Source Parameter Estimation of the 2009 Ms6.0 Yao’an Earthquake, Southern China, Using InSAR Observations

Qu, Wei; Zhang, Bing; Lü, Zhong; Kim, Jin‐Woo; Zhang, Qin; Gao, Yuan; Hao, Ming; Zhu, Wu; Qu, Feifei

doi:10.3390/rs11040462

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…InSAR has been widely used by researchers in different fields of research over the past decade. InSAR shows significant advantages in various fields of geoscience, including seismicity research [8,9,10,11], volcanic research [12], artificial building deformation monitoring [13], and surface deformation caused by underground resource exploitation [14,15]. In the case of urban areas research, Yang Zhang et al used RADARSAT-2 satellite data to monitor the land subsidence of Wuhan, China, with Small BAseline Subset (SBAS) [16].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS

Chen

Zhang

2019

Sensors

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In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area.

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Section: Introductionmentioning

confidence: 99%

Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS

Chen

Zhang

2019

Sensors

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“…De Novellis et al [15] estimated the source pa-rameters and its uncertainties for the 2015 Wolf volcano using the Okada and Yang models and the Levenberg-Marquardt method. Studies by Funning et al [16] and Qu et al [17] used the downhill simplex optimization and the Okada model to estimate the source parameters of the 2003 Bam earthquake and the 2009 Yao'an earthquake, respectively. Dicelis et al [18] estimated the source parameters of the 2008 Quetame earthquake using the nonlinear interior-point optimization and the Okada model.…”

Section: Earthquake Source Parameter Estimationmentioning

confidence: 99%

Parallel Dislocation Model Implementation for Earthquake Source Parameter Estimation on Multi-Threaded GPU

Lee

Kim

2021

Applied Sciences

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Graphics processing units (GPUs) have been in the spotlight in various fields because they can process a massive amount of computation at a relatively low price. This research proposes a performance acceleration framework applied to Monte Carlo method-based earthquake source parameter estimation using multi-threaded compute unified device architecture (CUDA) GPU. The Monte Carlo method takes an exhaustive computational burden because iterative nonlinear optimization is performed more than 1000 times. To alleviate this problem, we parallelize the rectangular dislocation model, i.e., the Okada model, since the model consists of independent point-wise computations and takes up most of the time in the nonlinear optimization. Adjusting the degree of common subexpression elimination, thread block size, and constant caching, we obtained the best CUDA optimization configuration that achieves 134.94×, 14.00×, and 2.99× speedups over sequential CPU, 16-threads CPU, and baseline CUDA GPU implementation from the 1000×1000 mesh size, respectively. Then, we evaluated the performance and correctness of four different line search algorithms for the limited memory Broyden–Fletcher–Goldfarb–Shanno with boundaries (L-BFGS-B) optimization in the real earthquake dataset. The results demonstrated Armijo line search to be the most efficient one among the algorithms. The visualization results with the best-fit parameters finally derived by the proposed framework confirm that our framework also approximates the earthquake source parameters with an excellent agreement with the geodetic data, i.e., at most 0.5 cm root-mean-square-error (RMSE) of residual displacement.

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“…Obtaining results from multiple starting points helps to find the global minimum and show the probabilistic descriptions such as standard deviation and histogram. Many studies employed the Monte-Carlo restarts to obtain the best-fit source parameters of earthquakes or volcanic activities [8,9,[16][17][18]. Besides, Bayesian estimation of source parameters can be used to determine the source parameters [19,20].…”

Section: Related Workmentioning

confidence: 99%

“…Since a nonlinear least square method iteratively explores the parameter search space, the search space size significantly affects the accuracy and execution time of the algorithm. Previous studies [8,9] used the initial range of search space relying on the focal mechanism from seismologists. However, the accuracy of seismological studies can influence the result of an inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA andk-Means Clustering

Lee

Kim

2020

Journal of Sensors

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The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem.

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Source Parameter Estimation of the 2009 Ms6.0 Yao’an Earthquake, Southern China, Using InSAR Observations

Cited by 8 publications

References 30 publications

Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS

Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS

Parallel Dislocation Model Implementation for Earthquake Source Parameter Estimation on Multi-Threaded GPU

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA andk-Means Clustering

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