Editorial for the Special Issue “Application of Geophysical Data Interpretation in Geological and Mineral Potential Mapping”

Geological structures often control the distribution of natural resources such as oil, gas, and minerals [1,2,3]. Interpreting geophysical data can help in mapping these structures [4,5,6,7,8]. To better understand the application of geophysical data interpretation in mapping geological structures and mineral deposits, we collected 10 contributions for this Special Issue. These contributions will be briefly presented here.

In the first contribution by Xu and co-authors, “Research on the Tectonic Characteristics and Hydrocarbon Prospects in the Northern Area of the South Yellow Sea Based on Gravity and Magnetic Data”, potential field and seismic data are employed to locate fault lineaments and estimate the basement depth in the northern basin and the middle uplift of the South Yellow Sea, China.

In “Low-Dimensional Multi-Trace Impedance Inversion in Sparse Space with Elastic Half Norm Constraint”, Lan et al. aim to improve the computational efficiency in multi-trace impedance inversion. They also proposed an inversion constraint based on an elastic half norm, and tested the proposed approach on synthetic and field seismic 2D profiles.

Park and co-authors employ paleomagnetic data and analyze the anisotropy of magnetic susceptibility in their work “Preferred Orientations of Magnetic Minerals Inferred from Magnetic Fabrics of Hantangang Quaternary Basalts”, contributing to the knowledge about the eruptive origin of the Hantangang River Volcanic Field, Korea.

The methodological background of the article entitled “Particle Swarm Optimization (PSO) of High-Quality Magnetic Data of the Obudu Basement Complex, Nigeria” by Ekwok and collaborators is a stochastic optimization approach to the inversion of magnetic profiles. The inversion considers a universal model that comprises a variety of simple geometric sources (spheres, cylinders, thin sheets, and geological contacts). The inversion technique was applied to four magnetic profiles from the Precambrian Obudu basement complex, Nigeria, revealing depositional zones for igneous-related minerals and migratory pathways for hydrothermal fluids.

Another methodological strand, data-driven machine learning algorithms, is the tool used by Behnia et al. in their work, “Mineral Prospectivity Mapping for Orogenic Gold Mineralization in the Rainy River Area, Wabigoon Subprovince”. Specifically, the Random Forest algorithm is employed to produce prospectivity maps from geological, gravity, and magnetic data from the Rainy River Area, Canada.

In “Mapping of the Structural Lineaments and Sedimentary Basement Relief Using Gravity Data to Guide Mineral Exploration in the Denizli Basin”, Altinoğlu applies depth-estimation and edge detection techniques to gravity data with the purpose of designing a basin depth model and delineate geological structures of the Denizli Basin, Turkey. These results are correlated especially with geothermal occurrences.

Alvandi et al. also consider edge detection methods in their work, “Enhancement of Potential Field Source Boundaries Using the Hyperbolic Domain (Gudermannian Function)”, where new techniques are proposed and compared with classical ones in synthetic and real-potential-field data, in particular, gravity data from the Jalal Abad iron mine, Iran.

Induced polarization was the geophysical method of choice by do Amaral and collaborators in “Electrical Prospecting of Gold Mineralization in Exhalites of the Digo-Digo VMS Occurrence, Central Brazil”. The geological–geophysical model obtained from the processing and inversion of the acquired data, in addition to the correlation of electrical and surface geological data, has made it possible to identify four anomalous areas related to potential mineralized zones.

Egorov and co-authors, in “Impact of the Regional Pai-Khoi-Altai Strike-Slip Zone on the Localization of Hydrocarbon Fields in Pre-Jurassic Units of West Siberia”, carry out the interpretation of gravity, magnetic, and seismic data from the Pai-Khoi-Altai strike-slip zone, Russia, for the automated forecasting of prospective deep-seated hydrocarbon deposits in the study area.

In “Gravity Data Enhancement Using the Exponential Transform of the Tilt Angle of the Horizontal Gradient”, Pham et al. introduced an improved enhancement technique that uses the exponential transform of the tilt angle of the horizontal gradient to improve the edge detection results. The robustness of the presented method is tested on synthetic models before applying to real gravity datasets to determine the geological features of the Tuan Giao area (Vietnam) and the boundaries of the Voisey’s Bay Ni–Cu–Co deposit (Canada).

In conclusion, the papers collected in this Special Issue provided some applications and new methodologies for a variety of geophysical methods to aid geological methods with an emphasis on mineral exploration. We hope that these papers will further stimulate the integration of geological and geophysical information, as well as the development of processing and inversion techniques suited for mineral exploration.