The theoretical and practical foundations of strong earthquake predictability

Abstract: This paper provides theoretical and practical arguments regarding the possibility of predicting strong and major earthquakes worldwide. Many strong and major earthquakes can be predicted at least two to five months in advance, based on identifying stressed areas that begin to behave abnormally before strong events, with the size of these areas corresponding to Dobrovolsky formula. We make predictions by combining knowledge from many different disciplines: physics, geophysics, seismology, geology, and earth science, among others. An integrated approach is used to identify anomalies and make predictions, including satellite remote sensing techniques and data from ground-based instruments. Terabytes of information are currently processed every day with many different multi-parametric prediction systems applied thereto. Alerts are issued if anomalies are confirmed by a few different systems. It has been found that geophysical patterns of earthquake preparation and stress accumulation are similar for all key seismic regions. The same earthquake prediction methodologies and systems have been successfully applied in global practice since 2013, with the technology successfully used to retrospectively test against more than 700 strong and major earthquakes since 1970.

• The paper demonstrates that up to 90% of major past earthquakes could have been forecasted with a two-to-five-month lead time.
• It employs a multidisciplinary methodology that integrates satellite remote sensing, geophysical data, and big data analytics to detect stress anomalies.
• The research challenges traditional chaos-based seismology, offering practical insights for improving disaster preparedness in key earthquake-prone regions.

An Analysis of Methodologies and Implications in Strong Earthquake Predictability

The paper by Elshin and Tronin explores the feasibility of predicting significant seismic events with substantial precision by leveraging a multidisciplinary approach. Employing a fusion of satellite remote sensing, geophysical data, and big data analytics, this study asserts that major earthquakes can be forecasted with a lead time ranging from two to five months.

Key Findings and Methodology

The authors propose an earthquake prediction methodology that capitalizes on identifying anomalies in stressed geological areas, aligning with Dobrovolsky's formula. This approach relies on the integrated utilization of cross-disciplinary knowledge from physics, geophysics, seismology, and earth sciences. Their methodology is substantiated by retrospective analysis against over 700 historical earthquakes, suggesting that upto 90% of significant events over the past five decades could have been predicted using their approach.

A critical aspect of this methodology involves the analysis of massive datasets—terabytes of data are processed daily. Anomalies are detected through satellite remote sensing and confirmed by various systems. This research disputes the frequently cited notion that earthquake systems are inherently chaotic and self-organizing to the point of unpredictability. Instead, it suggests that stress accumulation is a substantial and measurable precursor to major seismic events.

Practical and Theoretical Implications

The practical implications of this research are far-reaching, potentially allowing governments and disaster relief agencies to enhance preparedness drastically. The ability to predict earthquakes with reasonable accuracy has the potential to mitigate loss of life and reduce economic damages in affected areas. In practice, the prediction technology is employed in 24 key earthquake-prone regions worldwide. Notably, the authors report successful alerts for significant seismic events such as the 2019 Ridgecrest earthquake sequence and projections in regions like Japan, Greece, and the Kuril Islands.

Theoretically, the research challenges existing paradigms on seismic activity predictability. By proposing a framework that contravenes the traditional views of seismic system self-organized criticality and randomness, it provides a basis for further studies aimed at refining prediction models and enhancing accuracy. The model advocates for a paradigm shift, positing that with precise anomaly detection, the chaotic nature of seismic events is not an insurmountable barrier to prediction.

Future Directions

Looking forward, continual improvements in satellite technology and data analytics offer promise in refining earthquake prediction methodologies. The progression from identifying large stress zones to more focused predictions with tighter epicenter localization is feasible as technological advancements are realized. Long-term prediction models, with horizons spanning several years, require continuous fine-tuning and validation as more significant earthquakes are retrospectively analyzed.

The research underscores the necessity of collaborative efforts across countries and disciplines to consolidate resources and expertise. Such cooperation could accelerate the development of a robust global earthquake prediction system, potentially revolutionizing disaster management and seismology.

In conclusion, this paper contributes significantly to both the theoretical discourse and practical applications in the domain of earthquake prediction. While it challenges established assumptions in seismology, it also opens avenues for robust prediction models capable of shielding communities from the devastating impacts of seismic activities.