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We describe a rapid imaging approach for the interpretation of self-potential data collected along profile by some geometrically simple model of cylinders and spheres. The approach calculates the correlation coefficient between the analytic signal (AS) of the observed self-potential measurements and the AS of the self-potential signature of the idealized model. The depth, electric dipole moment, polarization angle, and center are the inverse parameters we aim to extract from the imaging approach for the interpretative model, and they pertain to the highest value of the correlation coefficient. The approach is demonstrated on noise-free numerical experiments, and reproduced the true model parameters. The accuracy and stability of the proposed approach are examined on numerical experiments contaminated with realistic noise levels and regional fields prior to the interpretation of real data. Following that, five real field examples from geothermal systems and mineral exploration have been successfully analyzed. The results agree well with the published research. [ABSTRACT FROM AUTHOR]

Dykes play a key role in various geologic environments. Here, we show how we employed profile magnetic data to perceive and identify the number of dykes and their parameters by a particle swarm optimiser scheme. The results deduced from this inversion include identification and estimation of the magnetic dipole moment, depth, half-width, magnetisation angle, and origin location (dyke parameters) of dykes. Two synthetic models demonstrate this scheme: the first model tests the impact of a third-order regional anomaly and the second model considers the effect of adjacent structures. Besides, we provide two case studies related to mineral exploration, viz., the Parnaiba basin from Brazil and the Pishabo Lake example from Canada. The subsurface dyke parameters estimated from drilling information and the available literature were consistently matched with those achieved from our inversion scheme, thereby demonstrating the effectiveness of the particle swarm optimisation scheme. [ABSTRACT FROM AUTHOR]

Herein, the gravity anomalies are a function of horizontal variations in subsurface rock densities; therefore, the interpretation of gravity anomalies is useful in prospecting the provinces that have contrasting geological structures, which contain crypts, minerals, ores, and hydrocarbon deposit. Depth-size relationships due to geological considerations in the region are also necessary. This study attempted to develop a new fast inversion algorithm to prospect the gravity anomaly profiles, like evaluating the depth, the amplitude coefficient, and the shape parameter of target structures for simple geometric bodies. This new inverted model parameters approach mainly used the analytical signal of the gravity anomaly profile data. The efficiency and stability of this method were tested by many cases such as noise-free and noisy synthetic cases, multiple model cases effect, and the effect of choosing the location of the origin. In addition, the validity of this method was examined by raw gravity data from three different locations around the world, Slovakia, Cuba, and India. In all the three examined real gravity data, it was reported that the estimated parameters were consistent in an appositive manner with the actual ones and with those reported by the extent of research. The necessary time to discover this proper elucidation was very short and the assessed parameters demonstrated that the new suggested method was applicable for gravity exploration. The study concludes that this method can be extracted parameters, which have a significant association in terms of geologic and economic characteristics. [ABSTRACT FROM AUTHOR]

We have developed a new method for the interpretation of a magnetic anomaly profile by idealized-geometrical bodies using the variance analysis. This method is based on estimating the fourth horizontal derivative of the magnetic data profile. The advantage behind the use of the fourth horizontal derivative method is to reduce the regional background effect. The model parameters estimated are the depth and shape of the buried body using all available window lengths (s-value). Then, the variance value for the calculated depth at each shape value is estimated using all available s-values. The minimum variance is considered as the best criterion for determining the best-inverted parameters (depth and shape). The developed method has been verified on some noise free examples. Following that, the accuracy of the method was assessed by studying the impact of synthetic data with 5%, 10%, and 15% random noise, the effect of the regional background, the influence of interfering structures, and the selection for the wrong origin of the body. The presented method has been successfully applied to three real case studies from Brazil and India from mineral exploration. [ABSTRACT FROM AUTHOR]

A new imaging method has been developed for elucidating the observed magnetic data gauged along profile. The method is based on the calculation of the correlation factor (the R-parameter) between the analytic signal of the measured magnetic anomaly and the analytic signal of the calculated response of some geometrically simple interpretive models in the confined category of sheets, cylinders, and spheres. The characteristic parameters (amplitude coefficient, depth, location, approximative shape of the buried structure, and effective angle of magnetization) of the interpretive model correspond to the maximum R-parameter value. The scheme has been verified on a number of noise-free synthetic examples and recovered the actual model parameters. Prior to applying the developed scheme to real-field examples, the accuracy of it has been carefully investigated on synthetic examples which are contaminated with realistic noise levels, interference effects, and regional field. Finally, the method has been successfully applied to three real-field data examples from the USA, Senegal, and Egypt for mineral exploration, and it is found that the obtained results are in good concordance with those obtained from drilling and/or the published literature. [ABSTRACT FROM AUTHOR]

The self potential data interpretation is very important to delineate and trace the mineralized zones in several regions. We study how to interpret self potential anomalies due to a finite two-dimensional inclined dike using the particle swarm algorithm. However, the precise estimation of the model parameters during the inverse solution are unknown. Here, we show that the particle swarm algorithm is capable of estimating the unknown parameters with acceptable accuracy. The evaluated parameters are the polarization parameter, the depth, the inclination angle, the width, and the location of the source of the target. We found in controlled in free-noise synthetic case that the particle swarm algorithm has a remarkable capability of assessing the parameters. For a noisy case, the results also are very competitive. Furthermore, it is utilized for real mineralized zones examples from Germany and India. Our results demonstrate how the particle swarm algorithm overcomes in trapping in local minimum solutions (undesired) and go faster to the global solutions (desired). Finally, the target parameters estimated are matched with accessible geologic and geophysical information. [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]

This paper describes the use of the particle swarm optimization (PSO) method for interpreting observed self-potential anomalies measured along a profile. First, the technique applies the second moving average to the observed self-potential data in order to eradicate the possible influence of the regional anomaly (up to the third-order polynomial effect) via the filter of consecutive window lengths (s-values) and to calculate the residual anomaly. Following that, the PSO method is applied to the residual response to infer the source parameters: amplitude coefficient (K), depth (z), polarization angle (𝜃) and the shape factor (q) of the underlying buried target. The technique has been applied to three different theoretical and two field examples from the USA and Turkey. Comparisons have shown that the source parameters retrieved from the technique described here are in good agreement with the available geologic and geophysical information. [ABSTRACT FROM AUTHOR]

Geophysical methods, especially the gravity method, are very helpful in ore and mineral explorations. Here, gravity modeling and interpretation for the subsurface geologic structures generally assumes either homogenous or spatially varying densities within target source rocks and surrounding structures. Therefore, the use of simple-geometric bodies helps in the validation of the subsurface ore and mineral targets. A Bat optimization algorithm is a recently developed metaheuristic algorithm that is used in various geophysical applications to explore and explain the parameters of buried ore and mineral targets. Using the Bat optimization algorithm, we were elucidating gravity anomaly profiles for ore and mineral cases. To perform global optimization, the Bat optimization algorithm is based on the echolocation behavior of bats. The global optimum solution in the Bat optimization algorithm reached the suggested minimum value of the objective function. The Bat optimization algorithm is applied to gravity data to estimate the target parameters (e.g., amplitude coefficient, depth, origin location, and geometric shape). The stability and efficiency of the introduced optimizing algorithm have been checked on two synthetic models represented in a spherical model and an infinitely horizontal cylinder model using two different kinds of noise. Furthermore, successful applications of the proposed algorithm for discovering the ore and minerals in Canada, Cuba, and India were presented. The results match well with the available geological and borehole information and other results from the published literature. [ABSTRACT FROM AUTHOR]

This study focuses on interpreting gravity anomalies caused by fault structures. The faults types are the two-sided inclined fault, which represents mainly the normal and reverse faults, the two-sided vertical fault, the one-sided inclined fault, and the one-sided vertical fault. The study scheme is depends on the combination between the second moving average operator as a tool for separating the residual (desired) and regional (undesired) anomalies and then detecting the fault parameters applying the global particle swarm. The stability and efficiency of the proposed method has been applied to a synthetic example including the effect of regional background and to five real data sets from Iraq, USA, Egypt, and France. Available geologic and geophysical information supports our interpretation for the field examples from Iraq, USA, and Egypt. While, the forward model results from the detected parameters for the last field example (Pyrenees faults, France) was compared and matched well with measured gravity anomaly and found in a good agreement. So, this interpretation is open a sight-view for researchers around the world work in this area to use our results as a priori information for more investigations. • Global particle swarm optimization for interpreting fault-like geologic structure. • The proposed method is depends on second moving average operator to remove the regional effect. • This method is tested on synthetic and fields data for oil and mineral exploration. • This method is powerful and robust. [ABSTRACT FROM AUTHOR]

We have developed a scheme for the interpretation of gravity data by a two-sided fault-like geological structure. The scheme is based on the particle swarm optimization and determines the model parameters of the upthrown and downthrown of the fault. It uses the second horizontal derivatives, which are calculated numerically from the measured gravity data by applying some filters of consecutive graticule spacings (the so-called here window lengths or s-values). The developed scheme has been verified on various noise-free synthetic models. Following that, the accuracy of the scheme has been assessed on a number of noisy synthetic examples. Finally, the developed technique has been successfully applied to two real data examples from India and the United States of America. It is found that the model parameters obtained from the scheme developed here are in good agreement with those reported in the published literature. Unlabelled Image • New method is utilized to model two-sided faults based upon particle swarm optimization and second horizontal gradient. • The proposed method is applied to four synthetic models and two field gravity data sets. • The method can be applied in mineral exploration and studying fault hazards. [ABSTRACT FROM AUTHOR]

• The R-parameter imaging technique for interpreting gravity data. • The method is based on the analytic signal of observed and synthetic responses. • The maximum R-parameter value is the criterion in determining the true parameters. • The accuracy of this technique is verified on numerical and real data sets. This paper describes a fast imaging technique, the so-called here the "R-parameter imaging technique", for the interpretation of gravity data measured along profile. The technique calculates the R-parameter (a statistical parameter called the correlation coefficient) between the analytic signal of the measured gravity field and that of the synthetic response of an assumed interpretive model in the restricted class of spheres and cylinders. The characteristic source parameters (depth, shape, origin point of the gravity profile, and amplitude factor) of the model correspond to the maximum R-parameter value. The method has been verified on noise free numerical examples. Following that, the accuracy of the method has been carefully assessed on both noisy numerical examples and numerical examples with interference effects. Finally, the technique has been successfully applied to five field data examples from Canada, Sweden and Cuba for mineral exploration, and it is found that the obtained results are in good agreement with those reported in the published literature. [ABSTRACT FROM AUTHOR]

A global particle swarm algorithm utilized to assess the inverted two-dimensional horizontal thin sheet parameters from the gravity anomaly profile based on applying the second moving average method. The using of the second moving average method has more advantageous than using the Bouguer gravity anomaly because this method has a capability in eliminating the regional field up to third-order impeded in the Bouguer anomaly. This algorithm is applied to interpret the gravity anomaly profile, i.e., estimating the depth, the width, the thickness, the density contrast and the origin location of the buried structure. The efficacy and stability of this method are investigated and exposed for utilizing free-noise and noisy synthetic examples. Real gravity data associated with oil and gas and mineral exploration from three different locations around the world is interpreted. The evaluated time to discovering this appropriate clarification is very short and the estimated parameters contrast the view that the proposed method is applicable for real exploration. It can extract the inverse parameters which have a geologic and economic significance. • A global particle swarm for interpreting gravity data to delineate the parameters of buried geologic structure • This technique relies on second moving average operator to eliminate the impact of the regional and noise in a main field. • This technique applied to synthetic and field examples for oil and mineral exploration. [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]