Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data

Sharma, Ram C.; Tateishi, Ryosuke; Hara, Kohjiro; Nguyen, Hoan Thanh; Gharechelou, Saeid; Nguyen, Luong Viet

doi:10.3390/s17020235

Cited by 38 publications

(43 citation statements)

References 46 publications

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“…Optimal model parameters are reported in Table 1, and full a-posteriori probability density functions and trade-offs between fault parameters are shown in Figure 5. Assuming a rigidity modulus of 30 GPa, the computed optimal solution presents a seismic moment equal to 6.14 × 10 18 Nm, corresponding to a moment magnitude of 6.46, in agreement with the CMT solution (www.globalcmt.org).…”

Section: Resultssupporting

confidence: 71%

“…The InSAR method has become a widely used technique for extracting the deformation of the earth's surface resulting from natural and anthropic events [17][18][19]. The SAR data used in this study consist in a pair of images acquired along ascending orbit by the Sentinel 1-B (S1B) satellite, the second platform of S1 mission in the framework of the ESA Copernicus program (http://www.copernicus.eu/).…”

Section: Sar Datamentioning

confidence: 99%

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Did Anthropogenic Activities Trigger the 3 April 2017 Mw 6.5 Botswana Earthquake?

et al. 2017

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Abstract:On 3 April 2017, a M w 6.5 earthquake occurred in Botswana, representing the secondstrongest earthquake registered since 1949. Such an intraplate event occurred in a low seismic hazard area and was suspected to be an artificial earthquake induced by nearby anthropogenic activities (gas extraction). The possible relation between anthropogenic activities and the earthquake occurrence has been qualitatively investigated. We estimated the geometric and kinematic characteristics of the causative fault from the modeling of Sentinel-1 InSAR interferograms. Our best-fit solution for the main shock is represented by a normal fault located at a depth greater than 20 km, dipping 65 • northeast, with a right-lateral component, and a mean slip of 2.7 m. The retrieved fault geometry and mechanism are incompatible with the hypothetical stress perturbation caused by the anthropogenic activities performed in the area. Therefore, the 3 April 2017 Botswana earthquake can be classified as a natural intraplate earthquake.

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Section: Resultssupporting

confidence: 71%

Section: Sar Datamentioning

confidence: 99%

Did Anthropogenic Activities Trigger the 3 April 2017 Mw 6.5 Botswana Earthquake?

et al. 2017

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“…Building damages were investigated using postseismic high‐resolution satellite data . A visualization technique for the rapid detection of earthquake‐induced damages was implemented by synthetic aperture radar data . A road damage extraction method was proposed using road vector data overlaid on postearthquake images …”

Section: Introductionmentioning

confidence: 99%

“…20,21 A visualization technique for the rapid detection of earthquake-induced damages was implemented by synthetic aperture radar data. 22 A road damage extraction method was proposed using road vector data overlaid on postearthquake images. 23 With the modern breakthroughs in artificial intelligence fields, deep learning algorithms have become more popular in structural damage detection fields.…”

Section: Introductionmentioning

confidence: 99%

Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

Wei

Bao

et al. 2019

Struct Control Health Monit

154

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Summary This paper proposed a modified faster region‐based convolutional neural network (faster R‐CNN) for the multitype seismic damage identification and localization (i.e., concrete cracking, concrete spalling, rebar exposure, and rebar buckling) of damaged reinforced concrete columns from images. Four hundred raw images containing different damages and complicated background information are taken by a consumer‐grade camera in various locations and arbitrary perspectives to simulate the diverse situations where real‐world postearthquake damaged structural images are taken by nonprofessionals. Rectangular bounding boxes are obtained to localize multitype structural damages along with the corresponding category labels and classification probabilities. Data augmentation is implemented by rotation at every 90°, vertical and horizontal flipping operations. An interactive labeling process for the ground‐truth regions of the aforementioned damages is performed by a semiautomatic MATLAB program. A four‐step alternating training procedure is adopted on the basis of the mini‐batch stochastic gradient decent algorithm with momentum by backpropagation. Test results show that the trained faster R‐CNN can automatically identify and localize the aforementioned multitype seismic damages and the overall average precision reaches 80%. The relative errors of coordinates of the left‐top point obey minimum extreme value distributions, and those of width and height obey three‐parameter lognormal distributions. The intersection ratio between the identification and ground truth has a mean value of 0.88, and the width–height ratio obeys a two‐parameter lognormal distribution. Updated convolutional kernels in the first layer have shown trending, focusing, and line detectors for the feature extraction of multitype damages. Trending and focusing detectors contribute to the recognition of local damage regions, for example, concrete spalling and rebar exposure, whereas line detectors are more sensitive to the segmentation geometry, that is, concrete cracks.

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“…The resulting map of coherence loss provides a spatial estimate of where damage may have occurred. Coherence studies using this approach for the purpose of surface damage monitoring have typically used the longer-wavelength L-band radar systems (e.g., the Japanese ALOS satellites) [6,10,11], which are less sensitive to surficial changes and partially penetrate through ephemeral and variable surface features such as vegetation cover. The shorter wavelength X-band radar systems (e.g., TerraSAR-X [12] or COSMO-SkyMed [13]) are sensitive SAR systems for urban or vegetation-free areas.…”

Section: Introductionmentioning

confidence: 99%

Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

Olen

Bookhagen

2018

Remote Sensing

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The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6-12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth's surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.

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Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data

Cited by 38 publications

References 46 publications

Did Anthropogenic Activities Trigger the 3 April 2017 Mw 6.5 Botswana Earthquake?

Did Anthropogenic Activities Trigger the 3 April 2017 Mw 6.5 Botswana Earthquake?

Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

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