Editorial of Board Members’ Collection Series: “New Horizons in Geophysics: From Theory to Applications”

Geophysics represents a dynamic research field that delves into the intricate physical properties and processes that shape the Earth and its surrounding space environment. It uses standard and advanced statistical and quantitative methods to analyze the Earth’s processes. By integrating theoretical insights with observations, researchers in the field are striving to advance our understanding of natural phenomena (such as earthquakes, tsunamis, geomagnetic storms, volcanic eruptions, rainfall, etc.) and address pressing societal challenges.

For instance, multi-instrument, multi-satellite Earth Observation missions, such as the European Space Agency (ESA) Swarm constellation, have considerably improved our understanding of the Earth’s magnetic field both on a temporal and a spatial scale, and consequently, they have increased the accuracy and precision of the corresponding magnetic field models [1,2,3]. In addition, the artificial intelligence and machine learning revolution has also found fertile applications in various domains of geophysics, including geomagnetism [4,5].

The analysis of seismic hazard and the statistical characterization of earthquake processes have likewise undergone significant methodological renewal. Approaches rooted in natural time analysis [6], non-extensive statistical mechanics [7], or complex network theory [8] have enhanced our ability to model the spatiotemporal dynamics of seismicity. Simultaneously, the integration of advanced signal processing techniques with near-surface geophysical methods, such as Electrical Resistivity Tomography, Very Low Frequency (VLF), magnetic, and thermal data, has expanded the toolkit for subsurface characterization in seismic and volcanic areas [9].

This Special Issue aims to showcase cutting-edge research in geophysics, highlighting innovative methodologies and multi-disciplinary approaches.

Mancinelli et al. [10] start from observed variations in the length of day in the past 2.5 billion years to calculate the corresponding gravity variation and evaluate the possible effects that such variation would have induced on the lithosphere and on mantle convection. The results support the view that a convective system subject to changes in gravity responds through non-linear adjustments of its effective mixing at all scales.

Arsen’yev & Eppelbaum [11] attempt to develop a physical–mathematical model for calculating the influence of the ocean’s water masses on the Earth’s crust and geological layers. The surplus super-hydrostatic pressure depends on the amplitudes of solitary waves and the depth of an ocean. The surplus wave pressure averages 50% of the hydrostatic pressure on the shallow ocean shelves. Thus, the solitary wave’s tsunami class can provoke novel (repeated) earthquakes (or landslides) because surplus stresses affect the seismic focus.

Sobisevich et al. [12] present results of experimental studies performed at a full-scale geophysical observatory in the North Caucasus, which was established to study volcanic activity in the Elbrus area. Various geophysical instruments (Baksan Laser Interferometer–Strainmeter, Tiltmeters, Three-Component Magnetic Variometer, Precision Temperature Antenna) confirmed the presence of the magma chamber with the possibility of building a full-scale monitoring system for deep magmatic structures. Obtained estimates of the magmatic chamber size allow them to develop eruption scenarios and assess the volcanic hazard of the territory adjacent to the Elbrus volcanic center.

Samuel et al. [13] focus on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. They show how the integration of seismic attributes with machine learning can be incorporated into a seismic interpretation workflow to characterize potential storage reservoirs for hydrogen or $C{O}_2$ storage in MC-118. Geometric, amplitude-based spectral, textural, and instantaneous seismic attributes were combined and incorporated into an unsupervised machine learning algorithm, Self-Organizing Map (SOM), to classify seismic facies and delineate faulting influenced by salt tectonics and slope failure.

Varotsos et al. [14] apply natural time analysis to the seismicity that preceded the 3 April 2024 $M_W$7.4 Hualien earthquake. Natural time analysis enables the study of the dynamical evolution of a complex system and identifies when the system enters a critical stage. They find that in the beginning of September 2023, the order parameter of seismicity exhibited a clearly detectable minimum. Such a minimum demonstrates that seismic electric signal (SES) activity initiated which comprised several low-frequency transient changes in the Earth’s electric field preceding major earthquakes.

Alexopoulos et al. [15] conduct a combined aerial and surface investigation to study an inhabited, creeping mountainous landmass with profound surface deformation. The aerial investigation involves UAV’s LiDAR acquisition for the terrain model, while the multi-technique surface investigation includes resistivity (Electrical Resistivity Tomography) and seismic (Seismic Refraction Tomography, Multi-Channel Analysis of Surface Waves) measurements. This combination of methods has proven fruitful since several aspects of the landslide are clarified, such as water flow paths, the internal geological structure of the creeping mass, and its geometrical extent.

Alexopoulos et al. [16] apply several near-surface geophysical techniques (Electrical Resistivity Tomography, Seismic Refraction Tomography, Ground-Penetrating Radar, and Very-Low Frequency electromagnetic technique), supported by drone LiDAR usage to provide a comprehensive understanding of a subsurface hydrological regime. The focus of their study is on the identification of any subsurface water in the context of sustainable water management for a selected living hub, located in the semi-arid area of Agios Georgios-Mandra Attiki in Greece.

Mu et al. [17] propose a novel method for determining time-varying solid tidal factors based on the normal time–frequency transform (NTFT) theory, an approach allowing them to unbiasedly determine the instantaneous amplitude, frequency, and phase of time-varying signals, while mitigating the influence of edge effects to a certain extent. Building upon this foundation, they focus on analyzing the pre-seismic disturbance characteristics of the Q1 tidal wave, aiming to overcome the reliance of traditional research on specific tidal waves, thereby offering new technical pathways and research perspectives for pre-seismic disturbance detection.

In conclusion, the eight contributions collected in this Special Issue highlight the complexity and multifaceted nature of modern geophysics, ranging from the analysis of seismic and volcanic phenomena to the characterization of water and geological resources, as well as the development of novel methods for pre-seismic monitoring. From the deep-time evolution of Earth’s gravity to the immediate challenges of landslide monitoring and seismic risk mitigation, these studies highlight the indispensable role of innovative technologies—such as machine learning, LiDAR, and advanced signal processing—in deciphering the Earth’s dynamic processes. Furthermore, the presented results bridge the gap between theoretical modeling and practical applications, demonstrating how the integration of theoretical, experimental, and computational approaches provides new insights into the Earth’s processes, enhancing our ability to interpret complex natural phenomena and manage associated risks.

Finally, we thank all the authors for their high-quality contributions and the reviewers for their rigorous efforts in ensuring the excellence of this collection of cutting-edge studies. We hope this Special Issue catalyzes further research in the dynamic and ever-evolving field of geophysics.