A rapid graphical method for the interpretation of the self‐potential anomaly over a two‐dimensional inclined sheet of finite depth extent

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

doi:10.1190/1.1442551

Cited by 35 publications

(14 citation statements)

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“…The methods include, for example, those defined by YUNGUL (1950), BANERJEE (1971), FITTERMAN (1979), BHATTACHARYA and ROY (1981), ATCHUTA RAO and RAM BABU (1983), RAM BABU and ATCHUTA RAO (1988), ABDELRAHMAN and SHARAFELDIN (1997), and ABDELRAHMAN et al (1997aABDELRAHMAN et al ( ,b, 1998. However, the accuracy of the result obtained by most of these methods depends upon the accuracy to which the residual anomaly can be separated from the observed SP data.…”

Section: Introductionmentioning

confidence: 99%

A Least-squares Approach to Depth Determination from Numerical Horizontal Self-potential Gradients

Abdelrahman

Saber

Essa

et al. 2004

Pure and Applied Geophysics

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We have developed a least-squares minimization approach to depth determination of a buried ore deposit from numerical horizontal gradients obtained from self-potential (SP) data using filters of successive window lengths (graticule spacings). The problem of depth determination from SP gradients has been transformed into the problem of finding a solution to a nonlinear equation of the form f ðzÞ ¼ 0. Formulas have been derived for vertical and horizontal cylinders and spheres. Procedures are also formulated to estimate the electrical dipole moment and the polarization angle. The method is applied to synthetic data with and without random noise. Finally, the validity of the method is tested on two field examples. In both cases, the depth obtained is found to be in a very good agreement with that obtained from drilling information.

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Section: Introductionmentioning

confidence: 99%

A Least-squares Approach to Depth Determination from Numerical Horizontal Self-potential Gradients

Abdelrahman

Saber

Essa

et al. 2004

Pure and Applied Geophysics

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“…The methods include, for examples, use of characteristic points, distances, curves and nomograms (YUNGUL, 1950;BANERJEE, 1971;FITTERMAN, 1979;BHATTACHARYA and ROY, 1981;ATCHUTA RAO and RAM BABU, 1983;RAM BABU and ATCHUTA RAO, 1988), least-squares techniques (ABDELRAHMAN and SHARAFELDIN, 1997;EL-ARABY, 2004, and ABDELRAHMAN et al, 1997a, 2003, 2004, 2006a-b and 2008, Fourier analysis and wave number domain (ATCHUTA RAO et al, 1982;ROY and MOHAN, 1984), window-curves methods (ABDELRAHMAN et al, 1997b(ABDELRAHMAN et al, , 1998(ABDELRAHMAN et al, , 2003(ABDELRAHMAN et al, , and 2009). On the other hand, the drawback with most of the previous graphical and numerical methods is that they cannot determine the four model parameters from all data points of the SP anomaly profile.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Interpretation of Self-Potential Anomalies of Some Simple Geometric Bodies

Abdelrahman

Soliman

Abo-Ezz

et al. 2009

Pure Appl. Geophys.

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We have developed a new numerical method to determine the shape (shape factor), depth, polarization angle, and electric dipole moment of a buried structure from residual self-potential (SP) anomalies. The method is based on defining the anomaly value at the origin and four characteristic points and their corresponding distances on the anomaly profile. The problem of shape determination from residual SP anomaly has been transformed into the problem of finding a solution to a nonlinear equation of the form q = f (q). Knowing the shape, the depth, polarization angle and the electric dipole moment are determined individually using three linear equations. Formulas have been derived for spheres and cylinders. By using all possible combinations of the four characteristic points and their corresponding distances, a procedure is developed for automated determination of the best-fit-model parameters of the buried structure from SP anomalies.The method was applied to synthetic data with 5% random errors and tested on a field example from Colorado. In both cases, the model parameters obtained by the present method, particularly the shape and depth of the buried structures are found in good agreement with the actual ones. The present method has the capability of avoiding highly noisy data points and enforcing the incorporation of points of the least random errors to enhance the interpretation results.

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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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A rapid graphical method for the interpretation of the self‐potential anomaly over a two‐dimensional inclined sheet of finite depth extent

Cited by 35 publications

References 0 publications

A Least-squares Approach to Depth Determination from Numerical Horizontal Self-potential Gradients

A Least-squares Approach to Depth Determination from Numerical Horizontal Self-potential Gradients

Quantitative Interpretation of Self-Potential Anomalies of Some Simple Geometric Bodies

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

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