INRAE TomoSAR service: a free scientific calculation on persistent and distributed scatterers radar interferometry

International audienceRecently, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) radar interferometry technique has been implemented as an open-source TomoSAR package (https://github.com/DinhHoTongMinh/TomoSAR). TomoSAR offers state-of-the-art algorithms to capture your movement best. However, it is easy to make you crazy with memory requirements. Due to so many images to calculate, it says for only the covariance matrix with 200 images of 500x2000 size, 45 GB should be allocated for that. For a small computer, it can be a task impossible. For our cluster, the RAM is capacity up to TB. The good news is we can process free of charge for you under a scientific collaboration

Use of Sentinel-2 images for the detection of precursory motions before landslide failures

The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We investigate the ability of these freely available optical images to detect precursory motions before rapid landslides. A 9-month time-series of displacement is derived from Sentinel-2 data over a major landslide in the French Alps, which exhibited a sudden reactivation in June 2016. This analysis reveals a 7-month period of low activity (<= 1 m), followed by a sudden acceleration of 3.2 +/- 1.2 m in 3 days, before the failure of a mass of about 2 to 3.6 10(6) m(3). The location of this precursory motion is consistent with that of the slow motions occurring since 2001 (about 1 m/year), as revealed by aerial photographs and LiDAR analysis. This change in activity over a very short period of time (days) emphasizes the value of the frequent revisit time of Sentinel-2, despite its medium resolution of 10 m. We finally simulate the ability of Sentinel-2 for detecting these precursory patterns before a rapid landslide occurs, based on typical Voight's laws for creeping materials, characterized by a power law exponent a. Based on this analysis and on global cloud cover maps, we compute the probability to detect pre-failure motions of landslides using the Sentinel-2 constellation. This probability is highly heterogeneous at the global scale, affected by the revisit time of the satellite and the cloud cover. However the main factors controlling this detection ability are the properties of the landslide itself (its size and the alpha parameter), with almost 100% of detection probability for alpha = 1.3 and 0% for alpha = 1.8. Despite all these limitations, probability to detect a motion before a landslide failure often reaches 50% for classical landslide parameters. These results open new perspectives for the early warning of large landslide motion from global and open source remote sensing data

Fusion of D-InSAR and sub-pixel image correlation measurementsfor coseismic displacement field estimation : application to the Kashmir earthquake (2005)

In geophysics, the uncertainty associated with model parameters or displacement measurements plays a crucial role in the understanding of geophysical phenomenon. An emerging way to reduce the geodetic parameter uncertainty is to combine a large number of data provided by SAR images. However, the measurements by radar imagery are subject to both random and epistemic uncertainties. Probability theory is known as the appropriate theory for random uncertainty, but questionable for epistemic uncertainty. Fuzzy theory is more adapted to epistemic uncertainty. Moreover, in a context of random and epistemic uncertainties, the conventional joint inversion in the least squares sense cannot be considered any more as the best scheme to reduce uncertainty. Therefore, in this article, in addition to joint inversion, two other fusion schemes, pre-fusion and post-fusion, are proposed. We consider here the conventional approach and an original fuzzy approach for handling random and epistemic uncertainties of D-InSAR and sub-pixel image correlation measurements. Joint inversion and pre-fusion are then applied to the measurement of displacement field due to the 2005 Kashmir earthquake by fusion of these data. The behaviours of these two fusion schemes versus uncertainty reduction are highlighted through comparisons of results

Displacement field and slip distribution of the 2005 Kashmir earthquake from SAR imagery

The 8th October 2005 Kashmir Earthquake MW 7.6 involved primarily thrust motion on a NE-dipping fault. Sub-pixel correlation of ENVISAT SAR images gives the location of the 80 km-long fault trace (within 300-800 m) and a 3D surface displacement field with a sub-metric accuracy covering the whole epicentral area. The slip distribution inverted using elastic dislocation models indicates that slip occurs mainly in the upper 10 km, between the cities of Muzaffarabad and Balakot. The rupture reached the surface in several places. In the hanging wall, horizontal motions show rotation from pure thrust to oblique right-lateral motion that are not observed in the footwall. A segmentation of the fault near Muzaffarabad is also suggested. North of the city of Balakot, slip decreases dramatically, but a diffuse zone of mainly vertical surface displacements, which could be post-seismic, exists further north, where most of the aftershocks occur aligned along the NW-SE Indus-Kohistan Seismic Zone. Copyright 2006 by the American Geophysical Union

Independent component analysis and parametric approach for source separation in InSAR time series at regional scale: application to the 2017–2018 slow slip event in Guerrero (Mexico)

Separating different sources of signal in Interferometric Synthetic Aperture Radar (InSAR) studies over large areas is challenging, especially between the long‐wavelength changes of atmospheric conditions and tectonic deformations, both correlated to elevation. In this study, we focus on the 2017–2018 slow slip event (SSE) in the Guerrero state (Mexico) where (1) the permanent GPS network has a low spatial density (less than 30 stations in an area of 300 urn:x-wiley:jgrb:media:jgrb54081:jgrb54081-math-0001 300 km) with uneven distribution; (2) the tropospheric phase delays can be as high as 20 cm of apparent ground displacements, with a complex temporal evolution; (3) the tested global weather models fail to correct interferograms with enough accuracy (with residual tropospheric signal higher than the tectonic signal); and (4) the surface displacement caused by the seismic cycle shows complex interactions between seismic sequences and aseismic events. To extract the SSE signal from Sentinel‐1 InSAR time series, we test two different approaches. The first (parametric method) consists of a least squares linear inversion, imposing a functional form for each deformation or atmospheric component. The second uses independent component analysis of the InSAR time series. We obtain time series maps of surface displacements along the radar line of sight associated with the SSE and validate these results with a comparison to GPS. Combining those two approaches, we propose a method to separate atmospheric delays and tectonic deformation on time series data not corrected from atmospheric delays. From the extracted ground deformation maps, we propose a first‐order slip inversion model at the subduction interface during this SSE

Monitoring of active tectonic deformations in the Longitudinal Valley (Eastern Taiwan) using Persistent Scatterer InSAR method with ALOS PALSAR data

International audienceThis paper presents new observation of the interseismic deformation along the Longitudinal Valley (Eastern Taiwan) that represents a major tectonic boundary of the Taiwan collision zone. We investigate the southern part of the Valley from Rueisuei to Taitung (latitude 23.5°N–22.7°N), which is the part of the Valley where interseismic surface creep has already been observed at some points of the Longitudinal Valley Fault (LVF). A Persistent Scatterer SAR interferometry approach (StaMPS) is applied using ten L-band SAR images from ALOS satellite acquired over the period 2007–2010. Interferograms from L-Band data show a dramatic improvement of coherence in comparison to previous studies using C-Band ERS data. The density of measurement resulting from StaMPS processing is the highest achieved so far in the area (about 40–55 points per km2 for a total of 77,000 points) allowing a continuous view of the deformation along the Valley and also giving information on its borders (Central Range and Coastal Range). The most striking feature of the resulting mean velocity map is a clear velocity discontinuity localized in a narrow band (0.1–1 km) along the LVF and responsible for up to 3 cm/yr velocity offset along the radar line of sight, which is attributed to shallow interseismic creep. InSAR results are in good agreement with continuous GPS measurements over the same period (0.3 cm/yr rms). The density of measurement allows us to improve fault trace map along the creeping section of the LVF (with accuracy of about 100 m) and to find new field evidences of the fault activity. In some places, our trace differs significantly (hundreds of meters) from previous published traces. The creep rate shows significant variations along the fault. At the southern end of the valley the deformation is distributed on several structures, including the Luyeh Strand, and drops significantly south of the Peinanshan. However there are discrepancies with previous studies made from ERS data over the period 1993–1999 that remain to be investigated. The mean velocity for each point of measure and the improved faults' trace are provided as Supplementary data