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[4] J. RADON. Ober die Bestimmung von Funktio-nen durch ihre Integralwerte l¨angs gewisser Mannigfaltigkeiten. Computed Tomography, 27, 1983. [5] F. Autrusseau, A. Saadane, D. Barba “ Psychovisual Approach for Watermarking", SPIE Electronic Imaging : Security and watermarking of multimedia contents, January 2001, San Jose, CA. [6] M. Kutter and F. A. P. Petitcolas, “A fair benchmark for image watermarking systems” , Proc. SPIE Electronic Imaging Security and watermarking of multimedia contents, San Jose, CA, Vol. 3657, p. 226-239, January 1999. [7] JP. Guédon, B. Parrein, N. Normand, “ Internet distributed image databases", Integrated Computer Aided Engineering, Vol.8, p. 205-214, 2001. [8] M. Katz, “Questions of uniqueness and resolution in reconstruction from projections”, Springer Verlag, 1977. 128
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 129-132 Modeling Seismic Wave Propagation Using Graphics Processor Units (GPU) Zhangang Wang 1 , Suping Peng 1 , and Tao Liu 2 1 State Key Laboratory of Coal Resources and Mine Safety, China University of Mining and Technology, Beijing, China Email: millwzg@163.com 2 School of Earth and Space Sciences, Peking University, Beijing, China Email: liuluot@126.com Abstract—The main drawback of the seismic modeling in 2D viscoelastic media on a single PC is that simulations with large gridsizes require a tremendous amount of floating point calculations. To improve computation speedup, a graphic processing units (GPUs) accelerated method was proposed using the staggered-grid finite difference (FD) method. The geophysical model is decomposed into subdomains for PML absorbing conditions. The vertex and fragment processing are fully used to solve FD schemes in parallel and the latest updated frames are swapped in Framebuffer Object (FBO) attachments as inputs for the next simulation step. The simulation program running on modern PCs provides significant speedup over a CPU implementation, which makes it possible to simulate realtime complex seismic propagation in high resolution of 2048*2048 gridsizes on low-cost PCs. Index Terms—seismic, wave propagation, finite difference, viscoelastic media, model, GPU I. INTRODUCTION The main drawback of the FD method is that simulations with large model spaces or long nonsinusoidal waveforms can require a tremendous amount of floating point calculations and run times. In recent years the spatial parallelism on high-performance PC clusters makes it a promising method for seismic numerical computing in realistic (complex) media [1, 2]. Modern GPUs have now become ubiquitous in desktop computers and offer an excellent cost-toperformance-ratio, exceeding the performance of general purpose CPU by many times, due to their powerful and fully programmable parallel processing architectures. In the past few years, programmability of GPU and increased floating point precision has allowed GPU to perform general purpose computations [3, 4]. In this article, we have proposed a new implementation under the current GPU architecture to improve the real-time FD simulation of wave propagation in viscoelastic media, including domain decomposition and processing techniques. II. THEORY Early researches on FDM for elastic wave modeling in complex media gradually formulated finite difference schemes based on a system of high-order coupled elastic equations [5-8]. In such studies, however, the earth’s viscosity has been ignored and synthetic seismograms fail to model attenuation and dispersion of seismic waves. Day and Minster [9] made the first attempt to incorporate anelasticity into a 2-D time-domain modeling methods by applying a Pade approximant method. An efficient approach [10] was proposed based on the rheological model called “generalized standard linear solid” (GSLS), which was shown to explain experimental observations of wave propagation through earth materials [11]. Then a staggered grid finite difference technique was proposed based on the GSLS to model the propagation of seismic waves in 2D/3D viscoelastic media [12], for staggeredgrid operators are more accurate than standard grid to perform first derivatives for high frequencies close to the Nyquist limit [8]. A. Staggered-grid Finite difference formulation The first-order velocity-stress equations of viscoelastic wave propagation are given by s ⎧∂σ xy τ v ε ∂v ∂ x y ⎪ = μ ( + ) + γ xy ⎪ ∂t τσ ∂y ∂x ⎪ p s ∂σxx τ v x y v ε ∂v ∂ τ ∂ ε y ⎪ = π ( + ) −2μ ⋅ + γxx ⎪ ∂t τσ ∂x ∂y τσ ∂y ⎪ p s ⎪ ∂σ yy τ v v ε ∂ ∂ x y τε ∂vx = π ( + ) −2μ ⋅ + γ yy ⎪ ∂t τσ ∂x ∂y τσ ∂x ⎪ p s ⎪∂γ xx 1 τ v vy vy [ ( ε ∂ ∂ 1)( x τ ∂ xx ) 2 ( ε =− γ + π − + − μ − 1) ⋅ ] ⎪ ∂t τσ τσ ∂x ∂y τσ ∂y ⎨ p s ⎪∂γ yy 1 τ [ yy ( ε ∂v ∂v x y τε ∂vx ⎪ =− γ + π −1)( + ) −2 μ( −1) ⋅ ] ∂t τσ τσ ∂x ∂y τ σ ∂x ⎪ ⎪∂ s γ xy 1 τ v vy [ ( ε ∂ 1)( x ∂ ⎪ =− γ xy + μ − + )] ⎪ ∂ t τσ τσ ∂ y ∂ x ⎪∂vx 1 ∂σ ∂σ xy [ xx ⎪ = + + fx ] ⎪ ∂t ρ ∂x ∂y ⎪ ∂ vy 1 ∂ σxy ∂ σ ⎪ yy = [ + + f y ] ⎪⎩ ∂t ρ ∂x ∂y The moduli π, μ can be expressed as π = v ρR 2 2 p0 1 , iw0τ σ p + τε + iw0τ σ 1 1 μ = v ρR 2 2 s0 1 (1) (2) iw0τ σ s + τ ε + iw0τ σ 1 1 Where v P0 , v S0 denote the phase velocity at the centre frequency of the source(w 0 ) for P- and S-waves, respectively. The symbol R denotes the real part of the complex variable. The constants of stress relaxation times for both P-and S-waves, can be calculated by quality factor Q and angular frequency w. © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 129
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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Page 89 and 90: distance of view point. Given that
Page 93 and 94: Ⅳ. EVALUATION OF BLENDED LEARNING
Page 97 and 98: symmetric with respect to the origi
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Page 105 and 106: A. Data Preparing To generate our t
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Page 113 and 114: Strong earthquake 0.1< M L
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Page 121 and 122: B. Evaluation We have evaluated thi
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Page 125 and 126: Figure 3. Drainage networks generat
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Page 133 and 134: TABLE I. DESCRIPTION OF PROPOSITION
Page 135 and 136: Figure 2. The process of the invers
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Page 141 and 142: In order to reduce to the number of
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Page 147 and 148: Reference[10] analyzed the evolutio
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Page 151 and 152: After the comprehensive performance
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Page 157 and 158: The QoE i (T i ) is the ith user’
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Page 169 and 170: Keyboard event Application User mod
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Page 179 and 180: [3] Y. Li, S. M. Chung, and J. D. H
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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Eq.4 is NP-hard and can be solved b
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Where: G i is the selection field o
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Theorem 2.4([10]). Let L 1 and L 2
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The relation between the lattice im
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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in routing table. When search resou
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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