Earthquake Precursors: Techniques, Models and Artificial Intelligence

Dear Colleagues,

Earthquakes are particularly devastating natural hazards with the potential to seriously harm both people and property. Residents in cities are severely impacted by the tremendous quantity of energy released during an earthquake, particularly if the epicentre is close. Catastrophic earthquakes are natural events and thus inevitable, and it is incredibly hard to forecast them. Thus, finding trustworthy seismic precursors is one of science's biggest challenges.

Among different precursors, electromagnetic (EM) observations can be efficient in indicating the occurrence of approaching earthquakes. The associated frequency range comprises bands between 0.001 Hz and 1 Hz (ultra-low frequencies, ULFs), between 1 kHz and 10 kHz (low frequencies, LFs), between 40 MHz and 60 MHz (high frequencies, HFs), and up to 300 MHz (very high frequencies, VHFs). Satellites, remote sensors, and ground stations are used to record EM fluctuations.

Radon-222 (radon) is also a long-established precursor of approaching seismic activity. It is an inert gas formed by the radioactive decay of the 238U series with a half-life of 3.86 days. Before it reaches the surface, subsurface water, and atmosphere, it dissolves in soil pores and liquids. Radon is particularly important in earthquake-related studies since it can be identified far from its genesis place due to its long travel distance in soil and water. Many articles have documented pre-seismic increases in radon in soil gas, groundwater, wells, thermal spas, and atmosphere.

Various other trace gases and ions from the earth have also attracted scientific interest due their precursory activity. Characteristic examples of trace gasses are He,He/Ar, N2/Ar, CH4/Ar,He/Ar, Ar Cu, Zn, Mn, Cr, Fe, B, K, Li, Sr, Ru, Mo, and N2, and examples of ions are Cl-, Br-, HCO3-, SO4-2, Na+, and Ca+2. Recorded variations in the above trace gases and ions have been found to occur between 50 km and 500 km within the epicentre and in a precursory window between one week and several months.

Other seismic precursors include water level changes, temperature alterations, surface and ground deformations, and changes in recordings of seismograms and other seismic instrumentation. For example, wells have been reported to change levels or water quality in hours, days, or weeks before earthquakes. Some studies mention changes in surface temperature before seismic activity, including changes in the circulation patterns of groundwater, bringing water of different temperature to the surface. Another example is changes in ground elevations over distances of tens of kilometres before strong earthquakes. Finally, analysing changes in seismic equipment installed in permanent telemetric stations is a good approach to study forthcoming earthquakes.

Credible precursors include several remote sensory devices, techniques based on satellite sensors, Synthetic Aperture Radar (SAR and InSAR) techniques, Ground Penetrating Radars (GPRs), Total Electromagnetic Current (TEC) measurements, and several ionospheric precursors. In fact, according to the theory of Lithosphere–Atmosphere–Ionosphere Coupling (LAIC), seismic precursors are found in all parts of this interaction scheme.

Because earthquakes are complex, advanced approaches and techniques are needed. Such methods include fractals, techniques based on the theory of information and entropy, symbolic dynamics, natural time analysis, spectral power law, Monofractal and Multifractal Detrended Fluctuation Analysis (DFA and MFDFA), Rescaled-Range Analysis (R/S), Fisher Information, several calculations of fractal dimensions, Hurst and Lyapunov exponent and numerous entropy metrics, and signal analysis techniques, among others. Potential methods include statistical approaches, studies of the seismicity for long and specific time periods, terminator methods, and synthetic techniques. Finally, it should be emphasised that there has recently been an increase in studies using artificial intelligence and deep learning methods in earthquake forecasting. The published research in this specific field is interesting and promising, thus garnering significant attention.

Earthquake scientists are highly interested in various earthquake models that are available or will be constructed in the future. Besides the above-mentioned LAIC model, other models include the principle of asperities, Dobrovolsky’s distances, mathematical models demonstrating associations between earthquake-related parameters such as the precursory time and the distance from the earthquake epicentre, and models forecasting several types of magnitudes.

As the Guest Editor of this Special Issue, I invite you to submit your papers, experimental and case studies, investigations, and reviews related to the subjects mentioned above. Papers that discuss interconnections between subjects are highly encouraged.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Dimitrios Nikolopoulos
Guest Editor

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• fractals
• self-organised systems
• non-linear dynamics and chaos
• natural time analysis
• entropy metrics
• artificial intelligence
• deep learning algorithms
• radon
• electromagnetism
• geodesy
• earthquakes
• seismicity techniques
• data analysis and statistics
• modelling and simulation
• algorithms, script languages (e.g., Python, R, Octave/Matlab), and implementation