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Submarine mud diapirs (MDs) and volcanoes, often linked with gas hydrates and hydrocarbons, are prevalent features in the offshore region of SW Taiwan. Despite their significance, the gravity signatures of these MDs have not been investigated except for a few studies. Therefore, our study aims to address this gap by employing a recently proposed optimization algorithm, Hunger Games Search (HGS), to estimate model parameters of an inclined 2D anticlinal structure representative of an active MD. This approach was implemented to gravity anomaly obtained from SW coast of Taiwan. Before its application, we explored the nonlinear and high-dimensional characteristics of the cost function associated with this inverse problem by generating error landscape maps for model parameter pairs. Subsequently, to mitigate the challenges, a parameter tuning procedure was performed. Using the optimal control parameters derived from this procedure, we increased the efficacy of the HGS algorithm for the problem handled. Furthermore, we employed standard Particle Swarm Optimization (PSO), a widely utilized metaheuristic in geophysical inversion studies, for a comparison. The findings showed that the proposed novel algorithm yielded superior accuracy and reliability compared to PSO. Additionally, the inclined anticlinal structure exhibited better agreement with interpretation obtained from available seismic reflection outcomes, in contrast to approximation based on inclined dike-like structure. Besides, comparative analyses between HGS and two well-established techniques, namely correlation imaging and compact inversion algorithms further validated the effectiveness of HGS. Consequently, we assert that the novel HGS optimizer is a robust and appealing tool for inverse gravity problems. Moreover, assuming a 2D inclined anticlinal source type holds promise for extended investigations into other MDs in SW Taiwan's upper slope region. Such efforts can significantly enhance the understanding of regional mud diapirism and volcanism on a broader scale. • This study introduced a novel and powerful global optimizer, HGS algorithm. • It compared with the widely utilized PSO algorithm. • Compact inversion and correlation imaging techniques supported our results. • The mud diapiric model of Taiwan was approximated by an inclined anticlinal model. • Retrieved full parameters provided more abundant information about mud diapirs. [ABSTRACT FROM AUTHOR]

This study focuses on interpreting Bouguer gravity anomalies by two-sided fault structures. Faults have prime concerns for hazardous zones, mineralized areas, and hydrocarbon systems. The proposed scheme is done through the following steps: first, it utilizes the residual moving average anomalies estimated from the Bouguer gravity anomalies using several window lengths. Second, each residual anomaly is interpreted using the particle swarm. Third, calculate the average value for all interpreted anomalies. Fourth, the average values for the fault parameters are utilized to build the forward gravity model, which is compared with the true ones. The efficiency of this method has been studied by applying it to a synthetic example with different levels of impeded noise (0%, 5%, and 10%). Gravity data for fault structures were investigated from Egypt. It was found that the obtained results are in good agreement with the previously published studies. [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]

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]

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]

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]

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]