How geodesy can contribute to the understanding and prediction of earthquakes

TL;DR

This paper demonstrates how integrating geodetic data, especially GPS, with seismological algorithms can improve earthquake prediction by identifying strain accumulation and refining alarm areas, exemplified by the 2016 Central Italy crisis.

Contribution

It advances the integration of geodetic and seismological data, showing how GPS-based strain analysis can enhance earthquake prediction and reduce alarm zones.

Findings

GPS data reveal strain accumulation before the 2016 Amatrice earthquake.

Combined analysis can reduce the size of alarmed areas.

Geodesy contributes significantly to understanding earthquake precursors.

Abstract

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.