Open access peer-reviewed chapter

A High-Order Finite Volume Method for 3D Elastic Modelling on Unstructured Meshes

By Wensheng Zhang

Submitted: January 22nd 2019Reviewed: April 17th 2019Published: May 17th 2019

DOI: 10.5772/intechopen.86400

Downloaded: 81

Abstract

In this chapter, a new efficient high-order finite volume method for 3D elastic modelling on unstructured meshes is developed. The stencil for the high-order polynomial reconstruction is generated by subdividing the relative coarse tetrahedrons. The reconstruction on the stencil is performed by using cell-averaged quantities represented by the hierarchical orthonormal basis functions. Unlike the traditional high-order finite volume method, the new method has a very local property like the discontinuous Galerkin method. Furthermore, it can be written as an inner-split computational scheme which is beneficial to reducing computational amount. The reconstruction matrix is invertible and remains unchanged for all tetrahedrons, and thus it can be pre-computed and stored before time evolution. These special advantages facilitate the parallelization and high-order computations. The high-order accuracy in time is obtained by the Runge-Kutta method. Numerical computations including a 3D real model with complex topography demonstrate the effectiveness and good adaptability to complex topography.

Keywords

  • numerical solutions
  • computational seismology
  • 3D elastic wave
  • wave propagation
  • high-order finite volume method
  • unstructured meshes

1. Introduction

Wave propagation based on wave equations has important applications in geophysics. It is usually used as a powerful tool to detect the structures of reservoir. Thus solving wave equations efficiently and accurately is always an important research topic. There are several types of numerical methods to solve wave equations, for example, the finite difference (FD) method [1, 2], the pseudo-spectral (PS) method [3, 4], the finite element (FE) method [5, 6, 7, 8, 9], the spectral element (SE) method [10, 11, 12, 13, 14], the discontinuous Galerkin (DG) method [15, 16, 17, 18], and the finite volume (FV) method [19, 20, 21, 22]. Each numerical method has its own inherent advantages and disadvantages. For example, the FD method is efficient and relatively easy to implement, but the inherent restriction of using regular meshes limits its application to complex topography. The FE method has good adaptability to complex topography, but it has huge computational cost. In this chapter, the FV method is the key consideration.

In order to simulate wave propagation on unstructured meshes efficiently, the FV method is a good choice due to its high computational efficiency and good adaptability to complex geometry. In this chapter an efficient FV method for 3D elastic wave simulation on unstructured meshes is developed. It incorporates some nice features from the DG and FV methods [15, 16, 17, 19, 20, 23] and the spectral FV (SFV) method [24, 25, 26]. In our method, the computational domain is first meshed with relative coarse tetrahedral elements in 3D or triangle elements in 2D. Then, each element is further divided as a collection of finer subelements to form a stencil. The high-order polynomial reconstruction is performed on this stencil by using local cell-averaged values on the finer elements. The resulting reconstruction matrix on all coarse elements remains unchanged, and it can be pre-computed before time evolution. Moreover, the method can be written as an inner-split computational scheme. These two advantages of our method are very beneficial to enhancing the parallelization and reducing computational cost.

The rest of this chapter is organized as follows. In Section 2, the theory is described in detail. In Section 3, numerical results are given to illustrate the effectiveness of our method. Finally, the conclusion is given in Section 4.

2. Theory

2.1 The governing equation

The three-dimensional (3D) elastic wave equation with external sources in velocity-stress formulation can be written as the following system [1, 15]:

σxxtλ+2μuxλvyλwz=g1,σyytλuxλ+2μvyλwz=g2,σzztλuxλvyλ+2μwz=g3,σxytμvx+uy=g4,σyztμvz+wy=g5,σxztμuz+wx=g6,ρutσxxxσxyyσxzz=ρg7,ρvtσxyxσyyyσyzz=ρg8,ρwtσxzxσyzyσzzz=ρg9,E1

where u, v, and ware the wavefield of particle velocities in x, y, and zdirections, respectively; λand μare the Lamé coefficients and ρis the density; gixyztare the known sources; σxx, σyy, and σzzare the normal stress components while σxy, σxz, and σyzare the shear stresses. For the convenient of discussion, we rewrite Eq. (1) as the following compact form:

ut+Aux+Buy+Cuz=g,E2

where g=g1g9T,u=σxxσyyσzzσxyσyzσxzuvwT, and the matrices A, B, and Care all 9×9matrices and can be obtained obviously [27].

The propagation velocities of the elastic waves are determined by the eigenvalues siof matrices A, B, and Cand are given by

s1=vp,s2=vs,s3=vs,s4=s5=s6=0,s7=vs,s8=vs,s9=vp,E3

where

vp=λ+2μρ,vs=μρE4

are the velocities of the compression (P) wave and the shear (S) wave velocities, respectively.

2.2 The generation of a stencil

Suppose that the 3D computational domain Ωis meshed by NEconforming tetrahedral elements Tm:

Ω=m=1NETm.E5

In practical computations, the integrals in the FV scheme on physical tetrahedral element Tmare usually changed to be computed on its reference element. Figure 1 shows a physical tetrahedron Tmin the physical system, and xyzis transformed into a reference element TEin the reference system ξηζ. Let xiyizifor i=1,2,3,4be the coordinates of physical element Tm. The transformations between xyzsystem and ξηζsystem will be given in the final subsection of Section 2. For convenience, let x=xyzand ξ=ξηζ. And denote the transformation from ξηζsystem to xyzsystem by

Figure 1.

The physical element T m (left) in the physical coordinate system x − y − z is transformed into a reference element T E (right) in the reference coordinate system ξ − η − ζ .

x=xTmξ,E6

and its corresponding inverse transformation by

ξ=ξTmx.E7

The detailed expressions of the transformations (6) and (7) will be given in Section 2.5.

Inside each TEthe solutions of Eq. (2) are approximated numerically by using a linear combination of polynomial basis functions ϕlξηζand the time-dependent coefficients ŵlmt:

umξηζt=l=1Npŵlmtϕlξηζ,E8

where Npis the degree of freedom of a complete polynomial.

In order to construct a high-order polynomial, we need to choose a stencil. Traditionally, the elements being adjacent to the element Tmare selected to form a stencil. In [20] three types of stencils, i.e., the central stencil, the primary sector stencil, and the reverse stencil, are investigated. These stencils usually choose 2Nneighbors for the 3D reconstruction. Here Nis the degree of a complete polynomial. Due to geometrical issues, the reconstruction matrix resulting from these stencils may be not invertible. This may happen when all elements are aligned in a straight line [20]. In the following, we propose to partition Tmor in fact its corresponding reference element TEinto finer subelements to form a stencil. The subdivision algorithm guarantees the number of subelements is greater than the degrees of freedom of a complete polynomial. Moreover, this algorithm is easy to implement especially in 3D and for all elements whether they are internal or boundary elements.

Let Nebe the number of subelements in Tmafter subdividing. For a complete polynomial of degree Nin 3D, a reconstruction requires at least Npsubelements, where

Np=N+1N+2N+3/6.E9

In our algorithm, we guarantee Neis always greater than Np. As shown in Figure 2, we divide each edge of the reference element TEinto Muniform segments. Thus we have NeM3tetrahedral subelements in TE. Note that a small subcubic in TEconsists of six tetrahedrons. With the transformations of Eqs. (6) and (7), we denote all subelements in Tmfor a fixed mby Tmkfor k=1,,Ne. In Table 1, the degree of a complete polynomial Nand its corresponding degrees of freedom Npare listed. Correspondingly, the number of Mand Neare also listed in Table 1. This algorithm for generating the stencil is easily implemented for all coarse tetrahedrons. Moreover, the reconstruction matrix resulting from this stencil is always invertible and remains unchanged for all elements Tmfor m=1,,NE. Note that the reconstruction matrix may be not invertible if all elements are aligned on a straight line [15]. However, this will not happen here for our algorithm.

Figure 2.

The stencil obtained by subdividing the reference element T E into M 3 = 3 3 tetrahedral subelements, where M = 3 is the number of uniform segments on each edge of T E . Note that a small subcubic (red) in T E consists of six tetrahedrons.

N1234
Np4102035
M2334
Ne8272764

Table 1.

The degree of a complete polynomial Nand its corresponding degrees of freedom Npare listed. Correspondingly, the number of uniform segments Mon each edge and the number of subelements Neare also listed.

2.3 The high-order polynomial reconstruction

The high-order polynomial is reconstructed in each element Tmor TE. For the stencil designed above, we have

Tm=k=1NeTmk,E10

where k=1,,Neis the index for subelements in Tm. The FV method will use the cell-averaged quantities, i.e.,

u¯mk=1TmkTmkumxdV,k=1,,Ne,E11

to reconstruct a high-order polynomial, where Tmkrepresents the volume of the subelement Tmk. The time variable tin umis omitted for discussion convenience. The reconstruction requires integral conservation for umin each subelement Tmk, i.e.,

TmkumxTmξdV=Tmku¯mk,TmkTm,k=1,,Ne.E12

To solve the reconstruction problem, inspired by the DG method [15, 16, 17, 23, 28, 29], we use hierarchical orthogonal basis functions. The basis functions ϕlξηζof a complete polynomial of degree N(N=1,2,3,4) in the reference coordinate system can be found in [27]. We remark that the basis functions are orthonormal and satisfy the following property:

TEϕlξηζdξdηdζ=66,l=1,0,l1.E13

Transforming equation (12) in the physical coordinate system xyzinto the reference coordinate system ξηζand noticing Eq. (8), we obtain

l=1NpT˜mkϕlξηζdξdηdζŵlm=T˜mku¯mk,T˜mkT˜m=TE,k=1,,Ne,E14

where T˜mis in fact the reference element TEand T˜mkis the transformed element corresponding to the subelement Tmk.

The integration in Eq. (14) over T˜mkin ξsystem can be computed efficiently if it is performed over its reference element in a second reference system ξ˜. Denote the transformation from ξ˜to ξand its inverse by ξ=ξT˜mkξ˜and ξ˜=ξ˜T˜mkξ, respectively. Transforming Eq. (14) into ξ˜system and rewriting the result as a compact form, we have

Gŵ=u¯,E15

where Gis the Ne×Npmatrix with entries Gklgiven by

Gkl=1TETEϕlξT˜mkξ˜dξ˜dη˜dζ˜,k=1,,Ne;l=1,,Np,E16

and

u¯u¯m1u¯m2u¯mNeT,ŵŵ1mŵ2mŵNpmT.E17

We need at least Npsubelements in the stencil since the reconstructed number of degrees of freedom is Np. As listed in Table 1, Nesubelements are used to form the stencil. Note that Neis definitely larger than Np, which is helpful to improve the reconstruction robustness [20, 21]. Thus Eq. (15) is an overdetermined problem. We use the constrained least squared technique to solve it.

From the orthogonality of basis functions and the property of Eq. (13), we remark that Eq. (15) is subject to the following constraint condition [27]:

6ŵ1m=k=1Neu¯mkNe.E18

With the constraint, Eq. (15) is solved by the Lagrange multiplier method [19, 20, 27]. And the system can be written as

2GTGRTR0ŵλp=2GTu¯R˜u¯,E19

where λpis the Lagrangian multiplier and both Rand R˜are 1×Nematrices:

R=600,R˜=1Ne1Ne.E20

The coefficient matrix on the left-hand side of Eq. (19) is the so-called reconstruction matrix [19, 20].

2.4 The spatial discrete formulation

We now derive the semi-discrete finite volume scheme based on Eqs. (2) and (8). Integrating over each subelement Tmkon both sides of Eq. (2), we have

TmkutdV+TmkAux+Buy+CuzdV=0,k=1,,Ne.E21

Using Eq. (8) and integration by parts yield

TmkutdV+TmkFhdS=0,E22

where dSdenotes the infinitesimal element in the face integral and Fhis the numerical flux, and we adopt the widely used Godunov flux [15, 19, 20, 23]

Fh=12TAmk+AmkT1l=1Npŵlmϕlm+12TAmkAmkT1l=1Npŵlmjϕlmj,E23

where mjis the index number of coarse tetrahedral element neighboring subelement Tmk. The notation Amkdenotes applying the absolute value operator of the eigenvalues given in Eq. (3), i.e.,

Amk=RΛR1,Λ=diags1s9,E24

where Ris the matrix and its columns are made up of the eigenvectors associated with eigenvalues in Eq. (3), i.e.,

R=λ+2μ0000000λ+2μλ0001000λλ0000100λ0μ00000μ000010000000μ000μ00vp0000000vp0vs00000vs000vs000vs00.E25

And Tis the rotation matrix given by

T=nx2sx2tx22nxsx2sxtx2nxtxny2sy2ty22nysy2syty2nytynz2sz2tz22nzsz2sztz2nztznynxsysxtytxnysx+nxsysytx+sxtynytx+nxtynznyszsytztynzsy+nyszszty+sytznzty+nytznznxszsxtztxnzsx+nxszsztx+sxtznztx+nxtz,E26

where nxnynzis the normal vector of the face and sxsyszand txtytzare the two tangential vectors. T1denotes the inverse of T.

Inserting Eqs. (23) into (22) and rewriting the result into a splitting form of easy computation in the reference system ξ, we have

tu¯mkTmk+j=14Fjh=0E27

with

Fjh=TjAmkTj1Sjl=1NpFl,jŵlm,m=mj,E28

and

Fjh=12TjAmk+AmkTj1Sjl=1NpFl,jŵm+12TjAmkAmkTj1Sjl=1NpFl+,i,pŵlmj,mmj,E29

where Sjis the area of the j-th j=1,2,3,4face of subelement Tmk. Fl,jand Fl+,i,pare the left flux matrix and the right state flux matrix, respectively, which are given by

Fl,j=TEjϕlξT˜mjξ˜jχτdχdτ,j=1,2,3,4,E30
Fl+,i,p=TEjϕlξT˜miξ˜iχ˜pτ˜pdχdτ,i=1,2,3,4;p=1,2,3.E31

where χand τare the face parameters. The transformation of the face parameters χand τto the face parameters χ˜and τ˜in the neighbor tetrahedron depends on the orientation of the neighbor face with respect to the local face of the considered tetrahedron. And the mapping is given in Table 2. For a given tetrahedral mesh with the known indices iand p, there are only 4 of 12 possible matrices F+,i,pper element [15, 20]. Comparing with the traditional FV method, the method with the splitting form described above has much less computations of face integrations. Note that only our proposed FV method can be written as a splitting form. Theoretical analysis shows our method can save about half computational time under the condition of the same number of elements [27].

p123
χ˜τ1χτχ
τ˜χτ1χτ

Table 2.

Transformation of the face parameters χand τto the face parameters χ˜and τ˜.

2.5 The time discretization

Equation (27) is in fact a semi-discrete ordinary differential equation (ODE) system. In order to solve it formally, we denote the spatial semi-discrete part in Eq. (27) by a linear operator L. Then Eq. (27) can be written as a concise ODE form:

dudt=Lut.E32

Traditionally, the classic fourth-order explicit RK (ERK) method

k1=Luntn,k2=Lun+12Δtk1tn+12Δt,k3=Lun+12Δtk2tn+12Δt,k4=Lun+Δtk3tn+Δt,un+1=un+16Δtk1+2k2+2k3+k4E33

can be applied to advance ufrom unto un+1. Here Δtis the time step. Now we use the low-storage version of ERK (LSERK) to solve Eq. (32):

u0=un,ki=aiki1+ΔtLpi1tn+ciΔt,pi=pi1+biki,i=1,,5,un+1=p5.E34

As we can see the LSERK only requires one additional storage level, while ERK has four. The coefficients required in Eq. (34) are listed in Table 3 [30].

iaibici
100.14965902199922910
2−0.41789047449985190.37921031299962730.1496590219992291
3−1.19215169464267690.82295502938698170.3704009573642048
4−1.69778469247152790.69945045594912210.6222557631344432
5−1.51418344425715580.15305724796815200.9582821306746903

Table 3.

Coefficients for the low-storage five-stage fourth-order ERK method.

As to the stability condition, it is controlled by the Courant-Friedrichs-Lewy (CFL) condition [15, 19];

Δt12N+1hminvp,E35

where vpis the Pwave velocity and hminis the minimum diameter of the circumcircles of tetrahedral elements. This condition is a necessary condition for discrete stability, and a bit more restrictive form is actually used in numerical computations.

The absorbing boundary conditions (ABCs) in computations are required as the computational domain is finite. There are two typical ABCs to be adopted here. One is flux type ABCs [16, 19]. That is to say, the following numerical flux in Eq. (23) at all tetrahedral faces that coincide with domain boundary

Fh=12TAmk+AmkT1l=1Npŵlmϕlm,E36

which allows only for outgoing waves and is equivalent to the first order ABCs. Though the absorbing effects of this method vary the angles of incidence, it is still effective in many cases [19]. The advantage of this type ABCs is that it merged into the FVM framework naturally and there is almost no additional computational cost. Another type is the perfectly matched layer (PML) technique originally developed by [31], which is very popular in recent more 10 years.

2.6 Coordinate transformation

The transformation between different coordinate systems is frequently used. For ease of reading, we present the formulations here. Let xiyizifor i=1,2,3,4be the coordinates of a physical element. The transformation from ξηζsystem to xyzsystem is defined by

x=x1+x2x1ξ+x3x1η+x4x1ζ,y=y1+y2y1ξ+y3y1η+y4y1ζ,z=z1+z2z1ξ+z3z1η+z4z1ζ,E37

then the transformation from xyzsystem to ξηζsystem can be solved for ξ,ηand ζfrom Eq. (37) by the Cramer ruler, i.e.,

ξ=J1J,η=J2J,ζ=J3J,E38

where

J1=xx1x3x1x4x1yy1y3y1y4y1zz1z3z1z4z1,J2=x2x1xx1x4x1y2y1yy1y4y1z2z1zz1z4z1,E39
J3=x2x1x3x1xx1y2y1y3y1yy1z2z1z3z1zz1,J=x2x1x3x1x4x1y2y1y3y1y4y1z2z1z3z1z4z1.E40

Note that Jis the determinant of the Jacobian matrix of the transformation being equal to six times the volume of the tetrahedron element Tm.

The coordinate transformation from the second reference coordinate ξ˜η˜ζ˜to ξηζsystem is defined by

ξ=ξ1+ξ2ξ1ξ˜+ξ3ξ1η˜+ξ4ξ1ζ˜,η=η1+η2η1ξ˜+η3η1η˜+η4η1ζ˜,ζ=ζ1+ζ2ζ1ξ˜+ζ3ζ1η˜+ζ4ζ1ζ˜,E41

then the transform from ξηζsystem to ξ˜η˜ζ˜system can be solved for ξ˜η˜ζ˜from Eq. (41) by Cramer ruler similarly. Denote

J˜=ξ2ξ1ξ3ξ1ξ4ξ1η2η1η3η1η4η1ζ2ζ1ζ3ζ1ζ4ζ1,E42

which is the determinant of the Jacobian matrix of the transformation being equal to six times the volume of the subelement T˜mkfor k=1,,Ne. In Eqs. (41) and (42), ξiηiζifor i=1,2,3,4, denote the vertex coordinates of T˜mkin ξηζsystem.

3. Numerical computations

In this section we give three numerical examples to illustrate the performance of the developed method above. The convergence test of the proposed method can be found in [27]. Though the method is developed for the 3D case, it can be simplified to 2D without essential difficulty. The principle is the same. The first example is a test for a 2D model with uneven topography. The other two examples are for two 3D models.

Example 1. The first example is a two-layered model with the inclined interface shown in Figure 3a. The range of the model is x1.6km1.6kmand z1.6km,1.8km. The surface of the model is uneven to imitate the real topography. The vpand vsvelocities are 3000 m/sand 2000 m/sin the upper layer and 2400 m/sand 1600 m/sin the lower layer, respectively. The densities ρare 2200 kg/m3and 1800 kg/m3in the upper and lower layer, respectively. Figure 3b is the coarser triangular meshes for this model. A coarser version of the mesh is shown here as the finest mesh in computations cannot be seen clearly. The triangular meshes can fit the curve topography very well. Note that none triangular element crosses the interface. In computations the P4polynomial reconstruction is applied. The computational domain is meshed by 113472 coarse elements. Each coarse element is subdivided into 25 subelements further. So there are 2,836,800 fine elements totally. The time step is Δt=5×105s. The source is located at xz=0,0.2kmwith time history

Figure 3.

A two-layered model with curved surface topography (a) and the triangular meshes (b).

ft=2αtt0eαtt02,t0=0.08,α=πf02,E43

where f0=20Hzis the main frequency. In order to simulate point source excitation, a spatial local distribution function defined by

Gx=exp7xx022/r02,xx022r02,0,xx022>r02,E44

is applied, where x0=x0y0z0are positions of the source center. The source is added to the ucomponent; that is to say, all source terms except g7in Eq. (1) are all zero. Figure 4 is the snapshots of uand vcomponents at propagation time 0.25s. Figure 5 is the snapshots of uand vcomponents at propagation time 0.30s. We can see the Pwave and Swave propagate toward out of the model. The reflected and transmitted waves due to the tilted physical interface are also very clear. These are the expected physical phenomena of wave propagation in elastic media.

Figure 4.

Snapshots of u component (a) and v component (b) at propagation time 0.25 s .

Figure 5.

Snapshots of u component (a) and v component (b) at propagation time 0.30 s .

Example 2. The second example is a cuboid model. The physical size of the model is xyz0,2km×0,2km×0,1km. The model and its unstructured tetrahedral meshes are shown in Figure 6. There are totally 836,612 coarse tetrahedrons to mesh the model. A coarser mesh is shown as the actual mesh in computations is too fine to see clearly. Each coarse tetrahedron is subdivided into Ne=27subelements as we adopt P3polynomial reconstruction. The parameters for λ, μ, and ρare 109Pa, 109Pa, and 1000kg/m3. The time step in computations is 104s. The source is located in the center of the model with time history given by

Figure 6.

A cubic model and its unstructured tetrahedral meshes.

ft=sin40πte100t2.E45

It is applied to the ucomponent. The 3D snapshots of u, v, and wcomponents at propagation time 0.42sare shown in Figure 7. From these figures, we can clearly see two types of waves, i.e., the compressive wave and the shear wave. The splitting PML in nonconvolutional form is adopted here [32], and the boundary reflections are absorbed obviously and effectively. The message passing interface (MPI) parallelization based on spatial domain decomposition is applied. The CPU time for extrapolation 1000 time steps is about 33,310swith 128 processors each with 2.6 GHz main frequency.

Figure 7.

The 3D snapshots of u component (a), v component (b), and w component (c) at propagation time 0.42 s in a cuboid model. The source is located in the center of the model.

Example 3. The third example is a real geological model in China. As shown in Figure 8a, it has a very complex topography. The physical scope of the model is x0,2.0km, y0,3.5km, and z0,1.1km. The corresponding 3D mesh is shown in Figure 8b. A coarser version of the mesh is given as the actual mesh in computations is too fine to see clearly in the figure. The model is meshed with 210,701 relative coarse tetrahedral elements. Each coarse tetrahedron is subdivided into Ne=64subelements as we adopt P4polynomial reconstruction, and thus there are 13,484,864 fine elements totally. The time step Δtis 104s. The source is situated at x0y0z0=750m1300m300mwith the same time history in Eq. (45). The media velocities of vpand vsare vp=3000m/sand vs=2000m/s. The MPI parallelization based on spatial domain decomposition is applied. The nonconvolutional splitting PML [32] is adopted. The 3D snapshots of u, v, and wcomponents at propagation time 0.80sare shown in Figure 9. The CPU time for extrapolation 10,000 time steps is 100,449swith 256 processors each with 2.6 GHz main frequency. From Figure 9, we can see clearly the propagation of Pwave and Swave.

Figure 8.

A real 3D model with complex topography. (a) model and (b) unstructured tetrahedral meshes.

Figure 9.

3D snapshots of u , v , and w components at propagation time 0.80 s in a real 3D model. The results are obtained by the method in this chapter with P 4 reconstruction. (a) u component, (b) v component, (c) w component.

4. Conclusions

A new efficient high-order finite volume method for the 3D elastic wave simulation on unstructured meshes has been developed. It combines the advantages of the DG method and the traditional FV method. It adapts irregular topography very well. The reconstruction stencil is generated by refining each coarse tetrahedron which can be implemented effectively for all tetrahedrons whether they are internal or boundary elements. The hierarchical orthogonal basis functions are exploited to perform the high-order polynomial reconstruction on the stencil. The resulting reconstruction matrix remains unchanged for all tetrahedrons and can be pre-computed and stored before time evolution. The method preserves a very local property like the DG method, while it has high computational efficiency like the FV method. These advantages facilitate 3D large-scale parallel computations. Numerical computations including a 3D real physical model show its good performance. The method also can be expected to solve other linear hyperbolic equations without essential difficulty.

Acknowledgments

I appreciate Dr. Y. Zhuang, Prof. Chung, and Dr. L. Zhang very much for their important help and cooperation. This work is supported by the National Natural Science Foundation of China under the grant number 11471328 and 51739007. It is also partially supported by the National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wensheng Zhang (May 17th 2019). A High-Order Finite Volume Method for 3D Elastic Modelling on Unstructured Meshes, Seismic Waves - Probing Earth System, Masaki Kanao and Genti Toyokuni, IntechOpen, DOI: 10.5772/intechopen.86400. Available from:

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