Quantitative interpretation of self‐potential anomalies due to two‐dimensional sheet‐like bodies

Rao, D. Atchuta; Babu, H. V. Ram

doi:10.1190/1.1441446

Cited by 64 publications

(28 citation statements)

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“…The SP anomaly (mV) due to an inclined 2D sheet-like structure ( Figure 1) at a point P(x) along a profile normal to the strike of the structure is given by the following formula (Jagannadha Rao et al 1993;Rao and Babu 1983;Sharma and Biswas 2013):…”

Section: Formulation Of the Forward Modeling (Direct) Solutionmentioning

confidence: 99%

“…where r 1 and r 2 are the distances from the top and bottom edges of the sheet to the observation point (m), respectively, and k (k = Iρ/2π , where I is the current density and ρ is the resistivity of the surrounding medium) is the amplitude coefficient (mV) (Jagannadha Rao et al 1993;Rao and Babu 1983). Sharma and Biswas (2013) formulated the forward solution (1) in terms of the depth to the center of the sheet (z) as…”

Section: Formulation Of the Forward Modeling (Direct) Solutionmentioning

confidence: 99%

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Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes

Mehanee

2015

Earth Planets Space

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This paper describes a new method for tracing paleo-shear zones of the continental crust by self-potential (SP) data inversion. The method falls within the deterministic inversion framework, and it is exclusively applicable for the interpretation of the SP anomalies measured along a profile over sheet-type structures such as conductive thin films of interconnected graphite precipitations formed on shear planes. The inverse method fits a residual SP anomaly by a single thin sheet and recovers the characteristic parameters (depth to the top h, extension in depth a, amplitude coefficient k, and amount and direction of dip θ) of the sheet. This method minimizes an objective functional in the space of the logarithmed and non-logarithmed model parameters (log(h), log(a), log(k), and θ) successively by the steepest descent (SD) and Gauss-Newton (GN) techniques in order to essentially maintain the stability and convergence of this inverse method. Prior to applying the method to real data, its accuracy, convergence, and stability are successfully verified on numerical examples with and without noise. The method is then applied to SP profiles from the German Continental Deep Drilling Program (Kontinentales Tiefbohrprogramm der Bundesrepublik Deutschla -KTB), Rittsteig, and Grossensees sites in Germany for tracing paleo-shear planes coated with graphitic deposits. The comparisons of geologic sections constructed in this paper (based on the proposed deterministic approach) against the existing published interpretations (obtained based on trial-and-error modeling) for the SP data of the KTB and Rittsteig sites have revealed that the deterministic approach suggests some new details that are of some geological significance. The findings of the proposed inverse scheme are supported by available drilling and other geophysical data. Furthermore, the real SP data of the Grossensees site have been interpreted (apparently for the first time ever) by the deterministic inverse scheme from which interpretive geologic cross sections are suggested. The computational efficiency, analysis of the numerical examples investigated, and comparisons of the real data inverted here have demonstrated that the developed deterministic approach is advantageous to the existing interpretation methods, and it is suitable for meaningful interpretation of SP data acquired elsewhere over graphitic occurrences on fault planes.

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Section: Formulation Of the Forward Modeling (Direct) Solutionmentioning

confidence: 99%

Section: Formulation Of the Forward Modeling (Direct) Solutionmentioning

confidence: 99%

Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes

Mehanee

2015

Earth Planets Space

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“…Several methods have been proposed and discussed by many authors for interpreting the self-potential anomalies as a result of a two-dimensional inclined sheet, including, for example, logarithmic curve matching (Meiser 1962;Murty and Haricharan 1984), characteristic distances, points and curves approaches (Rao et al 1970;Paul 1965;Atchuta Rao and Ram Babu 1983), and nomograms (Ram Babu and Atchuta Rao 1988;Satyanarayana Murty and Haricharan 1985) Recently, Abdelrahman et al (1998Abdelrahman et al ( , 1999Abdelrahman et al ( , 2001) introduced the horizontal self-potential gradient and least squares approaches to interpret the self-potential anomaly due to a two-dimensional inclined sheet. The advantage of the proposed method over the previous techniques, which uses a few points, distances, and nomograms, is that all observed data can be used.…”

Section: Introductionmentioning

confidence: 99%

طريقة جديدة لتفسير شذات الجهد الذاتى الناشئة عن صفيحة صخرية مائلة ثنائية الأبعاد باستخدام تحليل التدرج العددى المركب

Hafez

Abbas

2009

Arab J Geosci

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A new approach is developed to determine the model parameters of a two-dimensional inclined sheet from self-potential anomaly. In this method, the numerical horizontal self-potential gradient obtained from self-potential anomaly is convolved using Hilbert transform to obtain the vertical self-potential gradient. The complex gradient is the sum of horizontal and vertical gradient anomalies. The horizontal and vertical gradients are plotted in one graph to form the complex gradient graph. By defining few characteristic points and distances along the complex gradient profile, procedures are then formulated using the analytical functions of the complex gradients to obtain the model parameters of sheet-like structures. The validity of the new proposed method has been tested on synthetic data with and without random noise. The obtained parameters are in congruence with the model parameters when using noise-free synthetic data. After adding 10% random error in the synthetic data, the maximum error in model parameters is 11.8%. Moreover, the method have been applied to analyze and interpret the self-potential anomaly measured on a graphite ore body at southern Bavarian woods, Germany to prove its efficiency where an acceptable agreement has been noticed between the obtained results and the other published results.

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“…These were developed by Yüngül (1950); Paul (1965); Paul et al (1965); Rao et al . (1970); Bhattacharya and Roy (1981) and Atchuta Rao & Ram Babu (1983). The logarithmic curve matching were developed by Meiser (1962);and Murty and Haricharan, (1985).…”

Section: Introductionmentioning

confidence: 99%

Self-Potential Inversion Using Genetic Algorithm

Abdelazeem¹,

Gobashy

2006

ear

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Abstract.A new technique is presented for automatic inversion of SP anomalies due to a polarized infinitely conductive simple geometrical source model structures, based on an artificial intelligence search strategy entitled genetic algorithm (GA) and the evolution theory. The genetic algorithm is used to find the minimum of fitness or cost function of the unknown depth, polarization angle, and shape factor. The study of synthetic examples shows fast and stable recovery of the true parameters even if the input data contain different percentages of noise. The GA leads to very realistic values for the inverted parameters in all tested examples and the root-mean squared error (rms) between the true and inverted field is accepted. The technique is further applied to real examples from Germany and Turkey.

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Quantitative interpretation of self‐potential anomalies due to two‐dimensional sheet‐like bodies

Cited by 64 publications

References 0 publications

Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes

Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes

طريقة جديدة لتفسير شذات الجهد الذاتى الناشئة عن صفيحة صخرية مائلة ثنائية الأبعاد باستخدام تحليل التدرج العددى المركب

Self-Potential Inversion Using Genetic Algorithm

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