Hilbert transform in the interpretation of magnetic anomalies of various components due to a thin infinite dike

Natarajan, Sundararajan; Mohan, N. L.; Raghava, M. S. Vijaya; Rao, Sadasiva M.

doi:10.1007/bf00877453

Cited by 13 publications

(11 citation statements)

References 5 publications

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“…The interpretation procedure based on the vertical and horizontal gradient has been done by Stanley and Green (1976) and Stanley (1977b). Hilbert transform has been used in the interpretation of magnetic anomalies of various components (Mohan et al 1982;Sundararajan et al 1985). 3D Hilbert transform has been utilized in the interpretation of aeromagnetic anomalies (Ofoegbu and Mohan 1990).…”

Section: Introductionmentioning

confidence: 99%

Interpretation of magnetic anomalies due to dipping dikes using neural network inversion

Al‐Garni

2015

Arab J Geosci

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A new approach is proposed for the interpretation of magnetic anomalies caused by dipping dikes. This approach is mainly based on modular neural network inversion for estimating the parameters of dipping dike model. Suitable network training examples and test data have been generated using forward models based on known true parameters. The training procedures adopt supervised learning routine using modular neural networks. The effect of random noise has been examined where the proposed technique showed stability and satisfactory results. The applicability of this technique has been tested on synthetic and field examples data. This technique is particularly applied to two field examples, namely magnetic anomaly over an outcropping quartz dike-like body in Karimnagar area, Andhra Pradesh, India, and Marcona magnetic anomaly, Marcona district, Peru. The results of using this technique showed good agreement with the measured field data compared with most conventional ones. Furthermore, neural networks proved to be efficient and flexible in the interpretation of magnetic anomaly of dipping dike.

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Section: Introductionmentioning

confidence: 99%

Interpretation of magnetic anomalies due to dipping dikes using neural network inversion

Al‐Garni

2015

Arab J Geosci

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“…Mohan et al (1990) used the Mellin transform in interpreting magnetic anomalies due to some two dimensional bodies [20]. Sundararajan et al (1985Sundararajan et al ( , 1989 interpreted the magnetic anomalies of various components due to thin infinite dyke and spherical source by using Hilbert transform ( [33], [34]). Srinivas (1998) used the modified Hilbert transform to interpret magnetic anomalies caused by 2-D horizontal circular cylindrical structures [31].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Anomalies Due To 2-D Cylindrical Structures - An Artificial Neural Network Based Inversion

Mamidala¹,

Narasimman²

2019

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ) and the constant term (A) comprising the radius(R) and the intensity of the magnetic field (I). The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data. It is noted that the method under discussion ensures fairly accurate results even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.

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“…Mohan et al (1990) used the Mellin transform in interpreting magnetic anomalies due to some two dimensional bodies [21]. Sundararajan et al (1985Sundararajan et al ( , 1989 interpreted the magnetic anomalies of various components due to thin infinite dyke and spherical source by using Hilbert transform( [34], [35]). Srinivas(1998) used the modified Hilbert transform to interpret magnetic anomalies caused by 2-D horizontal circular cylindrical structures [32].During 1999-2013, different methods Wavelet transform ([22], [16]), Displacement of the maximum and minimum by upward continuation [9], Euler deconvolution [12], Fraser filter [5], Hartley transform [18] and Direct analytic signal [6] were used for inversion of magnetic data.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Magnetic Anomalies Due to 2-D Cylindrical Structures – By an Artificial Neural Network

Mamidala¹,

Narasimman²

2019

IJSC

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Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data. It is noted that the method under discussion ensures fairly accurate results even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between the measured and predicted parameters.

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Hilbert transform in the interpretation of magnetic anomalies of various components due to a thin infinite dike

Cited by 13 publications

References 5 publications

Interpretation of magnetic anomalies due to dipping dikes using neural network inversion

Interpretation of magnetic anomalies due to dipping dikes using neural network inversion

Magnetic Anomalies Due To 2-D Cylindrical Structures - An Artificial Neural Network Based Inversion

Inversion of Magnetic Anomalies Due to 2-D Cylindrical Structures – By an Artificial Neural Network

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