Spatial forecasting of seismicity provided from Earth observation by space satellite technology
Spatial forecasting of seismicity provided from Earth observation by space satellite technology
Understanding the controls on the distribution and magnitude of earthquakes is required for effective earthquake forecasting. We present a study that demonstrates that the distribution and size of earthquakes in Italy correlates with the steady state rate at which the Earth’s crust moves. We use a new high-resolution horizontal strain rate (S) field determined from a very dense velocity field derived from the combination of Global Navigation Satellite System (GNSS) and satellite radar interferometry from two decades of observations. Through a statistical approach we study the correlation between the S and the magnitude of M ≥ 2.5 earthquakes that occurred in the same period of satellite observations. We found that the probability of earthquakes occurring is linked to S by a linear correlation, and more specifically the probability that a strong seismic event occurs doubles with the doubling of S. It also means that lower horizontal strain rate zone can have as large earthquakes as high horizontal strain rate zones, just with a reduced probability. The work demonstrates an independent and quantitative tool to spatially forecast seismicity.
Farolfi, Gregorio
2d54ab99-eb9f-4d8b-9d15-4928ef07a4de
Keir, Derek
5616f81f-bf1b-4678-a167-3160b5647c65
Corti, Giacomo
dce88b12-5b7a-43b1-8a58-5bd1bc13634c
Casagli, Nicola
8af41e26-f1c7-450c-b8e2-efa0bd68f8a3
16 June 2020
Farolfi, Gregorio
2d54ab99-eb9f-4d8b-9d15-4928ef07a4de
Keir, Derek
5616f81f-bf1b-4678-a167-3160b5647c65
Corti, Giacomo
dce88b12-5b7a-43b1-8a58-5bd1bc13634c
Casagli, Nicola
8af41e26-f1c7-450c-b8e2-efa0bd68f8a3
Farolfi, Gregorio, Keir, Derek, Corti, Giacomo and Casagli, Nicola
(2020)
Spatial forecasting of seismicity provided from Earth observation by space satellite technology.
Scientific Reports, 10 (1), [9696].
(doi:10.1038/s41598-020-66478-9).
Abstract
Understanding the controls on the distribution and magnitude of earthquakes is required for effective earthquake forecasting. We present a study that demonstrates that the distribution and size of earthquakes in Italy correlates with the steady state rate at which the Earth’s crust moves. We use a new high-resolution horizontal strain rate (S) field determined from a very dense velocity field derived from the combination of Global Navigation Satellite System (GNSS) and satellite radar interferometry from two decades of observations. Through a statistical approach we study the correlation between the S and the magnitude of M ≥ 2.5 earthquakes that occurred in the same period of satellite observations. We found that the probability of earthquakes occurring is linked to S by a linear correlation, and more specifically the probability that a strong seismic event occurs doubles with the doubling of S. It also means that lower horizontal strain rate zone can have as large earthquakes as high horizontal strain rate zones, just with a reduced probability. The work demonstrates an independent and quantitative tool to spatially forecast seismicity.
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s41598-020-66478-9
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Accepted/In Press date: 18 March 2020
e-pub ahead of print date: 16 June 2020
Published date: 16 June 2020
Additional Information:
Funding Information:
This work received the support of Department of Earth Sciences, University of Firenze and the Istituto Geografico Militare Italiano (IGM). We thank the institutions who run and maintain the various regional and global networks (EPN, ASI, INGV and others) and publicly share the continuous GNSS data and the Ministry of Environment for the SAR datasets used in this study. We would like to thank A. Piombino for the fruitful discussion concerning the Italian geodynamics. Geo-processing and figures were developed by using Quantum GIS, the Open Source Geospatial Foundation Project Quantum GIS (http://qgis.osgeo.org), PostGIS and Saga GIS and using the shaded-relief map ETOPO1 Global Relief Model. DK is supported by the NERC through grant NE/L013932/1.
Publisher Copyright:
© 2020, The Author(s).
Identifiers
Local EPrints ID: 442636
URI: http://eprints.soton.ac.uk/id/eprint/442636
ISSN: 2045-2322
PURE UUID: dc226b08-03a5-41dd-a226-1520e3b055b8
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Date deposited: 21 Jul 2020 16:54
Last modified: 17 Mar 2024 03:24
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Contributors
Author:
Gregorio Farolfi
Author:
Giacomo Corti
Author:
Nicola Casagli
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