Postseismic displacement of the 1999 Athens earthquake retrieved by the Differential Interferometry by Synthetic Aperture Radar time series

Este documento presenta la formulación y primeros pasos de un proyecto de Doctorado Industrial, desarrollado en elmarco del proyecto Kuk ahpán que tiene como objetivo comprender, monitorear y modelar procesos tectónicos a escalalitosférica en Centroamérica. Para ello, un equipo internacional de seis países (Nicaragua, Costa Rica, El Salvador,Guatemala, Noruega y España) trabaja integrando la investigación en diversas técnicas e ingenierías Geofísicas, con elobjetivo de actualizar los Mapas de Riesgo Sísmico de la Región, un insumo crítico. para los códigos de seguridad yconstrucción. El proyecto de doctorado propuesto se enmarca en la investigación y desarrollo de tecnologías para prevenirlos riesgos geológicos naturales e inducidos que afectan a ciudades e infraestructuras en países altamente vulnerables,utilizando la tecnología DInSAR (Differential Interferometry with Synthetic Aperture Radar) optimizada por la startupDetektia Earth Surface Monitoring en colaboración con la Universidad Politécnica de Madrid. La interferometría diferencialde radar de apertura sintética es una técnica basada en el procesamiento y análisis de series largas de imágenes de radarde apertura sintética. Esta tecnología proporciona registros (desde 1992) y movimientos actualizados en cualquiersuperficie en cualquier parte del mundo sin necesidad de instrumentación terrestre, con precisiones de alrededor de 1 mm/ año (velocidad). En este contexto, el radar satelital proporciona información valiosa sobre áreas muy grandes quecomplementan el trabajo de campo y la instrumentación in situ. Primero, comenzamos integrando datos DInSAR condiversos datos geofísicos como batimetría, geomagnetismo, gravimetría, perfiles sísmicos… para mapear completamentela falla Swan sobre Honduras y Guatemala. Usamos esta tecnología para abordar el riesgo sísmico sobre la falla y áreascercanas. En un segundo paso, aplicaremos esta evaluación de riesgo sísmico (incluyendo amenazas naturales yantropogénicas) en ciudades e infraestructuras críticas en Centroamérica.

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An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Applied Sciences ◽

10.3390/app9173561 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3561 ◽

Cited By ~ 2

Author(s):

Liu ◽

Wang ◽

Huang ◽

Yang

Keyword(s):

Time Series ◽

Synthetic Aperture Radar ◽

Noise Signal ◽

Second Order ◽

Synthetic Aperture ◽

Blind Identification ◽

Monitoring Point ◽

Whitening Process ◽

Mixing Matrix ◽

Aperture Radar

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

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Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar

Remote Sensing ◽

10.3390/rs11192273 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2273 ◽

Cited By ~ 2

Author(s):

Hongguo Jia ◽

Hao Zhang ◽

Luyao Liu ◽

Guoxiang Liu

Keyword(s):

Time Series ◽

Synthetic Aperture Radar ◽

Deformation Monitoring ◽

Synthetic Aperture ◽

Economic Losses ◽

Interferometric Synthetic Aperture Radar ◽

Mountainous Areas ◽

Landslide Area ◽

Landslide Deformation ◽

Aperture Radar

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.

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