology logs an indication of the reliability of the approach. This images offer increased resolution and spatial coverage compared to traditional approaches. The entire ERT permanent installation is of low cost, easy to implement and can be used to understand and evaluate the effects of the water injection process.

scholarly journals Appraisal on inversion algorithm techniques in 2D electrical resistivity tomography survey data for poised mapping of subsurface features

2020 ◽  
Author(s):  
Abhay Kumar Bharti ◽  
Amar Prakash ◽  
Krishna Kant Kumar Singh
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Electrode Spacing ◽  
Archaeological Survey ◽  
Imaging Data ◽  
Inversion Algorithm ◽  
Data Set ◽  
Resistivity Tomography ◽  
Resistivity Imaging ◽  
L2 Norm

Abstract. Analysis of non-uniqueness model in resistivity imaging data is vital in inaugurating the consistency of models. Nevertheless, such analysis is moderately unusual in resistivity imaging data set. Electrical resistivity tomography (ERT) technique is being constantly used in many scientific areas including engineering, environmental and archaeological survey. Primarily, the inversion algorithm techniques are employed on synthetic model data set with and without some random Gaussian noise, and its validity is tested by filed data set. The study was conducted in the premises of Central Institute of Mining and Fuel Research (CIMFR), Dhanbad by laying an ERT profile of 480 m length with 5 m electrode spacing using Syscal Pro (Iris instrument) resistivity meter. Two standard arrays were used in this study namely Wenner-Schlumberger and dipole-dipole array. The data set was mixed to a single array to achieve better resolution and enhanced clarification. On processing data by Prosys-II software, it was exported in Res2Dinv software for inversion. In this context, data was inverted by different algorithm techniques i.e. least square (L2-norm) and robust inversion (L1-norm). Exemplary results related to the heterogeneity of the resistivity structure within the high and low resistivity anomaly were obtained by robust inversion method. The obtained results are in broad agreement with the simulation model.

Seismic inversion and uncertainty quantification using transdimensional Markov chain Monte Carlo method

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. R321-R334 ◽  
Author(s):  
Dehan Zhu ◽  
Richard Gibson
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Uncertainty Quantification ◽  
Seismic Inversion ◽  
Inversion Algorithm ◽  
Seismic Attribute ◽  
Data Set ◽  
Location Uncertainty ◽  
Parameter Values

We applied a transdimensional stochastic inversion algorithm, reversible jump Markov chain Monte Carlo (rjMCMC), to angle-stack seismic inversion for characterization of reservoir acoustic and shear impedance with uncertainty quantification. The rjMCMC is able to infer the number of parameters for the model as well as the parameter values. In our case, the number of parameters depends on the number of model layers for a given data set. We also use this method in uncertainty quantification because a transdimensional sampling helps prevent underparameterization or strong overparameterization. An ensemble of models with proper parameterization can improve parameter estimation and uncertainty quantification. Our new results in uncertainty analysis indicate that (1) the uncertainty in seismic inversion, including uncertainty in earth properties and their locations, is related to the discontinuity of property across an interface, and (2) there is a trade-off between property uncertainty and location uncertainty. A stronger discontinuity will induce more property uncertainty but less location uncertainty at the discontinuity interface. Therefore, we further use the inversion uncertainty as a novel seismic attribute to assist in delineation of subsurface discontinuity interfaces and quantify the magnitude of the discontinuities, which further facilitates quantitative interpretation and stratigraphic interpretation.

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