Automatic 1D interpretation of DC resistivity sounding data

Muiuane, Elônio A.; Pedersen, Laust B.

doi:10.1016/s0926-9851(99)00015-4

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…One of its merits is that the inversion is fully automatic in the sense that the inversion parameters, such as the truncation level of the smallest singular values, the number of iterations, etc., are set intrinsically on the basis of the data alone. This is a great advantage over other inversion schemes such as those of Scales (1996) and Muiuane and Pedersen (1999). Another characteristic feature of the present inversion scheme is that the discarded smallest eigenvalues are not ignored completely and are used in the overall optimization of the inverse problem, in contrast to other inversion schemes.…”

Section: Discussionmentioning

confidence: 99%

“…In this inversion scheme the minimization of Q is performed automatically in the sense that no bounds of Q are defined, in contrast to other inversion schemes (e.g. Muiuane and Pedersen 1999) where lower and upper limits of Q are set by the user.…”

Section: The Final Modelmentioning

confidence: 99%

“…Many DC resistivity inversion schemes use a combination of iterative least‐squares procedures with truncated singular value decomposition (TSVD) to optimize the solution, whereby the truncation level is usually set arbitrarily or by a trial‐and‐error procedure (e.g. Scales 1996; Muiuane and Pedersen 1999). However, the credibility of such procedures has been affected by the lack of workable criteria to determine the cut‐off level of the small singular values in the SVD.…”

Section: Introductionmentioning

confidence: 99%

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1D inversion of DC resistivity data using a quality‐based truncated SVD

Muiuane

Pedersen

2001

Geophysical Prospecting

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Many DC resistivity inversion schemes use a combination of standard iterative least‐squares and truncated singular value decomposition (SVD) to optimize the solution to the inverse problem. However, until quite recently, the truncation was done arbitrarily or by a trial‐and‐error procedure, due to the lack of workable guidance criteria for discarding small singular values. In this paper we present an inversion scheme which adopts a truncation criterion based on the optimization of the total model variance. This consists of two terms: (i) the term associated with the variance of statistically significant principal components, i.e. the standard model estimate variance, and (ii) the term associated with statistically insignificant principal components of the solution, i.e. the variance of the bias term. As an initial model for the start of iterations, we use a multilayered homogeneous half‐space whose layer thicknesses increase logarithmically with depth to take into account the decrease of the resolution of the DC resistivity technique with depth. The present inversion scheme has been tested on synthetic and field data. The results of the tests show that the procedure works well and the convergence process is stable even in the most complicated cases. The fact that the truncation level in the SVD is determined intrinsically in the course of inversion proves to be a major advantage over other inversion schemes where it is set by the user.

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Section: Discussionmentioning

confidence: 99%

Section: The Final Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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1D inversion of DC resistivity data using a quality‐based truncated SVD

Muiuane

Pedersen

2001

Geophysical Prospecting

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“…As a consequence, many resistivity surveys are still being done with 1D resistivity or VES inversion still playing an important role for initial analysis in field applications. When the resistivity changes are smooth, 1D resistivity surveys can easily be used to interpret the data [1].…”

Section: Introductionmentioning

confidence: 99%

“…For the 1D resistivity inversion problem, many authors employ linear inversion such as Marquradt's algorithm [2], iterative least square procedure (ILSQP) with singular value decomposition (SVD) [1], least square method with ridge regression [3], and Occam's inversion algorithm [4]. These linearized methods are considered as an acceptable and computational effective solution when some robust a priori information and good starting models are available.…”

Section: Introductionmentioning

confidence: 99%

Developed Forward and Inverse Modelling of Vertical Electrical Sounding (VES) Using MATLAB Implementation

2015

IJSR

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Abstract:We have implemented a MATLAB source code for VES forward modeling and its inversion using a genetic algorithm (GA) optimization technique. The codes presented here are applied to the Schlumberger electrode arrangement. In the forward modeling computation, we have developed code to generate theoretical apparent resistivity curves from a specified layered earth model. The input to this program consists of the number of layers, the layer resistivity and thickness. The output of this program is apparent resistivity versus electrode spacing incorporated in the inversion process as apparent resistivity data. For the inversion, we have developed a MATLAB code to invert (for layer resistivity and thickness) the apparent resistivity data by the genetic algorithm optimization technique. The code also has some function files involving the basic stages in the GA inversion. Our inversion procedure addressed calculates forward solutions from sets of random input, to find the apparent resistivity. Then, it evolves the models by better sets of inputs through processes that imitate natural mating, selection, crossover, and mutation in each generation. The aim of GA inversion is to find the best correlation between model and theoretical apparent resistivity curves. In this study, we present three synthetic examples that demonstrate the effectiveness and usefulness of this program. Our numerical modeling shows that the GA optimization technique can be applied for resolving layer parameters with reasonably low error values.

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Novel Efficient Method for Automatic Inversion of Vertical Electrical Sounding Data: Case Study from Sindhudurg District, Maharashtra, India

Gupta

Maiti

2023

Pure Appl. Geophys.

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Automatic 1D interpretation of DC resistivity sounding data

Cited by 9 publications

References 11 publications

1D inversion of DC resistivity data using a quality‐based truncated SVD

1D inversion of DC resistivity data using a quality‐based truncated SVD

Developed Forward and Inverse Modelling of Vertical Electrical Sounding (VES) Using MATLAB Implementation

Novel Efficient Method for Automatic Inversion of Vertical Electrical Sounding Data: Case Study from Sindhudurg District, Maharashtra, India

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