Search

An effective extension to the particle swarm optimizer scheme has been developed to visualize and modelize robustly magnetic data acquired across vertical or dipping faults. This method can be applied to magnetic data sets that support various investigations, including mining, fault hazards assessment, and hydrocarbon exploration. The inversion algorithm is established depending on the second horizontal derivative technique and the particle swarm optimizer algorithm and was utilized for multi-source models. Herein, the inversion method is applied to three synthetic models (a dipping fault model contaminated without and with different Gaussian noises levels, a dipping fault model affected by regional anomaly, and a multi-source model) and three real datasets from India, Australia, and Egypt, respectively. The output models confirm the inversion approach's accuracy, applicability, and efficacy. Also, the results obtained from the suggested approach have been correlated with those from other methods published in the literature. [ABSTRACT FROM AUTHOR]

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]

The Bat optimizing algorithm (BOA) is one of the metaheuristic algorithms and applied here to interpret self-potential (SP) data. The BOA is depending upon a bat echolocation behavior for global optimization, which the global optimum solution reached at the suggested minimum value of the objective function. The best interpretive source parameters for the subsurface structures occurred at the minimal the objective function value (global best solution). The BOA is applied to 2D SP anomaly data to estimate the characteristic source parameters (i.e., the depth to center, amplitude coefficient, origin location, geometric shape factor, and polarization and inclination angle of the causative buried structure). The BOA can be applied to single and multiple source structures in the restricted class of simple geometric shapes, which these bodies help in the validation of the subsurface ore and mineral targets. The stability and efficiency of the proposed BOA have been examined by several synthetic examples. In addition, three different real field examples from Germany and Indonesia have been successfully applied to ore and mineral investigation and geological structure studies. In general, the achieved results are in good agreement with the available borehole data and results mentioned in the literature. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results. [ABSTRACT FROM AUTHOR]

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]

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]

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]

We have developed an algorithm to obtain the model parameters for two co-axial structures from selfpotential data. The method uses the first numerical horizontal derivatives calculated from the observed self-potential anomaly employing filters of sequential window lengths (s-values) so as to gauge the model constraints for the shallow and deep structures. In addition, this algorithm uses a standard inversion method for solving a non-linear equation based on the lowest root-mean-square (RMS) error of the estimated model parameters. The body constraints are the depth, polarization angle and electric dipole moment of each structure. Our approach models the self-potential dataset as an aggregation of spheres, horizontal cylinders, and vertical cylinders. These simple bodies are used to approximate, without a priori expectations, the furthermost plausible position and/or area of intersection. In other words, the bodies are used to estimate the true values of the source parameters for the two-co-axial bodies at different s-values. Minimizing the RMS error has the advantage of optimizing all model factors. The proposed technique is tested using a numerical model with and without noise and on self-potential field data acquired at a site in Germany. In all cases, the assessed body parameters are reasonable approximations of the known values. [ABSTRACT FROM AUTHOR]

In this paper, the effectiveness of recently proposed Hunger Games Search (HGS) optimization tool, which simulates behavioural choices of the predators in the animal kingdom, in estimating model parameters of idealized causative sources from Self-Potential (SP) anomalies is presented. Before the parameter estimations, the modal analyses of the model parameter pairs and parameter tuning processes were performed to characterize the optimization problem and to decide the optimal value of the control parameter of HGS algorithm, respectively. Subsequently, the proposed optimizer was applied on some synthetic SP data sets and four anomalies obtained from India, Türkiye, Canada, and Indonesia. Multiple and intercalating causative sources were considered in the synthetic and real data cases, and HGS algorithm produced satisfactory solutions. In the composite anomaly cases a second moving average procedure successfully removed the undesired background effects. Uncertainty appraisal analyses showed the validity of the model parameter estimations. The outputs of HGS optimizer were also compared with those of the well-established and popular standard particle swarm optimization regarding convergence rate, robustness, stability, and accuracy. Applications presented here clearly showed that SP anomalies can be inverted more effectively via HGS since the procedure has the ability to search the predefined model space extensively without being trapped into numerous local minima and approximate the global minimum more precisely. Thus, it is recommended to use the HGS algorithm in model parameter estimation studies using SP anomalies. [ABSTRACT FROM AUTHOR]

A new algorithm has been established to interpret magnetic anomaly data due to inclined dike-like structure. This algorithm uses first horizontal derivative anomalies attained from magnetic anomaly data utilizing filters of sequential window lengths. The final estimated parameters are the half-width, the depth, angle of magnetization and amplitude factor of an inclined dike-like geological structure. A minimum variance criterion is used for selecting the most suitable variables. This algorithm has been realized to theoretical data without and with random noise. The effects of interference due to near structures have additionally been studied. The method was then applied to two field examples from Turkey and Peru, which demonstrate its effectiveness and accurateness. Thus, it is a respectable correspondence among the model parameters retrieved from this approach, drilling information, and the outcomes published in the literature. For example, in Turkey, we applied the technique to gauge the source variables and also the results were precise to w = 64.74 m and h = 87.65 m (2% and 3% errors, respectively) based on information from Aydin and Gelişli (Jeofizik 10:41–49, 1996). [ABSTRACT FROM AUTHOR]

The near-surface groundwater aquifer that threatened the Great Pyramids of Giza, Egypt, was investigated using integrated geophysical surveys. A total of 10 electrical resistivity imaging, 26 shallow seismic refraction, and 19 ground-penetrating radar surveys were conducted in the Giza Plateau. Collected data for each method were evaluated by state-of-the art processing and modeling techniques. A three-layer model depicts the subsurface layers and better delineates the groundwater aquifer and water table elevation. The resistivity of the aquifer layer and seismic velocity vary between 40 and 80 Ω m and between 1500 and 2500 m s -1 , respectively. The average water table elevation is about + 15 m, which is safe for the Great Sphinx, but it is still subjected to potential hazards from the Nazlet El-Samman suburb where the water table elevation reaches 17 m. A shallower water table at the Valley Temple and the tomb of Queen Khentkawes, with a low topographic relief, represents severe hazards. It can be concluded that a perched groundwater table is detected in the elevated topography to the west and southwest that might be due to runoff and capillary seepage. [ABSTRACT FROM AUTHOR]

Abstract A new approach is developed to depict the fault lineaments and pertinent tectonic evidence of the Sinai Peninsula, Egypt from gravity and magnetic data. The approach is capable of identifying not only the main basements trends, but also the main trends of the sedimentary structures. The main lineaments trends in the Peninsula were derived from the geologic information as well as the regional and residual gravity and magnetic maps. Joint interpretation has revealed that the deep-seated structures have NW-SE, NE-SW, E-W, and N-S trends, whereas the shallow-seated structures have NE-SW, E-W, NW-SE, and WNW-ESE. Highlights • Lineament investigations utilizing gravity and magnetic data of the Sinai Peninsula, Egypt. • Tectonic evident that controls the study area. • Residual gravity and magnetic maps used to evaluate shallow structures. • Regional gravity and magnetic maps utilized to estimate deep structures. [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]

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]

The particle swamp optimization procedure was applied to high-quality magnetic data acquired from the Precambrian Obudu basement complex in Nigeria with the object of estimating the distinctive body parameters (depth (z), index angle (θ), amplitude coefficient (K), shape factor (Sf), and location of the origin (x0)) of magnetic models. The magnetic models were obtained from four profiles that ran perpendicular to the observed magnetic anomalies within the study area. Profile A–A' with a length of 2600 m is characterized by inverted model parameters of K = 315.67 nT, z = 425.34 m, θ = 43°, Sf = 1.15, and x0 = 1554.86 m, while profile B–B' with a length of 5600 m is described by K = 257.71 nT, z = 543.75 m, θ = 54°, Sf = 0.96, and x0 = 3645.42 m model parameters. Similarly, profile C–C' with a length of 3000 m is defined by K = 189.53 nT, z = 560.87 m, θ = 48, Sf = 1.2, and x0 = 1950 m. Profile D–D', which is well-defined by a 2500 m length, started at the crest of the observed magnetic anomaly and displays inverted model parameters of 247.23 nT, 394.16 m, 39°, 1.26, and 165.41 m. Correlatively, the estimated shape factor of the four models (Sf = 1.15, 0.96, 1.2, and 1.26) shows that the magnetic models are linked to thin sheets. Furthermore, quantitative interpretations of the models show that the PSO operation is rapid and proficient. [ABSTRACT FROM AUTHOR]

Geophysical studies were performed along selected locations across the Pyramids Plateau to investigate the groundwater table and the near aquifer, which harmfully affected the existed monuments of the Giza Pyramids and Sphinx. Electrical Resistivity Imaging (ERI), Shallow Seismic Refraction (SSR) and Ground Penetrating Radar (GPR) techniques were carried out along selected profiles in the plateau. Ten ERI, twenty six SSR and nineteen GPR profiles were performed at the sites. The ERI survey shows that, the groundwater table is at elevations varying from 13 to 18 m above the sea level (asl) and low resistivity values near the surface at the Great Sphinx. ERI profiles, which were applied southeast of the Middle Pyramid (Khafre), show high resistivity values near the surface, and water table is located at elevations ranging from 22 to 40 m asl, while the ERI profiles conducted south of Menkaure, show almost high resistivity near the surface. The groundwater table is located at elevations ranging between 45 and 58 m asl. The aquifer layer shows electrical resistivities ranging between 10 and 50 Ohm.m. The considerable high change in the groundwater table is due to the rapid increases of topography from the Great Sphinx towards the Small Pyramids (Menkaure), where this part looks-like a scarp. The SSR Survey is transmitted to know the different velocities and types of the layers, which can help in knowing the saturated layers in the area. The GPR Survey is performed to delineate the water table, which gives good matching with the ERI results. [ABSTRACT FROM AUTHOR]

A robust Particle Swarm Optimization (PSO) investigation for magnetic data by a 2D dipping dike has been presented. The interpretive model parameters are: the amplitude coefficient (K), the depth to the top of the dipping dike (z), exact origin of the dipping dike (x0), and the width of dipping dike (w). The inversion procedure is actualized to gauge the parameters of a 2D dipping dike structures where it has been confirmed first on synthetic models without and with different level of random noise. The results of the inversion demonstrate that the parameters derived from the inversion concur well with the true ones. The root mean square (RMS) is figured by the strategy which is considered as the misfit between the measured and computed anomalies. The technique has been warily and effectively applied to real data examples from China and UK with the presence of ore bodies. The present technique can be applicable for mineral exploration and ore bodies of dike-like structure embedded in the shallow and deeper subsurface. [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]

An interpretation for the gravity anomalies is essential to visualize the horizontal and vertical extension of the subsurface intrusion like mud diapirs resembling dike-like geologic bodies. Therefore, the use of simple-geometrical resembling models helps to validate the subsurface targets. A particle optimization algorithm is one of the recently established metaheuristic algorithms, which is utilized in various geophysical applications and allows discovering and explaining the parameters of the buried geologic targets. Here, we have interpreted gravity response profiles for mud diapir, which close an expected two-dimensional (2D) inclined dikes by calculating the following parameters; amplitude coefficient (A), depths to top (h) and bottom (H), width (2b), inclination angle (θ), origin (d), and length of the body (L) that represents the difference between two depths using the particle optimization algorithm. The stability and efficacy of this study were checked on numerical examples without noise and with numerous levels of random noise (10% and 20%). Also, it tested on a gravity response for mud diapir from the south-western (SW) Taiwan and validated by seismic interpretation. The obtained results declared that the suggested algorithm works well even in the existence of noises. Furthermore, the results of the real case model are found in a respectable agreement with available geological and borehole information and other results from the published literature. • Global optimization algorithm for interpreting a mud diapir of an active gas transmission. • Visualizing the application of the particle optimization algorithm. • This method is successively applied to numerical and real datasets. • This method is powerful and robust. [ABSTRACT FROM AUTHOR]

We have developed a simple and fast numerical method to simultaneously determine the depth and shape of a buried structure from second moving average residual anomalies obtained from magnetic data with filters of successive window lengths. The method is similar to Euler deconvolution, but it solves for depth and shape independently. The method involves using a nonlinear relationship between the depth to the source and the shape factor, and a combination of observations at five points with respect to the coordinate of the source centre with a free parameter (window length). The method is based on computing the standard deviation of the depths determined from all second moving average residual anomalies for each value of the shape factor. The standard deviation may generally be considered a criterion for determining the correct depth and shape of the buried structure. When the correct shape factor is used, the standard deviation of the depths is less than the standard deviation using incorrect values of the shape factor. This method can be applied to residuals, as well as the observed magnetic data consisting of the combined effect of a residual component due to a purely local structure and a regional component represented by a polynomial of up to fourth-order. The method is applied to synthetic data, with and without random errors, and tested on a field example from Brazil. In all cases, the shape and depth of the buried structures are found in good agreement with the actual ones. [ABSTRACT FROM AUTHOR]

Abstract: A new fast least-squares method is developed to estimate the shape factor (q-parameter) of a buried structure using normalized residual anomalies obtained from gravity data. The problem of shape factor estimation is transformed into a problem of finding a solution of a non-linear equation of the form f(q)=0 by defining the anomaly value at the origin and at different points on the profile (N-value). Procedures are also formulated to estimate the depth (z-parameter) and the amplitude coefficient (A-parameter) of the buried structure. The method is simple and rapid for estimating parameters that produced gravity anomalies. This technique is used for a class of geometrically simple anomalous bodies, including the semi-infinite vertical cylinder, the infinitely long horizontal cylinder, and the sphere. The technique is tested and verified on theoretical models with and without random errors. It is also successfully applied to real data sets from Senegal and India, and the inverted-parameters are in good agreement with the known actual values. [Copyright &y& Elsevier]

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 investigated 22 different serpentinization level peridotite samples from the North Pyrenean Zone (NPZ) and along the North Pyrenean Fault (NPF) to characterize their mineralogical composition and petrophysical properties (bulk and grain density, porosity, compressional and shear wave velocities, and thermal conductivity and diffusivity). Peridotites have a percentage of serpentine that varies between 5 and 97 per cent for fresh samples and totally altered ones, respectively. The mineralogical changes by serpentinization induced a wide variety of petrophysical properties. The key indicators of serpentinization from petrophysical properties in serpentinized peridotite include an increase in porosity of up to 10 per cent and the associated decrease in the solid density ranging from 3.29 g cm–1 for fresh peridotites to 2.5 g cm–3 for the totally serpentinized peridotites. Thermo-physical properties were found to follow the same deceasing trend as the serpentinization increases up to 3000 m s–1 and 1.85 W m–1 K–1 for P -wave velocity and thermal conductivity (TC), respectively. For TC estimation, empirical relationships were produced using multiple linear regression (MLR) and a mineralogy model. For the mineralogy model, we used 10 mixing models commonly used in indirect TC estimation based on mineral composition. For the mean TC of individual mineral phases, the TC estimation shows that in case of non-availability of the proper samples for direct measurement, the TC of peridotite can be inferred with an acceptable level of error from the geometric mean model and harmonic mean models for the dry and saturated conditions, respectively. Based on the results of MLR, the study also confirms that the TC of dry rocks can be predicted through acoustic velocity, density and porosity with a prediction accuracy of ±0.24 W m−1 K−1 and confidence of >80 per cent. [ABSTRACT FROM AUTHOR]

An inversion technique using a fast method is developed to estimate, successively, the depth, the shape factor, and the amplitude coefficient of a buried structure using residual gravity anomalies. By defining the anomaly value at the origin and the anomaly value at different points on the profile, the problem of depth estimation is transformed into a problem of solving a nonlinear equation of the form f (z) = 0. Knowing the depth, the shape factor can be estimated and finally the amplitude coefficient can be estimated. This technique is applicable for a class of geometrically simple anomalous bodies, including the semiinfinite vertical cylinder, the infinitely long horizontal cylinder, and the sphere. The efficiency of this technique is demonstrated with gravity anomaly due to a theoretical model, in each case with and without random errors. Finally, the applicability is illustrated using the residual gravity anomaly of Mobrun ore body, situated near Noranda, QC, Canada. The interpreted depth and the other model parameters are in good agreement with the known actual values. [ABSTRACT FROM AUTHOR]

Abstract: We have developed a new least-squares minimization approach to depth determination from self-potential (SP) data. By defining the anomaly value at the origin and at any two symmetrical points around the origin on the profile, the problem of depth determination from the residual SP anomaly has been transformed into finding a solution to a nonlinear equation of the form f(z)=0. Procedures are also formulated to estimate the polarization angle, amplitude coefficient and the shape of the buried structure (shape factor). The method is simple and can be used as a rapid method to estimate parameters that produced SP anomalies. The method is tested on synthetic data with and without random errors. It is also applied to a field example from Turkey. In all cases, the model parameters obtained are in good agreement with actual ones. [Copyright &y& Elsevier]

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]

A new approach to the inversion of gravity data utilizing the Particle Swarm Optimization (PSO) algorithm is used to model 2D vertical faults. The PSO algorithm is stochastic in nature; its development was motivated by the communal in-flight performance of birds looking for food. The birds are represented by particles (or models). Individual particles have a location and a velocity vector. The location vectors represent the parameter value. PSO is adjusted with random particles (models) and searches for targets by updating generations. Herein, the PSO algorithm is applied to three synthetic data sets (residual only with and without noise, residual plus regional, residual plus anomaly generated by a buried cylinder structure) and two field gravity data sets acquired across known faults in Egypt. Assessment of the synthetic data demonstrates that the PSO algorithm generates superior results if a first horizontal gradient (FHG) filter is applied first. The robustness of the PSO inversion algorithm is demonstrated for both synthetic and field gravity data. • New inversion approach is used to model 2D vertical faults based upon Particle Swarm Optimization (PSO) and first horizontal gradient (FHG). • PSO algorithm is applied to three synthetic data sets and two field gravity data sets. • The robustness of the new approach is demonstrated for both synthetic and field gravity data. [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]

Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems. Plain Language Summary: A new global optimization algorithm, named Hunger Games Search (HGS), is presented for the inversion of geophysical data sets. The efficiency of the algorithm is tested on geomagnetic anomalies originated from different ore masses and synthetic data sets. The applications show that the proposed inversion algorithm produces reliable results and outperforms standard Particle Swarm Optimization, which is the most widely used global optimization algorithm in the inversion of geophysical data sets. We conclude that the HGS algorithm is a powerful inversion tool for geophysical anomalies. Key Points: This is the first study in the literature to apply Hunger Games Search algorithm to geophysical anomaliesPost‐inversion analyses showed the reliability of the solutions obtained in synthetic and field data casesCompared to the standard Particle Swarm Optimizer, proposed algorithm outperformed in the presented inverse problem [ABSTRACT FROM AUTHOR]

We have developed a least-squares minimization approach to depth determination using numerical second horizontal derivative anomalies obtained from magnetic data with filters of successive window lengths (graticule spacings). The problem of depth determination from second-derivative magnetic anomalies has been transformed into finding a solution to a non-linear equation of the form, f( z) = 0. Formulae have been derived for a sphere, a horizontal cylinder, a dike and a geological contact. Procedures are also formulated to estimate the magnetic angle and the amplitude coefficient. We have also developed a simple method to define simultaneously the shape (shape factor) and the depth of a buried structure from magnetic data. The method is based on computing the variance of depths determined from all second-derivative anomaly profiles using the above method. The variance is considered a criterion for determining the correct shape and depth of the buried structure. When the correct shape factor is used, the variance of depths is less than the variances computed using incorrect shape factors. The method is applied to synthetic data with and without random errors, complicated regionals, and interference from neighbouring magnetic rocks. Finally, the method is tested on a field example from India. In all the cases examined, the depth and the shape parameters are found to be in good agreement with the actual parameters. [ABSTRACT FROM AUTHOR]

Spatial information about soils generally results from local observations which are destructive and time consuming. Geophysical techniques could help soil mapping since they are non-destructive and fast. Electrical resistivity is interesting for soil studies due to a wide range of values and as it depends on soil characteristics. This work aims to study soil spatial variability using electrical resistivity. GPS defined grid points of 40X40 m were installed in the experimental western farm (EWF) in the Faculty of Agriculture of Cairo University in Giza. Electrical resistivity was measured at 40 points using 4-electrodes Wenner array in a line perpendicular to the path direction. Soil resistivity data from 2- depths profiling mode was considered to produce two apparent resistivity maps and geostatistically tested. Soil resistivity taxa were sampled and analyzed for soil moisture, EC and bulk density. Krigged soil resistivity maps were produced for depths (i.e. 30 and 60 cm). Kriging and Semivariogram interpretation was conducted, and the spatial dependency of top and subsoil resistivity were moderate (48.4% and 68.6% respectively). Highly significant negative correlations were recorded in the topsoil between apparent or true resistivity and soil moisture, EC or bulk density. The obtained models were used to produce conjugated moisture and EC maps and geostatistically investigated. The spatial dependency of the top and subsoil moisture or salinity were moderate. Soil moisture and EC are the most significant factors for controlling soil electrical resistivity. The method used opens the way to the development of semi-automatic soil mapping from electrical resistivity data. [ABSTRACT FROM AUTHOR]

In this paper, we develop a method to determine the depth to two superimposed sources from a self-potential anomaly profile. The method is based on finding a relationship between the depths to the two superimposed structures from a combination of observations at symmetric points with respect to the coordinate of the sources' centre. Formulae have been derived for two classes of geometric sources: spheres and cylinders. Equations are also formulated to estimate the other model parameters, including the polarisation angle and electric dipole moment of both structures. The proposed method is tested both on synthetic data with and without random noise, as well as real self-potential data from a set of field data collected in Turkey. In all cases, the model parameters obtained are in good agreement with the actual ones. [ABSTRACT FROM AUTHOR]

Because of the abundant geological, geochemical and geophysical studies conducted on the Emeishan large igneous province (ELIP) in South China, the Permian mantle plume model associated with this region is widely accepted. Furthermore, the dome-shaped structure related with this plume has been determined with success by sedimentological data and gravity stripping. Although the sediment thickness, upper crust, Moho depth and the lithosphere–asthenosphere boundary (LAB) are well constrained by active- and passive-source seismological results, the density anomaly in ELIP is still a poorly constrained issue that needs further attention. With the aim especially to understand the impact on surface of the magmatic processes that originated in the deep mantle, we performed the COMGRA-ELIP gravity experiment across this region. Using a stripping method, we determined the residual gravity in ELIP and surrounding areas. The residual gravity reaches a maximum value of + 150 mGal in the inner zone of ELIP and its strength decreases gradually when measuring from the inner zone to the middle and outer zones. Combining active and passive seismic results and the least-squares variance analysis method, we propose a strong density contrast of 0.2 g/cm 3 (density of 3.14 g/cm 3 ) for the 15- to 20-km-thick igneous layer accreted at the base of the crust, as evidence of crustal underplating in ELIP, to explain the present-day residual gravity anomaly. [ABSTRACT FROM AUTHOR]

—We have developed a least-squares minimization approach to depth determination from magnetic data. By defining the anomaly value T(0) at the origin and the anomaly value T(N) at any other distance (N) on the profile, the problem of depth determination from magnetic data has been transformed into finding a solution to a nonlinear equation of the form f(z)=0. Formulas have been derived for a sphere, horizontal cylinder, dike, and for a geologic contact. Procedures are also formulated to estimate the effective magnetization intensity and the effective magnetization inclination. A scheme for analyzing the magnetic data has been formulated for determining the model parameters of the causative sources. The method is applied to synthetic data with and without random errors. Finally, the method is applied to two field examples from Canada and Arizona. In all cases examined, the estimated depths are found to be in good agreement with actual values. [ABSTRACT FROM AUTHOR]