How geodesy can contribute to the understanding and prediction of earthquakes

Earthquakes cannot be predicted with precision, but algorithms exist for intermediate-term middle range prediction of main shocks above a pre-assigned threshold, based on seismicity patterns. Few years ago, a first attempt was made in the framework of project SISMA, funded by Italian Space Agency, to jointly use seismological tools, like CN algorithm and scenario earthquakes, and geodetic methods and techniques, like GPS and SAR monitoring, in order to effectively constrain priority areas where to concentrate prevention and seismic risk mitigation. We present a further development of integration of seismological and geodetic information, clearly showing the contribution of geodesy to the understanding and prediction of earthquakes. As a relevant application, the seismic crisis that started in Central Italy in August 2016 is considered in a retrospective analysis. Differently from the much more common approach, here GPS data are not used to estimate the standard 2D velocity and strain field in the area, but to reconstruct the velocity and strain pattern along transects, which are properly oriented according to the a priori information about the known tectonic setting. Overall, the analysis of the available geodetic data indicates that it is possible to highlight the velocity variation and the related strain accumulation in the area of Amatrice event, within the area alarmed by CN since November 1st, 2012. The considered counter examples, across CN alarmed and not-alarmed areas, do not show any comparable spatial acceleration localized trend. Therefore, we show that the combined analysis of the results of CN prediction algorithms, with those from the processing of adequately dense and permanent GNSS network data, may allow the routine highlight in advance of the strain accumulation. Thus it is possible to significantly reduce the size of the CN alarmed areas.

💡 Research Summary

The paper presents an integrated methodology that combines the CN intermediate‑term earthquake prediction algorithm with high‑resolution geodetic observations (continuous GNSS and SAR) to refine the spatial definition of seismic hazard zones. The work builds on the SISMA project, funded by the Italian Space Agency, which previously demonstrated that CN can issue alarms for regions where a main shock above a predefined magnitude is statistically likely. However, CN’s alarm areas are often large, limiting their practical usefulness for emergency planning and risk mitigation.

To address this limitation, the authors propose a novel “transect‑based” geodetic analysis. Instead of constructing a conventional two‑dimensional velocity and strain field over the entire study area, they define linear transects that are oriented according to the known tectonic architecture (i.e., parallel and perpendicular to the dominant fault systems). GNSS time series along these transects are processed to extract velocity variations and strain accumulation with high spatial resolution. SAR interferograms are used as an independent check on surface deformation patterns.

The methodology is applied retrospectively to the Central Italy seismic crisis that began in August 2016, focusing on the Amatrice‑Rieti‑Norcia region. CN had issued an alarm for this area on 1 November 2012. By comparing GNSS‑derived velocity trends inside the CN‑alarmed zone with those in adjacent non‑alarmed zones, the authors demonstrate a clear acceleration of east‑west motion and a concurrent north‑south compressional strain that intensified in the months leading up to the Amatrice main shock. In contrast, the non‑alarmed sectors show no comparable acceleration or localized strain build‑up.

These findings illustrate that geodetic monitoring can provide a physical manifestation of the statistical risk identified by CN. When the two data streams are combined, it becomes possible to shrink the CN alarm footprint to the sub‑regions where measurable strain is actually accumulating. This reduction in alarm size has immediate implications for resource allocation, public communication, and targeted mitigation measures.

The paper also discusses practical requirements for operational implementation. A dense, permanent GNSS network with sub‑daily sampling, complemented by regular SAR acquisitions, is essential to maintain the temporal resolution needed for early detection of strain acceleration. The authors suggest that future work should explore machine‑learning classifiers trained on GNSS‑SAR time series to automatically flag anomalous strain trends, and that the approach be tested in other seismically active regions (e.g., Japan, Chile) to assess its generality.

In summary, the study demonstrates that integrating geodetic observations with the CN prediction algorithm can transform a broad statistical alarm into a focused, physics‑based warning. This synergy not only enhances the scientific understanding of strain accumulation prior to large earthquakes but also offers a pragmatic pathway to improve earthquake preparedness and reduce societal risk.