Relocation of the 1998 Zhangbei-Shangyi earthquake sequence using the double difference earthquake location algorithm

On January 10, 1998, at 11h50min Beijing Time (03h50min UTC), an earthquake of ML=6.2 occurred in the border region between the Zhangbei County and Shangyi County of Hebei Province. This earthquake is the most significant event to have occurred in northern China in the recent years. The earthquake-generating structure of this event was not clear due to no active fault capable of generating a moderate earthquake was found in the epicentral area, nor surface ruptures with any predominate orientation were observed, no distinct orientation of its aftershock distribution given by routine earthquake location was shown. To study the seismogenic structure of the Zhangbei- Shangyi earthquake, the main shock and its aftershocks with ML3.0 of the Zhangbei-Shangyi earthquake sequence were relocated by the authors of this paper in 2002 using the master event relative relocation technique. The relocated epicenter of the main shock was located at 41.145癗, 114.462癊, which was located 4 km to the NE of the macro-epicenter of this event. The relocated focal depth of the main shock was 15 km. Hypocenters of the aftershocks distributed in a nearly vertical plane striking 180~200 and its vicinity. The relocated results of the Zhangbei-Shangyi earthquake sequence clearly indicated that the seismogenic structure of this event was a NNE-SSW-striking fault with right-lateral and reverse slip. In this paper, a relocation of the Zhangbei-Shangyi earthquake sequence has been done using the double difference earthquake location algorithm (DD algorithm), and consistent results with that obtained by the master event technique were obtained. The relocated hypocenters of the main shock are located at 41.131癗, 114.456癊, which was located 2.5 km to the NE of the macro-epicenter of the main shock. The relocated focal depth of the main shock was 12.8 km. Hypocenters of the aftershocks also distributed in a nearly vertical N10E-striking plane and its vicinity. The relocated results using DD algorithm clearly indicated that the seismogenic structure of this event was a NNE-striking fault again.

Relocation of the M_S≥2.0 Earthquakes in the Northern Tianshan Region, Xinjiang, Using the Double-Difference Earthquake Relocation Algorithm

We applied the double-difference earthquake rdocation algorithm to 1348 earthquakes with Ms ≥2.0 that occurred in the northern Tianshan region, Xinjiang, from April 1988 to June 2003, using a total of 28701 P- and S-wave arrival times recorded by 32 seismic stations in Xinjiang. Aiming to obtain most of these Ms ≥ 2.0 earthquakes relocations, and considering the requirements of the DD method and the condition of data, we added the travel time data of another 437 earthquakes with 1.5 ≤ Ms 〈 2.0. Finally, we obtained the relocation results for 1253 earthquakes with Ms ≥2.0, which account for 93 % of all the 1348 earthquakes with Ms ≥ 2.0 and includes all the Ms ≥ 3.0 earthquakes. The reason for not relocating the 95 earthquakes with 2.0 ≤ Ms 〈 3.0 is analyzed in the paper. After relocation, the RMS residual decreased from 0.83s to 0.14s, the average error is 0.993 km in E-W direction, 1.10 km in N- S direction, and 1.33 km in vertical direction. The hypocenter depths are more convergent than before and distributed from 5 km to 35 kin, with 94% being from 5km to 35 kin, 68.2% from 10 km to 25 kin. The average hypocenter depth is 19 kin.

An algorithm for computed tomography image reconstruction from limited-view projections

With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.

Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System

This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.

A Reliable Iteration Algorithm for One-Bit Compressive Sensing on the Unit Sphere

The one-bit compressed sensing problem is of fundamental importance in many areas,such as wireless communication,statistics,and so on.However,the optimization of one-bit problem coustrained on the unit sphere lacks an algorithm with rigorous mathematical proof of convergence and validity.In this paper,an iteration algorithm is established based on difference-of-convex algorithm for the one-bit compressed sensing problem constrained on the unit sphere,with iterating formula■,where C is the convex cone generated by the one-bit measurements andη_(1)>η_(2)>1/2.The new algorithm is proved to converge as long as the initial point is on the unit sphere and accords with the measurements,and the convergence to the global minimum point of the l_(1)norm is discussed.

Relocation of aftershocks of the Wenchuan M_S8.0 earthquake and its implication to seismotectonics

From 14：28 （GMT＋8） on May 12th, 2008, the origin time of Ms8.0 Wenchuan earthquake, to December 31th, 2008, more than 10 000 aftershocks （M〉2.0） had been recorded by the seismic networks in Sichuan and surrounding areas. Using double difference algorithm, the main shock and more than 7 000 aftershocks were relocated. The aftershocks distribute about 350 km long. The depths of aftershocks are mainly between 10 km and 20 km. The average depth of aftershocks is about 13 km after relocation. In the southwest, the distribution of aftershocks is along the back-range fault, the central-range fault and the front-range fault of Longmenshan faults. In the middle, the distribution of aftershocks is along the central-range fault. In the north, aftershocks are relocated along the Qingchuan-Pingwu fault. Relocations suggest that the back-range fault mainly induced and controlled the aftershoek occurrence in the northern section of aftershocks sequence. The Ms8.0 main shock is between central-range and front-range of Longmenshan faults and is near the shear plane of the fault bottom. From the depth distribution of aftershock sequence, it suggests that these three faults show imbricate thrust structure.

Seismic relocation,focal mechanism and crustal seismic anisotropy associated with the 2010 Yushu M_S7.1 earthquake and its aftershocks

The 2010 Yushu MsT.1 earthquake occurred in Ganzi-Yushu fault, which is the south boundary of Bayan Har block. In this study, by using double difference algorithm, the locations of mainshock （33.13°N, 96.59°E, focal depth 10.22 km） and more than 600 aftershocks were obtained. The focal mechanisms of the mainshock and some aftershocks with Ms〉3.5 were estimated by jointly using broadband velocity waveforms from Global Seismic Network （GSN） and Qinghai Seismic Network as well. The focal mechanisms and relocation show that the strike of the fault plane is about 125° （WNW-ESE）, and the mainshock is left-laterally strikeslip. The parameters of shear-wave splitting were obtained at seismic stations of YUS and L6304 by systematic analysis method of shear-wave splitting （SAM） method. Based on the parameters of shear-wave splitting and focal mechanism, the characteristics of stress field in seismic source zone were analyzed. The directions of polarization at stations YUS and L6304 are different. It is concluded that after the mainshock and the Ms6.3 aftershock on April 14, the stress-field was changed.

UNSTEADY/STEADY NUMERICAL SIMULATION OF THREE-DIMENSIONAL INCOMPRESSIBLE NAVIER-STOKES EQUATIONS ON ARTIFICIAL COMPRESSIBILITY

A mixed algorithm of central and upwind difference scheme for the solution of steady/unsteady incompressible Navier-Stokes equations is presented. The algorithm is based on the method of artificial compressibility and uses a third-order flux-difference splitting technique for the convective terms and the second-order central difference for the viscous terms. The numerical flux of semi-discrete equations is computed by using the Roe approximation. Time accuracy is obtained in the numerical solutions by subiterating the equations in pseudotime for each physical time step. The algebraic turbulence model of Baldwin-Lomax is ulsed in this work. As examples, the solutions of flow through two dimensional flat, airfoil, prolate spheroid and cerebral aneurysm are computed and the results are compared with experimental data. The results show that the coefficient of pressure and skin friction are agreement with experimental data, the largest discrepancy occur in the separation region where the lagebraic turbulence model of Baldwin-Lomax could not exactly predict the flow.

Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video.However,how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge.In this paper,we propose a novel model of structured sparse-codingbased key frame extraction,wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error.To automatically extract key frames,a decomposition scheme is designed to separate the sparse coefficient matrix by rows.The rows enforced by the nonconvex group log-regularizer become zero or nonzero,leading to the learning of the structured sparse coefficient matrix.To solve the nonconvex problems due to the log-regularizer,the difference of convex algorithm(DCA)is employed to decompose the log-regularizer into the difference of two convex functions related to the l1 norm,which can be directly obtained through the proximal operator.Therefore,an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed,which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error.Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most Sum Me videos compared to the stateof-the-art methods.Furthermore,the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection(SMRS)and an 8% increase compared to SC-det on the VSUMM dataset.

State of charge estimation by finite difference extended Kalman filter with HPPC parameters identification

State of charge(SOC) is a key parameter of lithium-ion battery. In this paper, a finite difference extended Kalman filter(FDEKF)with Hybrid Pulse Power Characterization(HPPC) parameters identification is proposed to estimate the SOC. The finite difference(FD) algorithm is benefit to compute the partial derivative of nonlinear function, which can reduce the linearization error generated by the extended Kalman filter(EKF). The FDEKF algorithm can reduce the computational load of controller in engineering practice without solving the Jacobian matrix. It is simple of dynamic model of lithium-ion battery to adopt a secondorder resistor-capacitor(2 RC) network, the parameters of which are identified by the HPPC. Two conditions, both constant current discharge(CCD) and urban dynamometer driving schedule(UDDS), are utilized to validate the FDEKF algorithm.Comparing convergence rate and accuracy between the FDEKF and the EKF algorithm, it can be seen that the former is a better candidate to estimate the SOC.

SPARSE RECOVERY BASED ON THE GENERALIZED ERROR FUNCTION

In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are established.The practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are presented.Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances.Its practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.

ITERATIVE l1 MINIMIZATION FOR NON-CONVEX COMPRESSED SENSING

An algorithmic framework, based on the difference of convex functions algorithm （D- CA）, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence ofl1 minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit （l1 minimization） and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non-convex compressed sensing solvers in the literature, our method always out- performs basis pursuit, no matter how ill-conditioned the measurement matrix is. Moreover, the iterative l1 （ILl） algorithm lead by a wide margin the state-of-the-art algorithms on l1/2 and logarithimic minimizations in the strongly coherent （highly ill-conditioned） regime, despite the same objective functions. Last but not least, in the application of magnetic resonance imaging （MRI）, IL1 algorithm easily recovers the phantom image with just 7 line projections.

Image Segmentation via Fischer-Burmeister Total Variation and Thresholding

Image segmentation is a significant problem in image processing.In this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah model.Furthermore,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)iteration.In the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation result.To simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels well.Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.