Editorial for the Special Issue “Recent Advances in Exploration Geophysics”

Exploration geophysics aims to determine rocks’ mechanical, magnetic, and electric properties to support geological explorations for ore, minerals, and other resources [1]. Scientific and technological progress has revolutionized methods of exploration geophysics, transforming the field into a multidisciplinary endeavor that combines traditional techniques with cutting-edge innovations [2]. In particular, traditional gravimetric, seismological, magnetic, electric, and radiometric methods are often integrated with remote sensing, planetary geology, and machine learning methods, yielding unprecedented insights and efficiency gains [3].

One of the key themes of this special issue is integrating geophysical methods with machine learning, especially deep learning, to enhance efficiency in geophysical investigations [4]. This approach has shown great promise in both ore and petroleum prospecting. Notably, advancements in machine learning are not only being applied on Earth but also hold significant potential for emerging fields such as space mining, as efforts intensify to prepare instruments for resource prospecting on Mars, the Moon, and even asteroids [5].

The papers in this Special Issue cover various topics and applications, including numerous contributions concerning seismics. Dzulkefli et al. (Contribution 1) present an advanced target-oriented Kirchhoff migration method that integrates seismic redatuming to improve imaging accuracy, particularly in complex geological settings such as subsalt hydrocarbon reservoirs. Similarly, Deng et al. (Contribution 2) introduce a dynamic adaptive rock physics model (DARPM) for shale gas reservoirs, which leverages artificial intelligence to improve seismic characterization and address anisotropy challenges. Mad Zahir et al. (Contribution 3) explore the application of fiber optic distributed acoustic sensing (DAS) in vertical seismic profiling (VSP), showcasing its advantages over traditional geophone systems. Their findings underline the capability of DAS to deliver continuous and high-resolution data acquisition, paving the way for more precise imaging in exploration geophysics.

Gravity and magnetic data inversion methods are also featured in this Special Issue. Zhou et al. (Contribution 4) propose an innovative 3D gravity inversion algorithm that employs an improved conjugate gradient method to optimize computational efficiency. Del Razo Gonzalez and Yutsis (Contribution 5) extend this work by integrating joint inversion techniques for gravity and magnetic data, incorporating structural similarity constraints to enhance reliability and geological interpretability.

Environmental and hazard-related applications are another important focus. Liu et al. (Contribution 6) apply dual-frequency-induced polarization techniques to monitor chromium contamination in groundwater, emphasizing real-time monitoring for ecological preservation. Using gravimetric and geodetic methods, Szczerbowski and Gawałkiewicz (Contribution 7) conducted a multidisciplinary investigation of geohazards in a salt karst region affected by human activities. Katona et al. (Contribution 8) demonstrate the potential of spectral-induced polarization imaging for assessing graphite content in mining tailings, bridging laboratory-scale petrophysical models with field-scale applications.

Chen et al. (Contribution 9) propose a novel direct inversion method for determining the brittleness parameters of shale reservoirs, using a reweighted Lp-norm and the alternating direction method of multipliers. This approach improves convergence speed and accuracy in seismic inversion, addressing key challenges in brittleness parameter prediction. Dai et al. (Contribution 10) introduce the Arbitrary Sampling Fourier Transform algorithm, which enhances magnetic field forward modeling by overcoming edge effects and achieving higher computational efficiency. Their method provides a significant advancement for geophysical simulations and inversion imaging.

Innovative approaches to seismic ray theory are also featured. Yáñez et al. (Contribution 11) present a Riemannian seismic ray path tracing method for salt dome prospecting. This novel technique applies differential geometry to model seismic rays as geodesics in curved spaces, significantly improving the accuracy of wave propagation simulations in inhomogeneous media. Heilmann and Deidda (Contribution 12) optimize shallow seismic imaging with the Common-Reflection-Surface stack method, incorporating global simultaneous multi-parameter velocity analysis to enhance noise suppression and resolution.

Exploring extraterrestrial resources is increasingly becoming a critical area of research within exploration geophysics. Zwierzyński et al. (Contribution 13) investigate the potential of lunar cold microtraps as sources of raw materials, combining geological analysis with market valuation to present a business and technological perspective on space mining. Their work exemplifies the expanding role of geophysical methods in addressing planetary resource challenges.

Finally, this Special Issue showcases the role of remote sensing and computational advancements in pushing the boundaries of exploration geophysics. Several papers emphasize the importance of high-performance computing and machine learning in transforming data analysis, inversion, and imaging techniques, further highlighting their growing importance in terrestrial and planetary applications.

The collection of works in this Special Issue reflects the transformative potential of the modern exploration geophysics, underscoring its vital role in addressing critical challenges across disciplines. We thank the authors and reviewers for their contributions to this collection. We hope these works will inspire further advancements and foster continued innovation in the field of exploration geophysics.