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"Fluid pressure arrival-time tomography: Estimation and assessment in the presence of inequality constraints with an application to production at the Krechba field, Algeria
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Fluid pressure arrival time tomography: Estimation and assessment in the presence of inequality constraints, with an application to production at the Krechba field, Algeria
No full textVolcanic deformation mapping using PSInSAR: Piton de la Fournaise, Stromboli and Vulcano test sites for the Globvolcano project
No full textA Nonparametric Estimator for Coherent Change Detection: The Permutational Change Detection
Full text linkNowadays, synthetic aperture radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the interferometric-SAR (InSAR) to measure millimeter movements on the Earth's surface, aiming to monitor failures (e.g., landslides) or to measure the health state of infrastructures (e.g., mining assets, bridges, and buildings). In this article, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This article focuses on the temporal change detection framework, proposing a nonparametric coherent change detection (CCD) algorithm called permutational change detection (PCD), a purely statistical algorithm whose core is the permutational test. The PCD estimates the temporal change points (CPs) of a radar target recognizing blocks structure in the coherence matrix, namely, new radar objects. The algorithm has been fine-tuned for small SAR datasets, with the specific aim of prioritizing the analysis of the latest changes. A rigorous mathematical derivation of the algorithm is carried out, explaining how some limits have been addressed. Then, the performance analysis on the simulated data is deeply accomplished, carried out for the stand-alone PCD and the PCD compared with a parametric CCD algorithm based on the generalized likelihood ratio test (GLRT), and with the Omnibus and REACTIV detectors. The comparison with these other algorithms and the stand-alone performance analysis point out the robustness of the PCD in dealing with very noisy environments, even in the case of a single block. Finally, the PCD is validated by processing two Sentinel I data stacks, ascending and descending geometries, of the 2016 Central Italy earthquake
Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements
No full textOptical leveling campaigns, tiltmeters, GPS and InSAR are geodetic techniques used to detect and monitor surface deformation phenomena. In particular, InSAR data from satellite radar sensors are gaining increasing attention for their cost-effectiveness and unique technical features, making possible the monitoring of large areas, even revisiting the past. Moreover, more advanced InSAR techniques (PSInSARTM, SqueeSARTM) developed in the last decade are capable of providing millimeter precision, comparable to optical leveling, and a high spatial density of displacement measurements, over long periods of time without need of installing equipment or otherwise accessing the study area.
Thanks to the high density and quality of the measurements the PSInSAR data can be successfully used in geophysical inversion, to measure the permeability of oil reservoirs and/or to evaluate the possibilities and risks due to seismic faulting in the sequestration of CO2. In these cases, the precision, the sub weekly frequency of the measurements and the time required for the data to be available are the most important aspects, more relevant than the spatial resolution.
Until recently, the main limitation to the application of InSAR was the relatively long revisiting time (24 or 35 days) and the quite long waiting period for the delivery of the acquired data. The new Sentinel-1 mission, based on a constellation of two satellites, is expected to reduce such limitations guaranteeing a revisit cycle of 6 days on a global scale and in particular over Europe and Canada and providing a high level of service reliability with near-real-time delivery of data within 24 h, important for risk management applications. The new X band satellite SAR constellations like Cosmo Skymed and TerraSAR X have also a short revisiting time, from 4 to 11 days. However, their coverage is limited to well definite areas, and an expensive decision has to be made if to initiate the observations on any target. Sentinel 1, instead, yields global and costless observations and thus, after the end of the commissioning phase, will always produce present and past ground motion for any target.
It's important to underline that the millimeter accuracy, applying the InSAR analysis with Sentinel-1, will be achieved within a shorter observation time frame, thanks to the increased number of acquired images per year (Attema et al. 2010, De Zan et al., 2008). Results from ground based radar show that this improved precision is indeed achievable from C to Ku band, provided that an accurate model of the delay due to atmospheric water vapor is available or that precise reference points are close by
Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping
Full text linkInterferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones
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