The aim of the GEFS project was to improve the tools used for earthquake forecasting. The study was conducted by Prof. Didier Sornette (Principal Investigator), as part of the chair of Entrepreneurial Risks within the department of Management, Technology and Economics, at ETH Zurich, Switzerland. The three-year Global Earthquake Forecast System (GEFS) project was mainly funded by SCOR through the ETH Zurich foundation.
Duration of the project: 2015-2018
The overall objective of the GEFS was to provide a reliable, rigorously tested platform to issue earthquake predictions within the few days or weeks before a large event strikes a vulnerable area. It thus involved simultaneously processing a wide range of physical data provided by different sensors located on satellites or on the ground. Its general focus was:
- the multi-phenomena nature of earthquake precursors,
- a unifying physical theory in terms of stress activation of mobile electric charges,
- multi-observations, and multi-dimensional continuous monitoring,
- multi-criteria and multi-dimensional analyses and synthesis of precursors into a decision function providing earthquake alarms and likelihoods of target events,
- a decision-making process towards operational activation and use by authorities, industry and citizens.
The database has been available on the collaborative platform since 2018, along with a special volume built around GEFS and published by Springer Verlag. Since the project began, several new data processing techniques have been defined and assessed. Work on synthetics and real datasets shows very promising results, especially in the natural setting of Taiwan. These positive developments are further strengthened by other results on theoretical, experimental and numerical dimensions, which definitely help to better understand the expected signatures and properties of the anomalies. This, in turn, should boost the next investigations by the project team.
Click here to read the final report (Prof. Didier Sornette: “Global Earthquake Forecasting System”, 2018, ETH Zürich).