Can Earthquakes Announce Their Arrival? : A Short Review

Earthquake prediction has been investigated for many decades through multidisciplinary research efforts. Specifically, animal behavioral changes (e.g., ant, dog, frog, horse, mouse, etc.) atmospheric (e.g., cloud movement, thermal infrared, total electron content, very low frequency electromagnetic signals), geochemical (e.g., CO2, radon, and other gases), geodetic (e.g., GPS, GNSS, InSAR), hydrogeological (e.g., groundwater level change), seismic hazard analyses, seismic quiescence period, seismic velocity and seismic velocity ratio, stress analysis, and statistical seismology (e.g., b-value, foreshock distribution, fractal dimension) observations have been extensively applied in studies addressing earthquake prediction. Simultaneously, the development of artificial intelligence and the increase in scientific data, along with the improvement of data evaluation techniques, have further accelerated these studies. This study synthesizes some existing research to highlight the challenges and potential opportunities in short-medium-long term earthquake prediction. Rather than a descriptive approach, this overview presents a structured, concise synthesis of earthquake prediction research by classifying indicators in the literature according to their physical foundations, time-dependent variations within earthquake recurrence intervals, and methodological outcomes. Furthermore, this review is designed primarily for scientists, particularly researchers in earth sciences disciplines, and aims to synthesize the strengths and weaknesses of currently used methodologies, promoting informed evaluation rather than direct deterministic prediction. Recent developments in machine learning and deep learning techniques, particularly those integrating multiple parameters as inputs, are expected to significantly enhance the accuracy of earthquake prediction.