Mansour A. Al-Garni

Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks

The modular neural network (MNN) inversion method has been used for inversion of self-potential (SP) data anomalies caused by 2D inclined sheets of infinite horizontal extent. The analysed parameters are the depth (h), the half-width (a), the inclination (α), the zero distance from the origin (xo) and the polarization amplitude (k). The MNN inversion has been first tested on a synthetic example and then applied to two field examples from the Surda area of Rakha mines, India, and Kalava fault zone, India. The effect of random noise has been studied, and the technique showed satisfactory results. The inversion results show good agreement with the measured field data compared with other inversion techniques in use.

On the application of GPR for locating underground utilities in urban areas

With the rapid growth of complex network of different types of underground utility under large cities, the need of a noninvasive technique capable of swiftly and precisely detecting these utilities in such a noisy urban environment increases. Ground-penetrating radar (GPR) is considered one of the most promising techniques in this field. This study presents the experience of GPR data acquisition, processing, and interpretation in three cities located along the coast of the Red Sea. These cities are Jeddah in Saudi Arabia and Sharm El-Sheikh and Qusier in Egypt. Data acquisition parameters varied in the three cities based on site conditions, target characteristics, and equipment availability. The processing flows were kept simple to avoid introducing artifacts to the collected data. The results show that despite the difference in site conditions and survey parameters among the three cities, with the exception of fiber optic cable, GPR technique is capable of detecting different kinds of underground utilities and precisely determine the extension, diameter, and depth of burial of these utilities.

Inversion of residual gravity anomalies using neural network

A new approach is presented in order to interpret residual gravity anomalies from simple geometrically shaped bodies such as horizontal cylinder, vertical cylinder, and sphere. This approach is mainly based on using modular neural network (MNN) inversion for estimating the shape factor, the depth, and the amplitude coefficient. The sigmoid function has been used as an activation function in the MNN inversion. The new approach has been tested first on synthetic data from different models using only one well-trained network. The results of this approach show that the parameter values estimated by the modular inversion are almost identical to the true parameters. Furthermore, the noise analysis has been examined where the results of the inversion produce satisfactory results up to 10% of white Gaussian noise. The reliability of this approach is demonstrated through two published real gravity field anomalies taken over a chromite deposit in Camaguey province, Cuba and over sulfide ore body, Nornada, Quebec, Canada. A comparable and acceptable agreement is obtained between the results derived by the MNN inversion method and those deduced by other interpretation methods. Furthermore, the depth obtained by the proposed technique is found to be very close to that obtained by drilling information.

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

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.

Mathematical analysis of gravity anomalies due to an infinite sheet-like structure

Gravity anomalies caused by a thin infinite sheet are interpreted quantitatively based on an integral transform approach which is elegant and simple. The proposed technique depends on the modified Hilbert transform which is called “Sundararajan transform.” The amplitudes of the well-known Hilbert transform and Sundararajan transform are exactly the same but with a phase difference of 270° between them. The interpretation of gravity anomalies due to a thin infinite sheet has been implemented using the Sundararajan transform rather than the well-known Hilbert transform, yielding a straightforward solution. Parameters such as the depth to the top of the sheet (z), the inclination angle (θ), and the amplitude coefficient (K) have been analytically determined using simple mathematical equations. The origin of the causative target can be determined by the intersection point between Hilbert and Sundararajan transforms as well as the intersection point of the amplitudes of the analytic signal of the two transforms. The proposed technique has been first applied to synthetic data where the procedures are clearly illustrated. The effect of noise on the interpretation procedures of the proposed technique has been investigated, showing in general satisfactory results especially depth estimation. However, the most sensitive parameter to noise is the dipping angle, which can be misleading in high level of noise, whereas the least sensitive parameter is the depth. Strictly speaking, the noise does not significantly distort the depth estimation obtained with this proposed technique. Finally, the interpretation of the gravity anomaly across the Mobrun ore body, Noranda, Quebec, Canada, has been carried out using the proposed technique where the parameters are estimated and compared to the results that have been published in literatures using different techniques.

Inversion of magnetic anomalies due to isolated thin dike-like sources using artificial neural networks

A new approach is proposed to interpret magnetic anomalies caused by isolated thin dike-like causative targets. The approach is essentially based on utilizing artificial neural network (ANN) inversion for estimating the problem parameters. Particularly, the modular neural network (MNN) is used for the inversion process in order to quantitatively interpret the magnetic anomalies. The MNN inversion has been first tested on a synthetic data with and without random white Gaussian noise. The effect of random noise has been clearly investigated where it showed that the approach provided satisfactory results. Furthermore, three field examples have been inverted in order to investigate the applicability of the proposed approach. The results showed good agreement with the techniques that have been stated in the literatures.

Analysis of subsurface magnetic features of the southeast of Al-Muwayh quadrangle, Saudi Arabia, using aeromagnetic data

Aeromagnetic data have been utilized to investigate the subsurface features of the southeast of Al-Muwayh quadrangle. Several techniques have been comprehensively used in an integrative way to reach the goals. Local phase and normalized standard deviation filters are used in this study as edge detectors, showing the possible occurrences of structural lineaments/faults in the quadrangle. Magnitude magnetic transform filters are used to produce anomalies that are closer to the true horizontal position of magnetic sources to enhance the interpretation. Among these transforms, a transform which has been used as edge detectors and the other two transforms are used to show the shallow and the shallowest magnetic sources within the study area. Tilt angle is mainly used to delineate the main magnetic contacts (faults), their locations, and their expected depths. The integration between these different filters show clearly the possible occurrences of edges (contacts/faults), the direction of these lineaments, the source locations of magnetic anomalies, the shallow and the shallowest causative targets, and the location and the depths of the main faults deduced from the tilt angle approach.

Hartley spectral analysis of self-potential anomalies caused by a 2-D horizontal circular cylinder

The real spectral analysis of SP anomalies due to 2-D horizontal circular cylinder is carried out using Hartley transform. The Hartley transform is an alternate means of realizing spectral analysis in real domain unlike the Fourier spectral analysis. The Hartley transform yields a straightforward interpretation of SP anomalies caused by horizontal circular cylinder wherein all the parameters are derived independently as a function of frequency. A theoretical example illustrates the procedure. The effect of random noise, up to 10% of white Gaussian noise, on the interpretation scheme was also studied and found to be of negligible. The field example of the “Sulleymonkey” anomaly in the Ergoni copper district, Turkey exemplifies the applicability of the proposed method.