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The detection of buried geometrical model parameters is vital to full interpretation of potential field data, especially that related to gravity and/or self-potential anomalies. This study introduced a proposed non-linear least-squares algorithm for solving a combined formula for gravity and self-potential anomalies due to simple geometric shapes. This proposed algorithm was relied upon delimiting the origin anomaly value and two symmetric anomaly values with their equivalent distances along with the anomaly profile in order to invert the buried geometry model parameters. After that, a root mean square error (μ-value) for each parameter value at different postulated shape factor was assessed. The μ-value was considered as a benchmark for detecting the true-values of the subsurface geometry structures. The efficacy and rationality of the proposed approach were revealed by numerous synthetic cases with and without random noise. Furthermore, the sensitivity analysis between shape factor and μ-value were investigated on synthetic gravity and self-potential data. It was evident that the inverted parameters were reliable with the genuine ones. This proposed method was tested on samples of gravity data and self-potential data taken from Senegal and USA. To judge the satisfaction of this approach, the results gained were compared with other available geological or geophysical information in the published literature. [ABSTRACT FROM AUTHOR]

This paper emphasizes the use of the particle swarm optimization to infer second moving average residual magnetic anomalies. This approach was used to remove the impact of the regional background up to the third-order polynomial by applying filters of successive window lengths. The body parameters evaluated are the amplitude coefficient, depth, magnetization angle, location of the object, and target shape. After that, the root mean squared error was calculated to compare the measured and calculated magnetic anomalies (the misfit). This proposed technique was utilized for synthetic examples as follows: The first model explains the effect of introducing noise, the second model demonstrates the impact of an interfering structure, and the third model shows the influence of a third-order regional magnetic field. Furthermore, this method was connected to three real examples from diabase dike and chromite ore exploration in Canada, Turkey, and Egypt. In all the cases, the assessed body parameters reasonably approximate the known values. [ABSTRACT FROM AUTHOR]

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of PSO algorithm (particle swarm optimization) has been presented. The PSO algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results. [ABSTRACT FROM AUTHOR]

We have developed a new algorithm for the interpretation of magnetic data measured along a profile by a dipping dike like body. The algorithm is based on the second horizontal gradient (SHG) anomalies calculated from the observed magnetic data using filters of successive window lengths (s). The main merit of this new method is its capability to estimate (successively rather than simultaneously) all the model parameters of the buried anomalous body: depth to top (z), half-width (d), index parameter (α) and amplitude coefficient (K). This technique successively estimates the parameters z, α and K of the anomalous structure at an assumed half-width for various window lengths by solving a nonlinear equation for z and by direct substitution in two consequent formulas in order to infer α and K. The best fitting solution is that for which the root mean square error (RMS) between the observed and predicted data is minimal. The method has been applied to synthetic examples with and without random noise. Following this, it has been applied to three field examples from Canada, China and the United Kingdom which assesses the accuracy of the method. The results were compared against drilling information and published approaches, which highlight the usefulness of the developed approach. [ABSTRACT FROM AUTHOR]

A new accurate technique has been developed to interpret self-potential (SP) data ( d ) measured along a profile by a two-dimensional (2-D) inclined plate-like body. The technique solves for the depth ( z ) to top and half-width ( w ) of the body, and is based on the second horizontal gradient (SHG) that is calculated at a number of window lengths. Using points symmetrically distributed about the origin point of the SP profile, the scheme derives and evaluates a function F at various window lengths from the measured SP anomaly profile. At a fixed window length ( s ), the depth corresponding to an assumed half-width is determined by solving a nonlinear equation in the form φ ( d , s , w , z ) = 0. The depths corresponding to all considered windows are then obtained at the assumed width. The standard deviation of these depths is calculated for this width. This process is repeated for various widths. The final solution (the depth and half-width) of the interpretive buried structure is that which achieves the least standard deviation. The validity and accuracy of the new scheme has been demonstrated on theoretical noise free and noisy SP data. It has been successfully applied to two real data, and it is found that the parameters estimated from the scheme described here are in good agreement with those reported in the published literature. [ABSTRACT FROM AUTHOR]

This paper describes a new method based on the particle swarm optimisation (PSO) technique for interpreting the second moving average (SMA) residual gravity anomalies. The SMA anomalies are deduced from the measured gravity data to eradicate the regional anomaly by utilising filters of consecutive window lengths (s-value). The buried structural parameters are the amplitude factor (A), depth (z), location (d) and shape (q) that are estimated from the PSO method. The discrepancy between the measured and the predictable gravity anomaly is estimated by the root mean square error. The PSO method is applied to two different theoretical and three real data sets from Cuba, Canada and India. The model parameters inferred from the method developed here are compared with the available geological and geophysical information. [ABSTRACT FROM AUTHOR]