Ground-based SAR interferometry for monitoring mass movements

Antonello, G.; Casagli, Nicola; Farina, Paolo; Leva, D.; Nico, Giovanni; Sieber, A.J.; Tarchi, D.

doi:10.1007/s10346-003-0009-6

Cited by 198 publications

(113 citation statements)

References 13 publications

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“…Toc (Vajont), Voight (1988) found that rates can be suitably (Fig. 10a) are obtained by Bstacking^the interferograms phase using 8-h averaged SAR images, with a measurement accuracy of ±0.5 mm (Antonello et al 2004;Intrieri et al 2013;Di Traglia et al 2014;Di Traglia et al 2015). Interferometric phase can be corrupted by noise (decorrelation), caused by the contribution of scatterers summing up differently among SAR acquisitions and estimated by a calculation of the Bcoherence^between two images (values in the range 0-1).…”

Section: Effect Of Noise On the Reliability Of T F Predictionsmentioning

confidence: 99%

Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

et al. 2016

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Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.

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Section: Effect Of Noise On the Reliability Of T F Predictionsmentioning

confidence: 99%

Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The displacement is calculated from the phase difference between the back-scattered microwave signals received at different times through the cross-correlation of two SAR images. Images are obtained through sampling techniques, so frequency and spatial steps have to be selected in order to avoid ambiguity in range and cross-range (Antonello et al 2004). At Stromboli, the GBInSAR produces one SAR image of the NE flank of the crater area and of the upper part of the Sciara del Fuoco every 11 min.…”

Section: The Gbinsar Systemmentioning

confidence: 99%

“…Displacements can be estimated with high accuracy by stacking the phase of the interferograms, i.e., by processing many images together rather than adopting a more classic two-image approach. At Stromboli, the GBInSAR combines all the 8-hour-averaged images (Zebker et al 1997;Antonello et al 2004;Intrieri et al 2013a), producing cumulative displacement maps and displacement time series with an accuracy of ±0.5 mm (Di Traglia et al 2014c. Interferometric stacks are useful to highlight persistent deformation, suppressing other random signals, like atmospheric anomalies (Pinel et al 2014).…”

Section: The Gbinsar Systemmentioning

confidence: 99%

A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series

2016

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The evaluation of the intensity of unrest phases at active volcanoes is a crucial topic in volcano hazard studies. This is particularly troublesome in the case of persistently active volcanoes like Stromboli (Southern Italy), where intense eruptive summit activity (overflows, strong spattering, powerful explosions) has in some cases anticipated a flank eruption. In this context, a new approach for the analysis of displacement data is introduced. Daily displacements of the Stromboli crater terrace measured between January 1, 2010, and August 7, 2014, by a ground-based interferometric synthetic aperture radar system were compared, in retrospect, to displacement predictions provided by an autoregressive integrated moving average-based model. The methodology consisted in assessing when the actual displacements exceeded a fixed probability threshold for the forecasts (*95 %). Two sets of data were consequently produced: (1) series of residuals between actual displacements and model threshold (''anomalies'') and (2) series of normalized residuals between actual displacements and model threshold (''normalized anomalies''). This permitted to statistically identify and quantify the anomalous deformation at the crater terrace over the reference time interval of the analysis. Anomalies started to occur before each period of intense volcanic activity, highlighting the possibility to discern between background activity and unrest. Moreover, results indicated that the inflation of the crater terrace during the preparatory phase of the 2014 flank eruption was characterized by the greatest amount of anomalous deformation.

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“…GB-InSAR is a well-known technique, which has been widely used in deformation monitoring in civil structures and natural phenomena [42][43][44]. Due to its capabilities of offering optimal observing geometry and a significantly high-frequency data acquisition rate, GB-InSAR overcomes the limitations of space-borne DInSAR, such as a fixed revisit cycle and observation angle constraints.…”

Section: Ground-based Radar Interferometry Measurementsmentioning

confidence: 99%

Differential Radar Interferometry for Structural and Ground Deformation Monitoring: A New Tool for the Conservation and Sustainability of Cultural Heritage Sites

2015

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Abstract:Affected by natural and human-induced factors, cultural heritage sites and their surroundings face threats of structural instability and land displacement. Accurate and rapid identification of the key areas facing existing or potential deformation risks is essential for the conservation and sustainability of heritage sites, particularly for huge archaeological regions. In recent years, the successful application of differential radar interferometry techniques for the measurement of millimeter-level terrain motions has demonstrated their potential for deformation monitoring and preventive diagnosis of cultural heritage sites. In this paper, we review the principles of advanced differential radar interferometry approaches and their applicability for structural and ground deformation monitoring over heritage sites. Then, the advantages and challenges of these approaches are analyzed, followed by a discussion on the selection of radar interferometry systems for different archaeological applications. Finally, a workflow, integrating space-borne and ground-based differential radar interferometry technologies for deformation anomaly monitoring and preventive diagnosis of cultural heritage sites, is proposed.

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Ground-based SAR interferometry for monitoring mass movements

Abstract: An innovative technique for the remote assessment of ground displacements, based on radar interferometry and implemented using ground-based instrumentation (

Cited by 198 publications

References 13 publications

Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series

Differential Radar Interferometry for Structural and Ground Deformation Monitoring: A New Tool for the Conservation and Sustainability of Cultural Heritage Sites

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