Periodic Reporting for period 3 - GEO-4D (Geodetic data assimilation: Forecasting Deformation with InSAR)
Okres sprawozdawczy: 2021-01-01 do 2022-06-30
Earthquakes are one of the most destructive natural hazard on Earth and, despite tremendous progress on both fundamental aspects of the physics of earthquakes and on hazard mitigation, earthquakes remain unpredictable and pose a significant threat for a large part of the world population. One way to advance toward a more resilient society against earthquakes is to grow our understanding of tectonic faulting in order to better characterize the associated hazard.
In the past 20 years, tremendous efforts have been made in the development of ground deformation measurements in tectonically active regions, including ground- and space-based measurements such as GNSS or satellite imagery. Plate tectonics stress active faults by bending the crust until faults fail abruptly during an earthquake, radiating devastating seismic waves. However, recent observations actually revealed that faults can slip slowly, without radiating seismic waves, gently releasing stress. In parallel to these observations, our understanding of the mechanics of faulting made giant leaps toward more and more realistic models of loading and release of stress by slip on faults. We now know slow slip and earthquakes interact and both participate in the release of stress along faults, but the interplay between these and the underlying mechanisms controlling the mode of slip along faults are not understood.
In short, what are the physical mechanisms controlling whether a fault will generate slow harmless slip or a devastating earthquake? And more importantly, what are the most important data we should collect in order to grow our understanding and to improve the predictive ability of our models?
The Geo4D project aims at building tools to feed measurements of ground deformation into physics-based models of faulting, toward a data assimilation approach of ground deformation and earthquakes models. Similarly to what meteorologists do everyday, the question is whether we can build physics-based models that will be trained by incoming data. More importantly, we aim at recognizing which parts of these models can be constrained and which parts of the models will remain hidden.
The Geo4D project builds on todays immense amount of geodetic, open data, that can continuously feed into models. Today, GNSS data are acquired in active regions thanks to the efforts of the geodesy community and InSAR data are being continuously acquired globally. This provides the opportunity to test a data assimilation approach for the earthquake problem.
In the past 20 years, tremendous efforts have been made in the development of ground deformation measurements in tectonically active regions, including ground- and space-based measurements such as GNSS or satellite imagery. Plate tectonics stress active faults by bending the crust until faults fail abruptly during an earthquake, radiating devastating seismic waves. However, recent observations actually revealed that faults can slip slowly, without radiating seismic waves, gently releasing stress. In parallel to these observations, our understanding of the mechanics of faulting made giant leaps toward more and more realistic models of loading and release of stress by slip on faults. We now know slow slip and earthquakes interact and both participate in the release of stress along faults, but the interplay between these and the underlying mechanisms controlling the mode of slip along faults are not understood.
In short, what are the physical mechanisms controlling whether a fault will generate slow harmless slip or a devastating earthquake? And more importantly, what are the most important data we should collect in order to grow our understanding and to improve the predictive ability of our models?
The Geo4D project aims at building tools to feed measurements of ground deformation into physics-based models of faulting, toward a data assimilation approach of ground deformation and earthquakes models. Similarly to what meteorologists do everyday, the question is whether we can build physics-based models that will be trained by incoming data. More importantly, we aim at recognizing which parts of these models can be constrained and which parts of the models will remain hidden.
The Geo4D project builds on todays immense amount of geodetic, open data, that can continuously feed into models. Today, GNSS data are acquired in active regions thanks to the efforts of the geodesy community and InSAR data are being continuously acquired globally. This provides the opportunity to test a data assimilation approach for the earthquake problem.
We have initiated our efforts by first establishing a data processing strategy for InSAR images. Over the last decade, satellite constellations have been built and data now flow continuously. We have organized innovative strategies to process these data. In parallel, and in order to test our methodology, we have built a GNSS network to monitor ground deformation in Turkey, a region where we know earthquakes and slow slip events happen and can be measured with great fidelity. We have applied our processing strategy to different targets on earth and have established our detection capability and processed in the generation of active fault slip maps globally.
We are now moving toward the use of data assimilation for growing our understanding of the physics of earthquakes.
We are now moving toward the use of data assimilation for growing our understanding of the physics of earthquakes.
We expect significant progress in the following directions:
1. Geodetic data analysis: By exploring the continuous flow of data, we realized we needed additional tools in order to fully analyse these. We therefore have decided to move in the direction of machine learning and have made progress for the detection and quantification of ground deformation. Our methodology is ow able to detect millimeter slip events in noisy InSAR time series.
2. Systematic production of maps of surface displacements: We have mapped ground displacement over various actively deforming regions of the world and have significantly advanced our understanding of tectonic faulting in these regions. More exploration to come…
3. InSAR technical improvements: We expect (and have already produced) significant improvements in the processing of InSAR data.
4. Data Assimilation: We expect to provide one of the first assessment of the possibility of using data assimilation for the understanding of active faulting and for the development of a true predictive ability of the behavior of active faults.
1. Geodetic data analysis: By exploring the continuous flow of data, we realized we needed additional tools in order to fully analyse these. We therefore have decided to move in the direction of machine learning and have made progress for the detection and quantification of ground deformation. Our methodology is ow able to detect millimeter slip events in noisy InSAR time series.
2. Systematic production of maps of surface displacements: We have mapped ground displacement over various actively deforming regions of the world and have significantly advanced our understanding of tectonic faulting in these regions. More exploration to come…
3. InSAR technical improvements: We expect (and have already produced) significant improvements in the processing of InSAR data.
4. Data Assimilation: We expect to provide one of the first assessment of the possibility of using data assimilation for the understanding of active faulting and for the development of a true predictive ability of the behavior of active faults.