Application of DInSAR-GPS optimization for derivation of fine-scale surface motion maps of Southern California

A method based on random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is used to derive 3-D surface motion maps from sparse global positioning system (GPS) measurements and differential interferometric synthetic aperture radar (DInSAR) interferogram in the southern California region. The minimization of the Gibbs energy function is performed analytically, which is possible in the case when neighboring pixels are considered independent. The problem is well posed and the solution is unique and stable and not biased by the continuity condition. The technique produces a 3-D field containing estimates of surface motion on the spatial scale of the DInSAR image, over a given time period, complete with error estimates. Significant improvement in the accuracy of the vertical component and moderate improvement in the accuracy of the horizontal components of velocity are achieved in comparison with the GPS data alone. The method can be expanded to account for other available data sets, such as additional interferograms, lidar, or leveling data, in order to achieve even higher accuracy

3D displacement field retrieved by integrating Sentinel-1 InSAR and GPS data: the 2014 South Napa earthquake

In this study the integration of Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) and GPS (Global Positioning System) data was performed to estimate the three components of the ground deformation field due to the Mw 6.0 earthquake occurred on August 24th, 2014, in the Napa Valley, California, USA. The SAR data were acquired by the Sentinel-1 satellite on August 7th and 31st respectively. In addition, the GPS observations acquired during the whole month of August were analyzed. These data were obtained from the Bay Area Regional Deformation Network, the UNAVCO and the Crustal Dynamics Data Information System online archives. The data integration was realized by using a Bayesian statistical approach searching for the optimal estimation of the three deformation components. The experimental results show large displacements caused by the earthquake characterized by a predominantly NW-SE strike-slip fault mechanism.The research has been supported by the “Marco Polo” project by the University of Bologna (UNIBO), the Spanish Ministry of Economy and Competitiveness research project ESP2013-47780-557 C2-1-R and the EU 7th FP MED-SUV project (contract 308665).Peer reviewe

Review of works combining GNSS and insar in Europe

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach

Insar Role in the Study of Earth's Surface and Synergic Use with Other Geodetic Data: the 2014 South Napa Earthquake

This work focuses on the role of SAR Interferometry (InSAR) in the study of many phenomena characterizing the Earth's surface. We propose an advanced integration method in order to merge the InSAR data with other geodetic data, i.e. Multiple Aperture Interferometry (MAI), Pixel Offset Tracking (POT) and Global Positioning System (GPS). We apply the method to constrain the full 3D displacement field produced by the Mw 6.1 2014 South Napa Valley earthquake and then we used the results from the integration to perform the source modeling. The first Chapter is meant to introduce the topic of the progressive use of Remote Sensing geodetic data to support the activities of monitoring and hazard mitigation related to natural phenomena. Chapter 2 shows the application of the InSAR technique to reconstruct and model surface displacement fields induced by several phenomena. In Chapter 3, the 3D coseismic displacement map due to the 2014 Mw 6.1 South Napa earthquake, close the San Andreas Fault system (California), is estimated by using a method to merge InSAR and GPS data. InSAR data are provided by the latest satellite of the European Space Agency (ESA), i.e. Sentinel-1, whereas the GPS data were obtained from the BARD network and several online archives. In Chapter 4 we propose an improved algorithm for the data integration and test it on the Napa earthquake. Geodetic data from MAI and POT are added in the processing chain and the GPS data interpolation is modified according to the specific phenomenon. Futhermore, the source modeling is performed by inversion of the obtained 3D displacement component. The best fit is obtained by simulating a fracture in the fault segment in agreement with previous works. Finally, in the last chapter we discuss about the advantages and disadvantages of the data integration and the future perspectives

Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.</p

Time Series Analysis of Surface Deformation Associated With Fluid Injection and Induced Seismicity in Timpson, Texas Using DInSAR Methods

In recent years, a rise in unconventional oil and gas production in North America has been linked to an increase in seismicity rate in these regions (Ellsworth, 2013). As fluid is pumped into deep formations, the state of stress within the subsurface changes, potentially reactivating pre-existing faults and/or causing subsidence or uplift of the surface. Therefore, hydraulic fracturing and/or fluid disposal injection can significantly increase the seismic hazard to communities and structures surrounding the injection sites (Barnhart et al., 2014). On 17th May 2012 an Mw4.8 earthquake occurred near Timpson, TX and has been linked with wastewater injection operations in the area (Shirzaei et al., 2016). This study aims to spatiotemporally relate, wastewater injection operations to seismicity near Timpson using differential interferometric synthetic aperture radar (DInSAR) analysis. Results are presented as a set of time series, produced using the Multidimensional Small Baseline Subset (MSBAS) InSAR technique, revealing two-dimensional surface deformation

Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series

As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions

Monitoring Actives Volcanoes by Using of Envisat and Ers Data: First Results of the Eurorisk-Preview Project

In the framework of the Eurorisk-Preview project, funded by the European Union, a task is dedicated to the assessment, prevention and management of volcanic risk. We are developing a multidisciplinary approach, integrating the geophysical prospecting at local scale and large scale remote sensing data. To achieve this task, two volcanic test sites have been identified: Mt. Etna, in Sicily (Italy), and Tenerife, in Canary Islands (Spain). We investigate the surface deformation and the volcanic emission in the atmosphere by using SAR series and multispectral data, requested in the ESA Category 1 (n. 3560). For Mt. Etna data from historical to recent eruptions (1992 – 2006) has been analysed while for Tenerife archived SAR data from 1992 to 2005 has been analysed, individuating anomaly ground deformations in Pico de Teide and surrounding areas as suggested by GPS campaigns