SAR Data Analysis in Solid Earth Geophysics: From Science to Risk Management

Atzori, Simone; Salvi, Stefano

doi:10.5772/57479

Cited by 9 publications

(6 citation statements)

References 105 publications

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“…Both tasks were based on synthetic data, so that the output of the algorithm can be unambiguously compared with reference (objective, 'true') values, and on two common geophysical cases of inversion in spaces with 4 ≤ n ≤ 18: modeling magma chambers of active volcanoes and of the finite seismic faults using GPS observations of ground deformation (cf. Cervelli et al 2001;Atzori & Salvi 2014). For both tasks, results are very encouraging for the application of this algorithm in specific demanding cases of inversion.…”

Section: Introductionmentioning

confidence: 74%

Multivariable inversion using exhaustive grid search and high-performance GPU processing: a new perspective

Venetis

Saltogianni

Stiros

et al. 2020

Geophysical Journal International

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SUMMARY Exhaustive searches in regular grids is a traditional and effective method for inversion, that is numerical solution of systems of non-linear equations which cannot be solved using formal algebraic techniques. However, this technique is effective for very few (3–4) variables and is slow. Recently, the first limitation was to a major degree overpassed with the new TOPological INVersion (TOPINV) algorithm which was used for inversion of systems with up to 18, or even more unknown variables. The novelty of this algorithm is that it is not based on the principle of the mean minimum misfit (cost function) between observations and model predictions, used by most inversion techniques. The new algorithm investigates for each gridpoint whether misfits of each observation are within specified uncertainty intervals, and stores clusters of ‘successful’ gridpoints in matrix form. These clusters (ensembles, sets) of gridpoints are tested whether they satisfy certain criteria and are then used to compute one or more optimal statistical solutions. The new algorithm is efficient for highly non-linear problems with high measurement uncertainties (low signal-to-noise ratio, SNR) and poor distribution of observations, that is problems leading to complicated 3-D mean misfit surfaces without dominant peaks, but it is slow when running in common computers. To overcome this limitation, we used GPUs which permit parallel processing in common computers, but faced another computational problem: GPU parallel processing supports only up to three dimensions. To solve this problem, we used CUDA programming and optimized the distribution of the computational load to all GPU cores. This leads up to 100x speedup relative to common CPU processing, as is derived from comparative tests with synthetic data for two typical inversion geophysical problems with up to 18 unknown variables, Mogi magma source modeling and elastic dislocation modeling of seismic faults. This impressive speedup makes the GPU/CUDA implementation of TOPINV practical even for low-latency solution of certain geophysical problems. This speedup in calculations also permitted to investigate the performance of the new algorithm in relation to the density of the adopted grids. We focused on a typical problem of elastic dislocation in unfavorable conditions (poor observations geometry, data with low SNR) and on synthetic observations with noise, so that the difference of each solution from the ‘true’/reference value was known (accuracy-based approach). Application of the algorithm revealed stable, accurate and precise solutions, with quality increasing with the grid density. Solution defects (bias), mainly produced by very coarse grids, can be identified through specific diagnostic criteria, dictating finer search grids.

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Section: Introductionmentioning

confidence: 74%

Multivariable inversion using exhaustive grid search and high-performance GPU processing: a new perspective

Venetis

Saltogianni

Stiros

et al. 2020

Geophysical Journal International

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“…In addition to the analytical model, optimisation algorithms are adopted to constrain the source parameters (Atzori and Salvi, 2014). We carry out preliminary nonlinear optimisation based on the Levemberg-Marquardt algorithm (Marquardt, 1963) to identify the sliding surface geometry (dimension, position and inclination) assuming a uniform slip in a downhill direction.…”

Section: Estimation Of the Geometry And Slip Distribution Of The Pote...mentioning

confidence: 99%

Analysis of a large, seismically-induced mass movement after the December 2018, Etna volcano (southern Italy) seismic swarm

Albano¹,

Saroli²,

Atzori³

et al. 2021

Preprint

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<p>In the last decades, satellite monitoring techniques allowed to discover non-catastrophic slope movements triggered by earthquake shaking and involving deep blind sliding surfaces of old paleo-landslides. Understanding the triggering and attenuation mechanisms of such mass movements is crucial to assess their hazard. On December 2018, the Etna volcano (southern Italy) began a very intense eruption, accompanied by a seismic swarm with magnitudes up to 4.9. Synthetic Aperture Radar data from Sentinel-1 and ALOS-2 identified some local displacements over a hilly area to the southwest of the Etna volcano, near Patern&#242; village. We evaluated the contribution of seismically-induced surface instabilities to the observed ground displacement by employing a multidisciplinary analysis comprising geological, geotechnical and geomorphological data, together with analytical and dynamic modelling. The results of our study allowed us to identify the geometry and kinematics of a previously unknown paleo-landslide. A pseudostatic, limit-equilibrium back-analysis of the landslide mass highlighted that the displacements detected by InSAR data were caused by the undrained seismic instability of the landslide mass, which was dormant before the volcanic eruption, under the light-to-moderate seismic shacking of the December 26, Mw 4.9 earthquake. Such a new observation allowed to identify the geometry and kinematics of a previously unknown landslide mass and confirms that earthquakes have a cumulative effect on landslides that doesn't necessarily manifest as a failure but could evolve in a catastrophic collapse after several earthquakes. Such an aspect must be adequately investigated to identify unknown quiescent landslide bodies and to prevent the effects of their potential collapse during an earthquake.</p>

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“…Once deformation rates are decomposed in the horizontal and vertical directions, parameters like slip rate, dilatation or strain can be estimated more precisely. Those parameters, in turn, are of importance for defining the boundary conditions of the respective faults within seismic hazard models [44].…”

Section: Techniques For Decomposing the Dinsar Signal Into A 3-dimensmentioning

confidence: 99%

Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy

et al. 2017

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Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of the signal, especially in regions that are subject to multiple deformation processes. The Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements (SISTEM) algorithm enables decomposition into a three-dimensional velocity field through joint inversion with GNSS measurements, but has never been applied to interseismic deformation where strain rates are low. Here, we apply SISTEM for the first time to detect tectonic deformation on the Hyblean Foreland Plateau in South-East Sicily. In order to increase the signal-to-noise ratio of the DInSAR data beforehand, we reduce atmospheric InSAR noise using a weather model and combine it with a multi-directional spatial filtering technique. The resultant three-dimensional velocity field allows identification of anthropogenic, as well as tectonic deformation, with sub-centimeter accuracies in areas of sufficient GPS coverage. Our enhanced method allows for a more detailed view of ongoing deformation processes as compared to the single use of either GNSS or DInSAR only and thus is suited to improve assessments of regional seismic hazard.

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SAR Data Analysis in Solid Earth Geophysics: From Science to Risk Management

Cited by 9 publications

References 105 publications

Multivariable inversion using exhaustive grid search and high-performance GPU processing: a new perspective

Multivariable inversion using exhaustive grid search and high-performance GPU processing: a new perspective

Analysis of a large, seismically-induced mass movement after the December 2018, Etna volcano (southern Italy) seismic swarm

Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy

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