Horizontal boundary identification of causative sources is an essential tool in potential field data interpretation due to the feasibility of automatically retrieving the boundary information of subsurface gravity or geomagnetic structures. Although many approaches have been proposed to address these issues, it is still a hot research topic for many researchers to derive novel methods or enhance existing techniques. We present two high-resolution edge detectors based on the Gudermannian function and the modifications of the second-order derivative of the field. The effectiveness of the newly proposed filters was initially tested on synthetic gravity anomalies and geomagnetic responses with different assumptions (2-D and 3-D; imposed and superimposed; noise-free and noise-contaminated). The obtained results verified that the two novel methods yield the capability of producing high-resolution, balanced amplitudes and accurate results for better imaging causative sources with different geometrical and geophysical properties, compared with the other nine representative edge enhancement techniques. Furthermore, the yielded results from the application of the two strategies to a real-world aeromagnetic data set measured from the Central Puget Lowland (C.P.L) of the United States and a gravity data set surveyed from the Jalal Abad area of Kerman province, Iran, with detailed comparative studies validated that the edges identified via the two methods are in good agreement with the major geological structures within the study areas and the determined lateral information using the tilt-depth, top-depth estimation method. These features make them valuable tools for solving edge detection problems.

Enhancing the detection accuracy of the edges of the geological features within the subsurface remains a significant objective in geophysical data interpretation. Despite numerous advancements, approaches stemming from the directional gradients of gravity and magnetic fields still grapple with challenges such as low-resolution outcomes and susceptibility to noise contamination. In this study, we introduce a novel filtering framework based on the total horizontal gradient and its derivatives, designed to yield more precise and coherent edges free from false boundaries or disruptive artifacts. Validation using synthetic Bishop complex magnetic and gravity datasets, alongside Tuangiao aeromagnetic data from Vietnam, substantiates the robustness and applicability of our modified approach. Furthermore, recognizing the inherent susceptibility of edge detection filters to noise contamination resulting from directional derivatives, we employ the recently developed modified non-local means (MNLM) algorithm to alleviate noise effects prior to the analysis of noisy synthetic and real datasets. Our findings confirm the efficacy of the proposed method in reducing false artifacts and identifying edges with heightened precision, positioning MHGA as a valuable alternative for processing potential field data.

The self-potential method (SP) has been used extensively to reveal some model parameters of various ore deposits. However, estimating these parameters can be challenging due to the mathematical nature of the inversion process. To address this issue, we propose here a novel global optimizer called the Modified Barnacles Mating Optimizer (MBMO). We improved upon the original approach by incorporating a variable genital length strategy, a novel barnacle offspring evolving method, and an out-of-bounds correction approach. The MBMO has not been previously applied to geophysical anomalies. Prior to inversion of real data sets, modal and sensitivity Analyzes were conducted using a theoretical model with multiple sources. The Analyzes revealed that the problem is modal in nature, model parameters have varying levels of sensitivity, and an algorithm that can well balance global exploration with local exploitation is required to solve this problem. The MBMO was tested on theoretical SP anomalies and four real datasets from Türkiye, Canada, India, and Germany. Its performance was compared to the original version under equal conditions. Uncertainty determination studies were carried out to comprehend the reliability of the solutions obtained via both algorithms. The findings indicated clearly that the MBMO outperformed its original version in estimating the model parameters from SP anomalies. The modifications presented here improved its ability to search for the global minimum effectively. In addition to geophysical datasets, experiments with 11 challenging benchmark functions demonstrated the advantages of MBMO in optimization problems. Theoretical and field data applications showed that the proposed algorithm can be used effectively in model parameter estimations from SP anomalies of ore deposits with the help of total gradient anomalies.

Gravity measurement is a basic geophysical method for non-destructive exploration of mineral resources and hydrocarbons and also for geological studies. The quality of gravity data measured is constantly degraded by a low signal-to-noise ratio. Noise attenuation thus plays a vital role in processing and interpreting gravity field data. The non-local means (NLM) filtering was first and successfully introduced to attenuate randomly distributed noise situated in seismic records in geophysical community. However, less attention has been drawn to apply NLM to denoise potential field data, since the success of NLM is guaranteed by carefully tuned parameters and large computational costs. Here we propose the modified NLM (MNLM), based on unweighted Euclidean distance and integral image method, to denoise gravity datasets. The unweighted Euclidean distance is used to reduce the uncertainty and complexity of tuning the control parameters involved, and the integral image strategy is applied to avoid the enormous computational cost of the NLM. Since these filtering properties are desirable to mitigate noise in gravity datasets, we test and evaluate the MNLM filter on noisy synthetic gravity models with uniform and normal distributions and on real data from the Mariana trench and Slovakia, and compare it to other standard and representative denoising filters. The results on synthetic and field datasets confirm the high speed of this modified algorithm and show that, it removes noise most effectively while clearly preserving and not blurring structural details with less tuned parameters. The MNLM filter can therefore be considered as a promising and novel algorithm for denoising gravity data.

Gravity anomaly maps often contain spatially overlapping signatures from numerous sources, each with varying shapes, depths, and density contrasts. Locating these signatures using edge detection techniques is crucial for geological structural interpretation and imaging of horizontal boundaries. This paper proposes two effective edge detection tools: one combining the balanced total horizontal gradient (BHG), and the hyperbolic tangent function, abbreviated as “MTBHG”; and the other combining the tilt angle of the total horizontal gradient (TAHG) and the hyperbolic tangent function, abbreviated as “MTAHG.” Additionally, the Modified Non‐Local Means (MNLM) filter was applied to suppress possible noise effects amplified by the gradient calculation process. Synthetic tests validated that the MTAHG and MTBHG detectors outperform other representative detectors. Two high‐resolution gravity data sets from the Western Carpathians in Slovakia and the Witwatersrand Basin in South Africa were used to test the applicability of the modified methods. Results show that the modified detectors achieve superior edge delineation and avoid creating spurious anomalies or artifacts even in the presence of unwanted noise interference. Furthermore, by eliminating false tilt‐depth (TD) solutions via the edge detection results, we enhance the accuracy of depth estimates and facilitate the credible identification of both horizontal and vertical structure distributions.

Recent metaheuristic approaches are extensively and intensively being implemented to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a newly established metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After recognizing the modal features of the objective function tailored and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty appraisal tests have been further implemented to comprehend the reliability of solutions obtained. The solutions retrieved by HGS have been unbiasedly compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly used particle swarm optimization algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, stably, robustly, and coherently with the available geophysical, geological, and borehole verification, as HGS is able to better explore the model space without compromising its capability to efficiently approach the global minimum. This novel global optimization method can thus be considered as a promising tool in geothermal energy investigations and the study of volcanic activities.