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Detection of source horizontal boundaries is a common feature in the interpretation of magnetic and gravity data. A wide range of derivative- and phase-based methods are available to solve this problem. Here, we compare the effectiveness of the commonly used methods, and introduce a method based on the logistic function and the horizontal gradient amplitude, which shows improved performance as a boundary detection filter. The effectiveness of the proposed filter is demonstrated by evaluating synthetic examples and a real example from the Central Puget Lowland (United States). The main advantage of this method is that it provides high-resolution results, and can avoid producing spurious boundaries in the output maps.

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.

The Svalbard archipelago is well‐known for its outcropping geology, but its subsurface geometry and physical properties are less well constrained. To address this knowledge gap, a multidisciplinary approach has been applied to integrate potential field data with seismic reflection profiles, exploration boreholes and geology. We constructed five 2D gravity and magnetic forward models along pre‐existing seismic lines in central Spitsbergen, utilizing newly acquired ground‐borne gravity, helicopter‐borne magnetic, and regional gridded potential field data. Specifically, we focused on characterizing the geophysical signature of the pre‐Devonian basement composition and topography, position of the Billefjorden Fault Zone (BFZ), and the magmatic bodies related to the High Arctic Large Igneous Province (HALIP). Regional trends show a 15 km wide NNW‐SSE trending magnetic and gravity high east of the BFZ and a long‐wavelength elliptical magnetic anomaly of ca. 50 km diameter below central Isfjorden with no anomalous gravity response. We observe outcropping igneous rocks and interpret HALIP‐related igneous sills on 2D seismic data in this region matching the short‐wavelength signal. Our models show that most of the gravity signal is related to the arrangements of the sedimentary basins and the influence of the BFZ on the basement topography. Large lateral intra‐basement magnetic susceptibility contrasts and a deep magnetic source were necessary to fit the magnetic signal, indicating basement heterogeneity and locally the influence of magmatic bodies.

Locating the edges of anomalous bodies provides a fundamental tool in the geologic interpretation of potential field data. This paper compares the effectiveness of the commonly used edge detection methods such as the total horizontal gradient, analytic signal, tilt angle, theta map and their modified versions in terms of their accuracy on the determination of edges of source bodies. This paper also introduces an edge detector method for the enhancement of potential field anomalies, which is based on the logistic function of the total horizontal gradient. The new method is tested on synthetic data calculated using 3 models, and also on real magnetic and gravity data from Vietnam. The effectiveness of the method is evaluated by comparing the results with those of other popular methods. These results demonstrate that the method is a useful tool for the qualitative interpretation of potential field data.

On February 6, 2023, southeastern Türkiye experienced devastating doublet earthquakes (Mw = 7.7 and Mw = 7.6) with a series of aftershocks along the East Anatolian Fault Zone. The mainshocks were followed by ~ 15,000 aftershocks mainly distributed in the NNE–SSW direction, including ~ 400 events with an Mw ≥ 4.0 in the following 30 days. Although many moderate to large earthquakes have occurred in the historical and instrumental periods of this region, these double earthquakes and their aftershocks majorly impacted lives and released great seismic energy. In this study, we interpret the gravity-magnetic data and the epicenter and hypocenter distributions of the aftershocks to correlate the tectonic structures and the active fault zones. The results of potential field anomalies reveal that the rotational anomalies in the southwestward direction are associated with the tectonic structure of Anatolia. Results show that shallow aftershocks are associated with high-gravity anomalies, whereas deeper aftershocks are associated with low-gravity anomalies and they become shallower in places where gravity values increase. After the derivative transformations are applied to the magnetic anomalies, it is seen that the faults and regions of magnetic discontinuity are in good agreement. Consequently, the findings on gravity, magnetic anomalies and aftershock sequences demonstrate that the first mainshock occurred in the unbroken segment of the East Anatolian Fault Zone.

Edge detection plays an important role in interpreting geophysical potential field data and revealing faults, contacts, and other linear tectonic structures. Various methods have been proposed to detect and enhance edges; however, they are often sensitive to noise, unable to adequately balance amplitude information across different depths, and may introduce false edges that require manual removal. To overcome these limitations, we present an improved method called the hyperbolic tangent function with Gaussian envelope constraints on the horizontal gravity gradient tilt angle (THTAHG) that is based on the tilt angle of the horizontal gravity gradient and the hyperbolic tangent (TANH) function under Gaussian envelope constraints. The applicability of the method is illustrated with three synthetic models and actual gravity data. Compared with existing edge detection techniques, our method provides clearer and more precise identification results, with enhanced stability and without introducing spurious boundaries. Finally, to address the problem of the plate boundary division and internal response of the Tibetan Plateau, this research studies the characteristics of the distribution of the tectonic structures of the Tibetan Plateau and its surroundings on the basis of the THTAHG method with Bouguer gravity anomaly data. A total of 20 fault zones are identified, which have been verified by previous studies. We verify the existence of the central uplift zone of the Qiangtang Basin. These experiments demonstrate that this work provides an effective method for studying tectonic boundaries and geodynamic evolution. Graphical Abstract

By integrating inversion techniques with modeling data of the Earth’s passive potential field, encompassing gravity and magnetic fields, we can enhance our understanding of subsurface structural features, particularly faults, thereby contributing to advancements in earth science and environmental studies. Metaheuristic algorithms have gained prominence as global optimization tools, with increasing utilization for optimizing complex systems. This study proposes the utilization of the Metaheuristic Bat Algorithm (MBA), inspired by the echolocation capabilities of bats, to efficiently search for optimal solutions. The MBA method aims to minimize a predefined objective function, leading to the identification of fault-path parameters once the global optimum solution is attained. This approach offers a systematic means of evaluating fault characteristics without requiring prior domain knowledge. Application of the MBA methodology to potential field data facilitates the estimation of fault dimensions, including depth, origin, and dipping angle. Through rigorous testing on diverse simulated datasets with varying noise levels, the MBA approach demonstrates high precision and consistency in fault characterization. Moreover, field applications conducted in the USA, Egypt, Australia, and India validate the efficacy of the MBA scheme in earth science and engineering investigations. The inversion results obtained using the MBA approach align closely with drilling data, geologic observations, and existing literature, underscoring its reliability and utility in subsurface analysis. Highlights Global optimization methodology for Earth application studies including geological, geotechnical, and geo-environmental studies. Innovative application of the Metaheuristic Bat Algorithm (MBA) to demonstrate its adaptability and efficiency in handling the complexities of potential field data analysis. Robust analysis of real-world data for fault detection, which plays a crucial role in understanding environmental impacts and seismic events like earthquakes.

Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis.

Improving the horizontal boundaries of subsurface geological structures is one of the main objectives in interpreting potential fields. To solve this problem, a number of different algorithms have been introduced based on the derivatives of the field. However, these algorithms have some drawbacks, e.g., the determined edges do not match the actual boundaries. Here, we present a new algorithm based on the gradient amplitude and its derivatives which yields more precise and clear boundaries. The robustness of the proposed technique is illustrated using theoretical examples and a real example from Kon Tum province, Vietnam. Our results show that the proposed technique can produce results with better resolution and minimizes the artifacts in the pseudo-boundary map.

This study explores the application of a rapid 3D cross-correlation imaging technique for analyzing potential field gravity and magnetic data. The approach involves calculating cross-correlation values between observed potential field anomalies and theoretical data, followed by the assessment of correlation coefficients. The method's efficacy was demonstrated through preliminary tests on various synthetic models with differing characteristics, highlighting its ability to detect and accurately locate subsurface sources, even in complex scenarios with inclined and multiple sources. The 3D imaging outcomes provided comprehensive insights into the location and center of anomaly sources. The methodology was then applied to ground magnetic and gravity datasets to evaluate the spatial distribution of underground iron deposits in the Shavaz region. The results closely matched drilling data and prior studies in the area, reinforcing the technique's effectiveness in interpreting real geophysical data. This research emphasizes the advantages of the cross-correlation imaging method in enhancing the understanding of subsurface structures, revealing hidden anomalies, and estimating the depth and arrangement of buried features.