Assessment of the Seismic Hazards of the Marikina Valley Fault from 2019 Mw 6.1 Castillejos Earthquake and Historical Events

Abstract The 2019 Mw 6.1 Castillejos earthquake occurred in the Zambales range of the central Luzon Island in Philippines. No active fault was reported around the seismogenic zone according to previous investigations. This earthquake draws attention for assessment in seismic risk along the Marikina Valley fault system (MVFS) near the Manila dense metropolitan population. The Coulomb failure stress (CFS) change on the MVFS is estimated by the coseismic faulting model derived from the inversion of coseismic deformation field observed from the Differential Interferometric Synthetic Aperture Radar using both the Advanced Land Observing Satellite-2 and Sentinel-1 Synthetic Aperture Radar (SAR) images. The predicted CFS change is less than 0.5 kPa that implies insignificant Coulomb stress accumulation on the MVFS after the Mw 6.1 Castillejos event. However, the recorded 14 moderate and strong earthquakes in and around the Luzon islands caused significant CFS drop on the MVFS. This might delay the occurrence of the earthquake for 0.2–50 yr on the MVFS.

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RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Sensors ◽

10.3390/s20102919 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2919 ◽

Cited By ~ 2

Author(s):

Agnieszka Chojka ◽

Piotr Artiemjew ◽

Jacek Rapiński

Keyword(s):

Image Processing ◽

Synthetic Aperture Radar ◽

Nearest Neighbor ◽

Synthetic Aperture ◽

Interferometric Synthetic Aperture Radar ◽

Sar Images ◽

Permanent Scatterers ◽

Level 1 ◽

Processing Techniques ◽

Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

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Study on CR Point Identification in SAR Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.204-208.1754 ◽

2012 ◽

Vol 204-208 ◽

pp. 1754-1757

Author(s):

Xue Min Xing

Keyword(s):

Synthetic Aperture Radar ◽

Correlation Coefficient ◽

Traditional Method ◽

Deformation Monitoring ◽

Synthetic Aperture ◽

Interferometric Synthetic Aperture Radar ◽

Corner Reflector ◽

Sar Images ◽

Aperture Radar

In the algorithm of Corner Reflector Interferometric Synthetic Aperture Radar (CR-InSAR), the identification of Corner Reflector (CR) points in SAR images is necessary. Due to the uncertainty of traditional method in estimating the row and column information of CR, this paper presents a method for CR points’ identification, which is based on the intensity and correlation coefficient. The method has been successfully used to find the CR points in the six SAR images of the study area where the identification of CR points installed along a high way is difficult. The results show that the method presented is effective and reliable which will play important role in the deformation monitoring in highway using CR-InSAR algorithm.

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The 29 March 2017 Yuzhno-Ozernovskoe Kamchatka Earthquake: Fault Activity in An Extension of the East Kamchatka Fault Zone as Constrained by InSAR Observations

Bulletin of the Seismological Society of America ◽

10.1785/0120190174 ◽

2020 ◽

Vol 110 (3) ◽

pp. 1101-1114

Author(s):

Magdalena S. Vassileva ◽

Mahdi Motagh ◽

Thomas R. Walter ◽

Hans-Ulrich Wetzel ◽

Sergey L. Senyukov

Keyword(s):

Synthetic Aperture Radar ◽

Fault Zone ◽

Subduction Zones ◽

Synthetic Aperture ◽

Fault System ◽

Coseismic Deformation ◽

Reverse Fault ◽

Nonlinear Inversion ◽

Fault Geometry ◽

Aperture Radar

ABSTRACT Recent earthquakes off the northeastern Kamchatka coast reveal that this region is seismically active, although details of the locations and complexity of the fault system are lacking. The northern part of Kamchatka has poor coverage by permanent seismic stations and ground geodetic instruments. Here, we exploit the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to characterize the fault geometry and kinematics associated with the 29 March 2017 Mw 6.6 Yuzhno-Ozernovskoe earthquake. The aim is to contribute to identifying the active fault branches and to better understanding the complex tectonic regime in this region using the DInSAR technique, which has never before been applied to the analysis of coseismic offsets in Kamchatka. We produced coseismic deformation maps using Advanced Land Observation Satellite-2 ascending and descending and Sentinel-1A descending Synthetic Aperture Radar (SAR) scenes and detected a predominant uplift up to 20 cm and a westward motion of approximately 7 cm near the shoreline. We jointly inverted the three geodetic datasets using elastic half-space fault modeling to retrieve source geometry and fault kinematics. The best-fit solution for the nonlinear inversion suggests a north–west-dipping oblique reverse fault with right-lateral rupture. The model fault geometry is not only generally consistent with the seismic data but also reveals that a hitherto unknown fault was ruptured. The identified fault structure is interpreted as the northern extension of the east Kamchatka fault zone, implying that the region is more complex than previously thought. Important implications arise for the presence of unknown faults at the edges of subduction zones that can generate earthquakes with magnitudes greater than Mw 6.

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