Ground motion represents the main reaction to superficial and deep deformations induced by multiple natural and anthropic phenomena which take place at different spatio-temporal scale. Recent advanced ground deformation investigations make use of satellite Synthetic-Aperture Radar (SAR) data, a new remote sensing tool, to obtain the displacement time series of measuring points over wide areas at millimiters resolution. In the last two decades, A-DInSAR techniques have experienced a major development, which is mainly related to (i) the progress of the SAR data acquired by the COSMO-SkyMed satellites and the recent ESA Sentinel missions, that act at higher spatio-temporal resolution, and to (ii) the development of advanced processing algorithms. The improvements in the A-DInSAR technique need of an appropriate methodology to analyse extremely large datasets which consist of huge amounts of measuring points with high temporal resolution. This work contributes to address to these problems by exploiting the great potential contained in the A-DInSAR time series. The project aims are: 1.Development of a methodology to analyse multi-sensor and multi-temporal A-DInSAR dataset for the geological interpretation of areas affected by ground motion; 2.Analysis of the mechanisms of ground motion mainly due to groundwater level change; 3.Integration of A-DInSAR data with numerical models. The study has been carried out in areas representative of moderate rates of displacement (the valley bottom of the Oglio river, in Italy), of swelling-shrinkage of clayey soils (Oltrepo Pavese, in Italy), of coastal subsidence (Ravenna, in Italy), of slope instabilities (Piemonte Region, in Italy), of high rate of pumping-induced subsidence (Alto Guadalentín Basin, in Spain) and of ground motion due to groundwater level change (London Basin, in United Kingdom). In the investigation site of the Oltrepo Pavese, the developed methodology allowed the disentanglement of natural and man-induced processes through the analyses of ERS-1/2 and RADARSAT data. The results were useful to gain insight into three deformational behaviours: linear, non-linear, and seasonal components of motion. In the valley bottom of the Oglio river, a Pre-Alpine valley located upstream the Iseo Lake, the application of the methodology, through ERS-1/2 and RADARDAT data, highlighted the geomorphologic control of the subsidence pattern. Multiple datasets have been employed for the Ravenna case-study such as: ERS-1/2, Envisat, TerraSAR-X and Sentinel-1 to test the capability to recognize ground motion areas. The reproducibility of the methodology was also assessed for landslides investigation in the Piemonte Region. The analysis of the mechanisms, mainly due to groundwater level change, was performed in the Alto Guadalentín Basin in Spain. In this basin, the land subsidence due to the groundwater overexploitation reaches the higher values measured of Europa (>10 cm/yr). The approach allowed to understand that very thick soft soil layer with low permeability that has been drained since the 1960s, are involved in slow consolidation process, where the maximum settlement has yet to be reached. Finally, London Basin was chosen to model ground motion due to groundwater level change; by applying 1D model, since the large availability of geological, hydrogeological and geotechnical data. In this case the integration of A-DInSAR data in the modelling permitted to analyse the spatio-temporal variability of the ground motion response to groundwater levels variations across the London Basin. Overall, the study demonstrates how a better knowledge of ground deformations and the occurrence, measurement, mechanics and prediction can be reached by combining A-DInSAR with geological, geotechnical and hydrogeological data. The results could be used for land use planning and civil protection purposes, providing fundamental information to adopt appropriate mitigation measures.

Ground motion identification, monitoring and modelling through multi-sensor A-DInSAR data

BONI', ROBERTA
2016-12-16

Abstract

Ground motion represents the main reaction to superficial and deep deformations induced by multiple natural and anthropic phenomena which take place at different spatio-temporal scale. Recent advanced ground deformation investigations make use of satellite Synthetic-Aperture Radar (SAR) data, a new remote sensing tool, to obtain the displacement time series of measuring points over wide areas at millimiters resolution. In the last two decades, A-DInSAR techniques have experienced a major development, which is mainly related to (i) the progress of the SAR data acquired by the COSMO-SkyMed satellites and the recent ESA Sentinel missions, that act at higher spatio-temporal resolution, and to (ii) the development of advanced processing algorithms. The improvements in the A-DInSAR technique need of an appropriate methodology to analyse extremely large datasets which consist of huge amounts of measuring points with high temporal resolution. This work contributes to address to these problems by exploiting the great potential contained in the A-DInSAR time series. The project aims are: 1.Development of a methodology to analyse multi-sensor and multi-temporal A-DInSAR dataset for the geological interpretation of areas affected by ground motion; 2.Analysis of the mechanisms of ground motion mainly due to groundwater level change; 3.Integration of A-DInSAR data with numerical models. The study has been carried out in areas representative of moderate rates of displacement (the valley bottom of the Oglio river, in Italy), of swelling-shrinkage of clayey soils (Oltrepo Pavese, in Italy), of coastal subsidence (Ravenna, in Italy), of slope instabilities (Piemonte Region, in Italy), of high rate of pumping-induced subsidence (Alto Guadalentín Basin, in Spain) and of ground motion due to groundwater level change (London Basin, in United Kingdom). In the investigation site of the Oltrepo Pavese, the developed methodology allowed the disentanglement of natural and man-induced processes through the analyses of ERS-1/2 and RADARSAT data. The results were useful to gain insight into three deformational behaviours: linear, non-linear, and seasonal components of motion. In the valley bottom of the Oglio river, a Pre-Alpine valley located upstream the Iseo Lake, the application of the methodology, through ERS-1/2 and RADARDAT data, highlighted the geomorphologic control of the subsidence pattern. Multiple datasets have been employed for the Ravenna case-study such as: ERS-1/2, Envisat, TerraSAR-X and Sentinel-1 to test the capability to recognize ground motion areas. The reproducibility of the methodology was also assessed for landslides investigation in the Piemonte Region. The analysis of the mechanisms, mainly due to groundwater level change, was performed in the Alto Guadalentín Basin in Spain. In this basin, the land subsidence due to the groundwater overexploitation reaches the higher values measured of Europa (>10 cm/yr). The approach allowed to understand that very thick soft soil layer with low permeability that has been drained since the 1960s, are involved in slow consolidation process, where the maximum settlement has yet to be reached. Finally, London Basin was chosen to model ground motion due to groundwater level change; by applying 1D model, since the large availability of geological, hydrogeological and geotechnical data. In this case the integration of A-DInSAR data in the modelling permitted to analyse the spatio-temporal variability of the ground motion response to groundwater levels variations across the London Basin. Overall, the study demonstrates how a better knowledge of ground deformations and the occurrence, measurement, mechanics and prediction can be reached by combining A-DInSAR with geological, geotechnical and hydrogeological data. The results could be used for land use planning and civil protection purposes, providing fundamental information to adopt appropriate mitigation measures.
Ground; Motion,; A-DInSAR,; groundwater;
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1203392
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