A Damped Least-Squares Inversion Program for the Interpretation of Schlumberger Sounding Curves

Ekinci, Yunus Levent; Demirci, Alper

doi:10.3923/jas.2008.4070.4078

Cited by 47 publications

(40 citation statements)

References 17 publications

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“…Inverse modeling, in contrast starts with the data and a general principle or a model, in which the model parameters are estimated by minimizing the error/misfit function set up between the data and model parameters (Menke, 1984). During the past decades researchers have made several attempts to solve the resistivity inverse problems (Ghosh, 1971;Zohdy, 1989;Macias et al, 2000;Qady and Ushijima, 2001;Singh et al, 2005;Ekinci and Demirci, 2008). However, these approaches have some common drawbacks.…”

Section: S Maiti Et Al: Inversion Of DC Resistivity Data Of Koyna Rmentioning

confidence: 99%

“…The modeling and interpretation of Direct Current (DC) resistivity sounding in this region, therefore, assume a special significance to understand the inhomogeneous infiltrations of fluids through porous media and geologically weak zones, such as faults and fractured zones, near the sub-surface area. The DC resistivity sounding method is one of the most popular methods that has been invariably applied for solving hydrological, geothermal, environmental and engineering problems (Ekinci and Demirci, 2008;Singh et al, 2010). In this method, current is induced directly into the ground through a pair of current electrodes and resulting voltage difference is measured between a pair of potential electrodes.…”

Section: Introductionmentioning

confidence: 99%

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Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Maiti¹,

Gupta²,

Erram³

et al. 2011

Nonlin. Processes Geophys.

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Abstract. Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M = 6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC) vertical electrical sounding (VES) data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD)-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural deCorrespondence to: S. Maiti (saumen maiti2002@yahoo.co.in) tails. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results are quite useful for the interpretation of fractures and lineaments in seismically active region.

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Section: S Maiti Et Al: Inversion Of DC Resistivity Data Of Koyna Rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Maiti¹,

Gupta²,

Erram³

et al. 2011

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

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“…where g = [g i (m)] is the 1D model response calculated by using the well-known Gosh-Koefoed filters for the Schlumberger array [14,21]. Basically, resolving an inverse problem means finding models associated with the minimum of the misfit or the objective function.…”

Section: Inversion Algorithmmentioning

confidence: 99%

“…The common practice is to linearize the misfit function at the vicinity of a starting model and then update the model iteratively until a convergence to an optimum solution is reached [13,14]. In this study, we developed our algorithm [20] to simultaneously invert the VES data sets distributed along a profile.…”

Section: B Introductionmentioning

confidence: 99%

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Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

2015

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Abstract. Vertical electrical sounding (VES) data are usually interpreted in terms of a 1D resistivity model using linearized inversion. The local approach of a non-linear inverse problem has fundamental limitations, i.e. the necessity of a starting model close to the solution and possible convergence to a local rather than a global minimum solution. We studied the application of a global search approach for non-linear inversion using the guided random search method to model VES data. A quasi-2D resistivity model can be created by stitching 1D models obtained from VES data along a profile. Both vertical and lateral resistivity variations are minimized to incorporate a 2D smoothness constraint. The proposed method was applied to invert synthetic VES data as well as field data from a sedimentary environment. Both synthetic and field data inversions resulted in models that correlated well with the known synthetic model and with the geology of the study area, respectively.

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Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach

Maiti

Gupta

2020

Springer Geophysics

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A Damped Least-Squares Inversion Program for the Interpretation of Schlumberger Sounding Curves

Cited by 47 publications

References 17 publications

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach

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