An Optimal Nearly-Analytic Discrete Method for 2D Acoustic and Elastic Wave Equations Dinghui Yang Depart. of Math., Tsinghua University Joint with Dr.

Slides:



Advertisements
Similar presentations
Courant and all that Consistency, Convergence Stability Numerical Dispersion Computational grids and numerical anisotropy The goal of this lecture is to.
Advertisements

Finite Difference Discretization of Hyperbolic Equations: Linear Problems Lectures 8, 9 and 10.
3 Copyright © Cengage Learning. All rights reserved. Applications of Differentiation.
5/4/2015rew Accuracy increase in FDTD using two sets of staggered grids E. Shcherbakov May 9, 2006.
EAGE Dubai 12/11/ Interpretation of hydrocarbon microtremors as pore fluid oscillations driven by ambient seismic noise Marcel.
Numerical Method for Computing Ground States of Spin-1 Bose-Einstein Condensates Fong Yin Lim Department of Mathematics and Center for Computational Science.
Numerical methods in the Earth Sciences: seismic wave propagation Heiner Igel, LMU Munich III The latest developments, outlook Grenoble Valley Benchmark.
MEASURES OF POST-PROCESSING THE HUMAN BODY RESPONSE TO TRANSIENT FIELDS Dragan Poljak Department of Electronics, University of Split R.Boskovica bb,
Numerical Uncertainty Assessment Reference: P.J. Roache, Verification and Validation in Computational Science and Engineering, Hermosa Press, Albuquerque.
CE33500 – Computational Methods in Civil Engineering Differentiation Provided by : Shahab Afshari
Session: Computational Wave Propagation: Basic Theory Igel H., Fichtner A., Käser M., Virieux J., Seriani G., Capdeville Y., Moczo P.  The finite-difference.
Error Measurement and Iterative Methods
Total Recall Math, Part 2 Ordinary diff. equations First order ODE, one boundary/initial condition: Second order ODE.
Computational Solutions of Helmholtz Equation Yau Shu Wong Department of Mathematical & Statistical Sciences University of Alberta Edmonton, Alberta, Canada.
Computer-Aided Analysis on Energy and Thermofluid Sciences Y.C. Shih Fall 2011 Chapter 6: Basics of Finite Difference Chapter 6 Basics of Finite Difference.
Chapter 1 Introduction The solutions of engineering problems can be obtained using analytical methods or numerical methods. Analytical differentiation.
October, Scripps Institution of Oceanography An Alternative Method to Building Adjoints Julia Levin Rutgers University Andrew Bennett “Inverse Modeling.
1/36 Gridless Method for Solving Moving Boundary Problems Wang Hong Department of Mathematical Information Technology University of Jyväskyklä
Finite Difference Time Domain Method (FDTD)
NUMERICAL DIFFERENTIATION The derivative of f (x) at x 0 is: An approximation to this is: for small values of h. Forward Difference Formula.
Pseudospectral Methods
Tutorial 5: Numerical methods - buildings Q1. Identify three principal differences between a response function method and a numerical method when both.
Finite Differences Finite Difference Approximations  Simple geophysical partial differential equations  Finite differences - definitions  Finite-difference.
© Arturo S. Leon, BSU, Spring 2010
Hybrid WENO-FD and RKDG Method for Hyperbolic Conservation Laws
Mathematical Models and Numerical Investigation for the Eigenmodes of the Modern Gyrotron Resonators Oleksiy KONONENKO RF Structure Development Meeting,
Representing Groundwater in Management Models Julien Harou University College London 2010 International Congress on Environmental Modelling and Software.
Lecture 3.
1 EEE 431 Computational Methods in Electrodynamics Lecture 4 By Dr. Rasime Uyguroglu
To Dream the Impossible Scheme Part 1 Approximating Derivatives on Non-Uniform, Skewed and Random Grid Schemes Part 2 Applying Rectangular Finite Difference.
An Optimization Method on Joint Inversion of Different Types of Seismic Data M. Argaez¹, R. Romero 3, A. Sosa¹, L. Thompson² L. Velazquez¹, A. Velasco².
7. Introduction to the numerical integration of PDE. As an example, we consider the following PDE with one variable; Finite difference method is one of.
+ Numerical Integration Techniques A Brief Introduction By Kai Zhao January, 2011.
台灣師範大學機電科技學系 C. R. Yang, NTNU MT -1- Chapter 11 Numerical Integration Methods in Vibration Analysis 11.
Computational fluid dynamics Authors: A. Ghavrish, Ass. Prof. of NMU, M. Packer, President of LDI inc.
Engineering Analysis – Computational Fluid Dynamics –
Quality of model and Error Analysis in Variational Data Assimilation François-Xavier LE DIMET Victor SHUTYAEV Université Joseph Fourier+INRIA Projet IDOPT,
Computation of the complete acoustic field with Finite-Differences algorithms. Adan Garriga Carlos Spa Vicente López Forum Acusticum Budapest31/08/2005.
1 EEE 431 Computational Methods in Electrodynamics Lecture 18 By Dr. Rasime Uyguroglu
Numerical Analysis – Differential Equation
Acoustic wave propagation in the solar subphotosphere S. Shelyag, R. Erdélyi, M.J. Thompson Solar Physics and upper Atmosphere Research Group, Department.
Modeling Electromagnetic Fields in Strongly Inhomogeneous Media
Discretization for PDEs Chunfang Chen,Danny Thorne Adam Zornes, Deng Li CS 521 Feb., 9,2006.
1 EEE 431 Computational Methods in Electrodynamics Lecture 7 By Dr. Rasime Uyguroglu
November, 2008 Bermuda ITW Numerical Simulation of Infrasound Propagation, including Wind, Attenuation, Gravity and Non-linearity Catherine de Groot-Hedlin.
The Backward Error Compensation Method for Level Set Equation Wayne Lawton and Jia Shuo Department of Mathematics National University.
Topics 1 Specific topics to be covered are: Discrete-time signals Z-transforms Sampling and reconstruction Aliasing and anti-aliasing filters Sampled-data.
Wave-Equation Migration in Anisotropic Media Jianhua Yu University of Utah.
NUMERICAL DIFFERENTIATION or DIFFERENCE APPROXIMATION Used to evaluate derivatives of a function using the functional values at grid points. They are.
1 EEE 431 Computational Methods in Electrodynamics Lecture 13 By Dr. Rasime Uyguroglu
The Finite-Difference Method Taylor Series Expansion Suppose we have a continuous function f(x), its value in the vicinity of can be approximately expressed.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Chapter 21 Numerical Differentiation.
EEE 431 Computational Methods in Electrodynamics
NUMERICAL DIFFERENTIATION Forward Difference Formula
Modeling of Traffic Flow Problems
7/21/2018 Analysis and quantification of modelling errors introduced in the deterministic calculational path applied to a mini-core problem SAIP 2015 conference.
Simple FD Gerard T. Schuster.
Lecture 19 MA471 Fall 2003.
THE METHOD OF LINES ANALYSIS OF ASYMMETRIC OPTICAL WAVEGUIDES Ary Syahriar.
NUMERICAL INVESTIGATIONS OF FINITE DIFFERENCE SCHEMES
17-Nov-18 Parallel 2D and 3D Acoustic Modeling Application for hybrid computing platform of PARAM Yuva II Abhishek Srivastava, Ashutosh Londhe*, Richa.
Chapter 23.
MATH 2140 Numerical Methods
The FOCI method versus other wavefield extrapolation methods
5.3 Higher-Order Taylor Methods
Diyu Yang Mentor: Xu Chen Advisor: José E. Schutt-Ainé Abstract
Topic 8 Pressure Correction
Numerical Computation and Optimization
An optimized implicit finite-difference scheme for the two-dimensional Helmholtz equation Zhaolun Liu Next, I will give u an report about the “”
Local Defect Correction for the Boundary Element Method
Presentation transcript:

An Optimal Nearly-Analytic Discrete Method for 2D Acoustic and Elastic Wave Equations Dinghui Yang Depart. of Math., Tsinghua University Joint with Dr. Peng, McMaster University Supported by the MCME of China and the MITACS

Outline Introduction Basic Nearly-Analytic Discrete Method(NADM) Optimal Nearly-Analytic Discrete Method (ONADM) Numerical Errors and Comparisons Wave-Field Modeling Conclusions

Introduction Computational Geophysics Geophysics: a subject of studying the earth problems such as inner structure and substance, earthquake, motional and changing law, and evolution process of the earth. Computational Geophysics: a branch of Geophysics, using computational mathematics to study Geophysical problems. Example: Wave propagation.

Model Problems to be solved: acoustic and elastic wave equations derived from Geophysics. Computational Issues: numerical dispersion, computational efficiency, computational costs and storages, accuracy.

Mathematical Model For the 2D case, the wave equation can be written as (1) Stress,Force source. displacement component, Let

TIM case

Computational Methods 1.High-order finite-difference (FD) schemes (Kelly et al.,1976; Wang et al., 2002) 2.Lax-Wendroff methods (Dablain, 86) 3.Others like optimally accurate schemes (Geller et al., 1998, 2000), pseudo-spectral methods (Kosloff et al., 1982)

Basic Nearly-Analytic Discrete Method (NADM) Using the Taylor expansion, we have (2) (3) Where denotes the time increment. We converted these high-order time derivatives to the spatial derivatives and included in Eqs. (2) and (3).

Actually, equation (1) can be rewritten as follows with the operators Where and are known elastic constant matrices. So we have

etc. To determine the high-order spatial derivatives, the NADM introduced the following interpolation function

Interpolation connections At the grid point (i-1, j):

Spatial derivatives expressed in term of the wave displacement and its gradients. etc.

Ideas: use the forward FD to approximate the derivatives of the so-called “velocity”, i.e., Computational Cost and Accuracy: 1.Needs to compute the so-called velocity and it’s derivatives. 2.In total, 57 arrays are needed for storing the displacement U, the velocity, and their derivatives. 3.2-order accuracy in time (Yang, et al 03)

Optimal Nearly-Analytic Discrete Method Improving NADM: Reduce additional computational cost Save storage in computation Increase time accuracy Observation We have

Merits of ONADM 1.No needs to compute the velocity and it’s derivatives in (4); 2.Save storage (53%): in total only 27 arrays are used based on the formula (6); 3.Higher time accuracy: ONADM (4-order) VS NADM(2-order); ONADM enjoys the same space accuracy as NADM.

Numerical Errors and comparisons The relative errors are defined by for the 1D case and for the 2D case

1D case Initial problem and Its exact solution

Fig. 1. The relative errors of the Lax-Wendroff correction (line 1), the NADM (line 2), and the ONADM (line 3).

Fig. 2. The relative errors of the Lax-Wendroff correction (line 1), the NADM (line 2), and the ONADM (line 3).

2D case Initial problem Its exact solution

Fig. 3. The relative errors of the second-order FD (line 1), the NADM (line 2), and the ONADM (line 3) for case 1.

Fig. 4. The relative errors of the second-order FD (line 1), the NADM (line 2), and the ONADM (line 3) for case 2.

Fig. 5. The relative errors of the second-order FD (line 1), the NADM (line 2), and the ONADM (line 3) for case 3.

Wave field modeling Wave propagation equations The time variation of the source function f i is with f 0 =15 Hz.

Fig. 6. Three-component snapshots at time 1.4s, computed by the NADM. Fig. 7. Three-component snapshots at time 1.4s, computed by the ONADM. It took about 3.4 minutes.

Figure 8. Synthetic seismograms.

Conclusions The new ONADM is proposed. The ONADM is more accurate than the NADM, Lax-Wendroff, and second-order methods. Significant improvement over NADM in storage (53%) and computational cost (32%). Much less numerical dispersion confirmed by numerical simulation.

Future works Theoretical analyses in numerical dispersion, stability, etc. Applications in heterogeneous and porous media cases. 3-D ONADM.

Thanks

Absorbing boundary conditions Where