Interpretation of Self-potential Anomalies over Two-dimensional Plates by Gradient Analysis

Abdelrahman, E. M.; Hassaneen, A. Gh.; Hafez, Mahfooz

doi:10.1007/s000240050177

Cited by 10 publications

(3 citation statements)

References 6 publications

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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: 98%

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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Section: Introductionmentioning

confidence: 98%

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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“…There are quantitative methods used to determine the parameters of a polarized structure assuming a model with simple geometry. There are available various graphical and numerical methods developed to interpret SP anomalies, including curve matching [4], [14], [15], characteristic points [16]- [18], least squares [19]- [21], derivative and gradient analysis [22], [23], [24], nonlinear modeling [25]- [27], simple iterative [28], singular value decomposition [29], neural networks [30], genetic algorithm [31], particle swarm optimization [32], [33], Whale optimization [34], and Fourier analysis techniques [35], [36]. Since the ore bodies of metallic sulfides and graphites which are found in nature as veins, they could be approximated to two dimensional simple geometric models, they may be considered as two dimensional sheets (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Inverse solution of the SP anomalies of two dimensional sheet like bodies

Sarı¹,

Timur²

2020

Pamukkale J Eng Sci

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Usage of the least squares and inversion methods are commonly applied to the geophysical data analysis. Solution of the theoretical anomalies of inclined sheet like bodies for the self-potential method were compared by writing a Fortran based computer program which is using simple iterative methods with damped least squares (Marquardt-Levenberg) algorithm. As a result of theoretical model studies, model parameters have been reached with very little number of iterations at the small error limits. Applied Marquardt-Levenberg method damping factor has been carried out automatically in the program depending on converging and non-converging conditions. Depth, horizontal length and starting point ( 0 ) parameters of the inclined sheet model were obtained within low error limits compared with the iteration methods for model and real field data. En küçük kareler ve ters çözüm yöntemlerinin kullanımı jeofizik veri analizinde yaygın olarak uygulanmaktadır. Eğilimli tabaka benzeri cisimlerin kuramsal anomalilerinin doğal potansiyel yöntemi için çözümü, basit yinelemeli yöntemler ve sönümlü en küçük kareler (Marquardt-Levenberg) yöntemi kullanılarak Fortran tabanlı bir bilgisayar programı yazılarak karşılaştırılmıştır. Kuramsal model çalışmaları sonucunda, küçük hata limitlerinde çok az sayıda yineleme ile model parametrelere ulaşılmıştır. Uygulanan Marquardt-Levenberg yöntemi sönümleme faktörü programda, yakınsak ve yakınsak olmayan koşullara bağlı olarak otomatik olarak gerçekleştirilmiştir. Eğimli levha modelinin derinlik, yatay uzunluk ve başlangıç noktası ( 0 ) parametreleri, model ve gerçek alan verileri için iterasyon yöntemleri ile karşılaştırıldığında düşük hata sınırları içinde elde edilmiştir.

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“…. Several numerical approaches has been applied to interpret SP anomalies including least square methods [19]-21], spectral analyzes [22]- [24], derivative and gradient analyzes [25], [26], moving average methods [27], [28], and global optimization methods, for example: particle swarm optimization (PSO) [29], [30], genetic algorithm (GA) [17], [30], simulated annealing (SA) [31], [30], differential evolution algorithm (DEA) [32], and black hole algorithm (BHA) [13]. These approaches are applied by researchers to obtain the suitable method which is able to find global optimum solution because the SP data inversion is a highly nonlinear inversion problem.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

Ramadhani

Sungkono

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem.

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Interpretation of Self-potential Anomalies over Two-dimensional Plates by Gradient Analysis

Cited by 10 publications

References 6 publications

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

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

Inverse solution of the SP anomalies of two dimensional sheet like bodies

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

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