In this paper we present high-order formulations of the finite volume and discontinuous Galerkin finite-element methods for wave propagation problems with a space–time adaptation technique using unstructured meshes in order to reduce computational cost without reducing accuracy. Both methods can be derived in a similar mathematical framework and are identical in their first-order version. In their extension to higher order accuracy in space and time, both methods use spatial polynomials of higher degree inside each element, a high-order solution of the generalized Riemann problem and a high-order time integration method based on the Taylor series expansion. The static adaptation strategy uses locally refined high-resolution meshes in areas with low wave speeds to improve the approximation quality. Furthermore, the time step length is chosen locally adaptive such that the solution is evolved explicitly in time by an optimal time step determined by a local stability criterion. After validating the numerical approach, both schemes are applied to geophysical wave propagation problems such as tsunami waves and seismic waves comparing the new approach with the classical global time-stepping technique. The problem of mesh partitioning for large-scale applications on multi-processor architectures is discussed and a new mesh partition approach is proposed and tested to further reduce computational cost.
Within the last decade many disciplines in natural and engineering sciences have extensively made use of scientific computations in addition to purely theoretical, classical experimental or observational research. Today, supercomputers are invaluable virtual laboratories, where experiments can be carried out to test theoretical mathematical formulations of physical problems, to investigate large parameter spaces, and to design and optimize models under investigation. However, the ever increasing computational resources are not enough to achieve sufficient resolution in the current Earth science problems if a simple uniform increase of the discrete model resolution is employed. This is particularly true in cases where large computational domains contain local small-scale features that strongly affect the behaviour of the global solution. Therefore, different adaptive strategies have been developed to maximize the resolution to cost ratio when numerically solving partial differential equations. Adaptive mesh refinement (AMR) has been developed in different scientific communities and is used in a static or dynamic way to locally achieve high spatial resolution (e.g. Babuska et al. 1995; Plewa et al. 2000; Käser & Iske 2005; Shi et al. 2006). However, for explicit numerical time-integration schemes, a fine spatial resolution of the mesh limits the time-step length due to the stability condition. Therefore, local time-stepping approaches have been developed, where the size of a time step can vary locally such that small time steps only need to be used in zones of the computational domain where the spatial resolution is high or waves are fast. Such schemes in combination with first-order discontinuous Galerkin (DG) finite-element schemes on unstructured three-dimensional meshes have been proposed by Flaherty et al. (1997) and local time-stepping for finite volume (FV) methods on unstructured tetrahedral meshes have been developed by Fumeaux et al. (2004). A high-order DG scheme using the Cauchy–Kowalewski procedure for space–time expansion and local time-stepping has been introduced for linear seismic wave equations by Dumbser et al. (2007) and later extended to nonlinear hyperbolic–parabolic systems (e.g. Lörcher et al. 2007; Gassner et al. 2008). Despite the overall reduction of computational cost using adaptive numerical methods, their application to large-scale problems on multi-processor supercomputers still requires further developments. Owing to the adaptive character of such numerical schemes, the distribution of the computational load becomes a non-trivial task. To efficiently use the computational performance of large systems, the numerical algorithms have to scale to several thousands of processors, which requires a homogeneous load balance that can become a problematic issue, especially for complex heterogeneous numerical models.
The aim of this paper is to present high-order FV and DG schemes on unstructured meshes statically adapted to particular physical problems using a local time-step approach. Furthermore, a new partitioning strategy is introduced and its effect on the computational performance is compared for large-scale applications in tsunami and earthquake wave-propagation scenarios.
2. Numerical method
The numerical scheme is constructed considering a conforming discretization of the computational domain , with elements identified using a unique index m. In two-dimensional study we use triangular elements, while in three-dimensional study we use tetrahedral elements. The numerical method is used to approximate the solution of the following hyperbolic balance law: 2.1 with U=[u1,…,up] the unknown vector of conservative variables, F(U), G(U) and H(U) the flux vectors in each spatial coordinate and S(U) a source term. The quasi-linear form of equation (2.1) reads 2.2 with the Jacobian matrices A=∂F/∂U, B=∂G/∂U and C=∂H/∂U. Equation (2.2) is said to be hyperbolic if for any direction of a normalized vector the matrix Anx+Bny+Cnz has real eigenvalues and a complete set of linear-independent eigenvectors. Considering one component of the unknown vector for simplicity, we can approximate it by 2.3 with the time-dependent scalar degrees of freedom, the space-dependent polynomial basis functions and N the number of degrees of freedom. We use orthogonal basis functions from Jacobi polynomials defined in a reference element which are mapped into each element of the discretized computational domain Ω. Approximation (2.3) introduces high-order polynomial representation in space of the unknown vector. The number of degrees of freedom and therefore the number of basis functions is defined by the order of the numerical approximation . For two-dimensional problems we use and for three-dimensional problems we use basis function hierarchically ordered. We write equation (2.1) in divergence form as 2.4 with ∇=(∂x,∂y,∂z) and . Multiplying equation (2.4) by the basis function , integrating in the control volume and using the divergence theorem we get 2.5
Introducing the polynomial approximation (2.3) and integrating in the time interval [tn,tn+1] we obtain 2.6 with the Jacobian matrix of the mapping between the reference element and the physical element , and mk the diagonal mass matrix computed in the reference element. We refer to the work of Käser & Dumbser (2006) and Dumbser & Käser (2006) for details on the numerical method when applied to the linear seismic wave equation and its efficient implementation and to the work of Castro (2007) for an application to nonlinear hyperbolic equations.
Equation (2.6) is a one-step scheme for the numerical solution from time t=tn to t=tn+1 providing the approximation to the flux, volume and source space–time integrals. Note that we update each single degrees of freedom of the polynomial approximation (2.3). This is a standard DG finite-element discretization. If we consider the FV approach, we use only first-order approximation in the polynomial representation (2.3), the term ∇ϕk cancels out and the scheme (2.6) updates the cell average of the numerical solution. The choice of the scheme, i.e. DG or FV, is problem-dependent. In general, DG numerical methods produce smaller errors and are compact, meaning that each element needs only its direct neighbours. However, they can become expensive in computational time when applied to nonlinear equations. On the other hand, FV schemes need a high-order non-oscillatory reconstruction step that involves several large stencils containing more elements than just the direct neighbours, and therefore the schemes are less compact. Equation (2.6) is a general representation of the numerical scheme and applies to one-, two-, and three-dimensional problems integrating over the corresponding element .
The flux integral along the boundaries of the element is computed by solving the generalized Riemann problem using the arbitrary high-order derivative (ADER) approach, originally presented by Toro et al. (2001) and Titarev & Toro (2002). We refer to the work of Castro & Toro (2008) for a detailed description of the solution and comparison of three different strategies. A fourth possible solution was presented by Dumbser et al. (2008).
The generalized Riemann problem introduces time accuracy of the same order as the spatial accuracy and is expressed as the following initial value problem: 2.7 where ξ is a local coordinate aligned with the normal vector to the boundary face , with the origin ξ=0 right at the interface. The index j refers to the boundaries of the control volume with . The Riemann problem (2.7) is generalized in two ways, the initial conditions are high-order polynomials in space and the equations include source terms. The solution is a time-dependent solution that allows us to evaluate the numerical flux and therefore the calculation of the flux integral in equation (2.6).
The initial conditions UL(ξ) and UR(ξ) are obtained from the polynomial representation of the numerical solution at time t=tn for DG schemes. In the case of FV schemes, we use a weighted essentially non-oscillatory (WENO) reconstruction procedure of order , for example the one presented by Dumbser & Käser (2007), to recover WENO polynomial representation of the unknown vector U in each element at each time step. In both cases the data have to be rotated in order to align them to the normal direction .
The volume and source integrals in equation (2.6) are approximated using a space–time high-order evaluation of the integrals, where we use the Cauchy–Kowalewski method to express high-order spatial derivatives into high-order time derivatives and then use a Taylor time-series expansion in the time interval [tn,tn+1]. In practice, we do an L2 projection of each time derivatives inside element to obtain a space–time evolution equation expressed as follows: 2.8 where φr(τ) are the coefficients of the Taylor series with and τ∈[0,Δt]. The time step Δt=tn+1−tn is defined by the stability condition 2.9 where Ccfl is the Courant–Friedrichs–Lewy number proposed by Courant et al. (1928), the order of the numerical approximation used in equation (2.3), h a measure of the size of the element and v the fastest wave velocity in the particular element. As mentioned above, FV methods use in approximation (2.3) and therefore equation (2.10) reduces to the classical FV stability condition. This condition is applied to each element in the computational domain and then the smallest time step is used to update the numerical solution for all elements. We refer to this approach as the global time step (GTS) method, which is still the most common approach for solving balance laws like equation (2.1).
If we consider the same Ccfl number for the full computational domain, equation (2.10) shows that the maximal time step Δt in each element depends on the element size h, the order of the approximation used in equation (2.3) and the maximum wave velocity v. Some of these parameters can be adjusted to recover a homogeneous time step throughout the computational domain, while others depend on the particular problem that we are trying to solve. In both cases an adaptive local time step (LTS) is a powerful tool to improve computational efficiency on medium- and large-scale numerical simulations, in particular for geophysical problems. In the next section we present an LTS scheme that can be applied to ADER-DG and ADER-FV numerical methods.
3. Space–time adaptive approach
When solving realistic large-scale geophysical problems we normally encounter complex geometries that define the computational domain. On the other hand, the maximum and minimum wave speeds involved in the numerical simulation can present highly heterogeneous values and therefore forcing us to adapt the mesh spacing to provide enough spatial resolution. Take for example a seismic wave propagation problem, where the velocity model consists of a large-scale homogeneous material but with a confined small basin of very low velocity. Because of the small-scale geometry of the basin and the slow velocity of the waves inside, we are forced to prescribe a fine mesh in order to resolve the wave structure inside this basin. Another example is the wave propagation of a tsunami wave where long period waves propagating in the open ocean approach and interact with a complex small-scale coast line. In these two examples, an LTS scheme would improve the computational effort to simulate the problem.
In this section we generalize the approach presented by Dumbser et al. (2007) for the ADER-DG LTS in order to be used also in the context of ADER-FV schemes. The main difficulty to directly apply the algorithm of ADER-DG LTS to high-order FV methods is the reconstruction process. In this process we make use of cell averages of neighbour elements to construct a polynomial representation of the unknown vector inside each element at each time step, typically performing a WENO type of reconstruction. The number of cell averages needed is defined by the number of elements in the reconstruction stencil. The cell average values in the corresponding stencil need to be at the same time level as the element that is reconstructed. This is typically not possible when each element runs its own time step. In the work of Fumeaux et al. (2004) in the context of FV, they enforce some level of synchronization by allowing the elements to advance by a multiple of 2 of a fundamental time step. A more general approach was presented by Tessmer (2000) for the finite difference scheme, allowing for any positive multiple of a fundamental time step. In the work of Lörcher et al. (2007) and Gassner et al. (2008) they extend the ADER-DG LTS to nonlinear Euler and Navier–Stokes equations considering DG methods.
The LTS approach presented here introduces two modifications to the one presented by Dumbser et al. (2007). First, we use LTS applied to clusters of elements instead to a single element; and second, we propose a reconstruction step based on evolved cell averages when the neighbour elements in the reconstruction stencil belong to different clusters and therefore to different time levels. Thereby, the reconstruction stencils have to be fully contained in direct neighbour clusters. In figure 1 we show the computational domain subdivided into three clusters. Each cluster defines the maximum time step by taking the smallest step from the stability criterion (2.10) applied to the elements inside the cluster.
The main ingredient of the LTS approach is the update criterion which when applied to a cluster of elements reads: a cluster of elements, identified with superscript c, is updated from time level t(c) to the time level t(c)+Δt(c) if and only if the update criterion 3.1 is satisfied with respect to all direct cluster neighbours cj. Once the criterion has been fulfilled for the cluster c, the elements in the cluster with neighbours inside the same cluster can compute the flux in the full interval Δt(c), while the ones along the boundary of the cluster compute the flux integrals in the following time interval: 3.2 Note that [t1;t2] depends on the corresponding cluster neighbour cj. Equations (3.1) and (3.2) are identical to the one presented by Dumbser et al. (2007), but now applied to clusters of elements.
The novelty of this approach is the reconstruction step which we explain next, via a one-dimensional example, for simplicity.
In figure 2 we show two columns. The left one shows the reconstruction step while the right one shows the update step. At the beginning of the simulation, figure 2a, we cluster the elements with similar time step: , and . Because all the elements are at the same time level t=0 we can reconstruct the initial data. After each cluster defines its maximum time step, see dashed line in the update column, we use the update criterion (3.1). In figure 2a, cluster C(2) fulfills the criterion and is updated to the time level t=Δt(2). In updating C(2) we compute fluxes in the time interval with respect to C(1) and with respect to C(3). These fluxes are stored in the corresponding neighbour elements on the neighbouring clusters. In this case the flux associated to the interface is stored by element in C(1) and the flux associated to the interface is stored by element in C(3). At the next time cycle represented in figure 2b, we need to reconstruct the elements in C(2) at the new time level. The problem is that the neighbouring clusters remain at time t=0. Here, we use an evolved cell average value when the elements in the reconstruction stencil belong to a different time level. In this reconstruction step, elements of C(2) will use evolved cell averages from C(1) and C(3) when needed as represented with in figure 2b. After C(2) is reconstructed and the new maximum time step is computed, the update criterion is used. In figure 2bC(1) is updated to the time level t=Δt(1). All elements on the boundary between C(1) and C(2) will compute flux only in the time interval as defined by equation (3.2). The flux in the segment was already computed and stored in the previous cycle. In figure 2c we reconstruct C(1) using information from C(2) and compute the maximum time step. The update criterion tells us to update C(2) to the time level . Boundary elements between C(1) and C(2) compute the flux in the interval and then is stored by element in C(1). Elements in the boundary between C(2) and C(3) compute the flux for the full update time interval which is stored by element in C(3). In figure 2d we reconstruct C(2) using evolved cell averages from C(1) and C(3) and then C(3) is updated. Elements at the interface between C(2) and C(3) need to compute the flux integral only in the time segment . In figure 2e we reconstruct C(3) using evolved cell averages from C(2) and then update C(2). This procedure continues until a synchronization time level is imposed or the final time simulation is reached.
Because we consider reconstruction stencils to be fully contained in direct neighbour clusters we can ensure that the evolved cell averages are within the valid time interval defined by the time step of each cluster. The cell average evolution within the time interval t=[t(c),t(c)+Δt(c)] is obtained from equation (2.9) considering only the first basis function as follows: 3.3 and then evaluated at the desired time level.
With this new method we can use local time steps associated with each cluster of elements to reduce computational time in large-scale simulations. Moreover, the optimized time step with respect to the stability criterion reduces the numerical diffusion of the scheme. The proposed scheme reproduces the one presented by Dumbser et al. (2007) for ADER-DG, and therefore without reconstruction step, if we consider each single element in the computational domain as a cluster.
With the LTS modification the update scheme reads as follows: 3.4 with and . If the element that is updated has direct neighbouring elements inside the same cluster and . On the other hand, if the element to update has a neighbouring element in a different cluster and contains the precomputed fluxes from previous neighbouring cluster updates. In the same manner [t1;t2] are computed from equation (3.2). After the element is updated, is set to zero.
The fact that we restrict this approach to consider reconstruction stencils fully contained in direct neighbouring clusters does not present practical restrictions. The LTS approach only pays off if the computational domain has a small number of short time-step elements, which then one can cluster together. Moreover, if we only consider two clusters of elements, which are not necessarily formed by neighbouring elements, this approach still applies.
In the next section we validate the proposed ADER-FV LTS scheme through a convergence test for the two-dimensional nonlinear shallow water equations to illustrate that the new scheme achieves the expected order of convergence. In Dumbser et al. (2007), the accuracy of the ADER-DG LTS was already presented for linear seismic wave equations in three dimensions.
In order to verify the expected order of accuracy we perform a convergence test where the numerical solution is compared with an exact solution on a sequence of refined meshes for the nonlinear shallow water equations with non-horizontal bathymetry; see the book of Toro (2001) for further details of the equations. Measuring the error resulting from different meshes we can compute the empirical order of convergence of the ADER-FV LTS numerical scheme. Here, we consider an exact solution given by 4.1 Introducing this exact solution into the two-dimensional version of the balance law (2.1) we obtain the new source term, . The convergence test problem is then defined as follows: 4.2 The computational domain [−1,1]×[−1,1] is discretized with structured triangular meshes and two clusters are defined with different characteristic element sizes determining the local time steps (figure 3). Periodic boundary conditions are applied. The final time in the computation is set to t=1. In table 1 we observe that the expected orders are reached for second to fifth order for this test problem using the ADER-FV LTS numerical method. The fine cluster C(2) computes 32, 35 and 39 per cent more time steps than the coarse cluster C(1) for meshes 4, 8 and 16.
5. Geophysical applications
(a) Tsunami wave propagation
Here we show an application of the ADER-FV LTS method where LTS adaptation helps to reduce computational effort to solve a real-scale problem. The case considers a tsunami wave propagation problem modelled with the two-dimensional nonlinear shallow water equations. We use the well-balanced ADER-FV scheme presented by Castro et al. (submitted) with and without the LTS modification. The computational domain represents an area offshore the west coast of Chile close to San Antonio with the coordinates (x,y)=(0,0) centred at longitude 78°W and latitude 38°S (figure 4a). The bathymetry of the area (Amante & Eakins 2008) was used to extract the coast line. In figure 4a we plot the bathymetry of the area of interest. Observe the strong contrast on the bathymetry depth with variation from −5800 m in the Atacama Trench to 0 m on the coast. In figure 4b we plot the mesh used to discretize the physical domain. The mesh was constructed to accurately describe the coast line with very small elements (see right part of figure 4b). Because in the open ocean the velocity of the tsunami wave is much higher, the wave length is larger. Furthermore, there are no geometrical restrictions and we can use larger elements to discretize that part of the domain (see left part of figure 4b). This unbalance of mesh size produces very small time steps near the coast and large time steps away from the coast. Therefore, we subdivide the mesh into two clusters when using the LTS scheme identified in figure 4b with a bold black line parallel to the coast. The right cluster is denoted as C(1) and the left one as C(2). Coloured parts of the mesh identify different message-passing interface (MPI) domains.
For this numerical simulation we use third order of approximation in space and time and subdivide the computational domain into 16 MPI subdomains. We run the code on the TETHYS cluster of the Institute of Geophysics at the Ludwig-Maximilians-Universität München (Oeser et al. 2006). In figures 5 and 6 we show the numerical results of this simulation. Figure 5 shows snapshots at t=0,5,10,15,20,25 min and the location of three stations where we register the free surface displacement. The time histories of the three stations are plotted in figure 6. We compare the numerical solution using the GTS and the LTS scheme. The GTS scheme runs for 12 154 s on 16 AMD Opteron 250 2.4 GHz cores, while the LTS scheme uses 9810 s, an improvement of 20 per cent. In the LTS the smaller time step cluster C(1) uses 2333 updates, while the cluster C(2) only requires 528 wich is, on average, a difference of 4.4 number of updates. In figure 5 at time t=0 we observe the initial condition with a wave amplitude of the order of 1 m. A negative wave travels to the coast crossing the cluster boundary without generating any spurious numerical artefacts. See time t=5,…,20 min. At t=15 min this wave arrives to the coast and reflects back. On the other hand, a positive wave travels through deep ocean heading northwest. This wave is sampled by the register number 3.
In figure 6 we plot the signals for both the methods showing the same behaviour during the full simulation time, as expected. We observe a slightly higher amplitude in the first wave arrivals obtained by the LTS scheme, particularly visible in signal 3. We explain this as an effect of the reduced numerical diffusion due to the optimal use of the time step. Signal 1 shows a much richer wave structure due to the vicinity of the coast and the corresponding wave reflections from there. Signal 2 is very interesting as it is recorded just to the left of the cluster interface. After the first wave passage there are about 15 min without vertical water displacement. Later, after roughly 30 min of simulation time, we register again a vertical displacement due to reflections from the coast. We consider these results as a clear indication that the LTS scheme works properly.
(b) Seismic wave propagation
Here we present a large-scale application of the proposed ADER-DG LTS scheme to a ground motion simulation problem in the continental area of San Antonio on the west coast of Chile. This is done by solving the set of linear hyperbolic equations describing seismic wave propagation in three space dimensions as presented by Dumbser & Käser (2006). We want to remark that this application represents a simplified case of the real geophysical situation. However, the test case clearly demonstrates the flexibility and feasibility of the proposed methodology and could also be applied for more realistic scenarios where detailed knowledge of the earthquake source and the geological properties of the subsurface are included. Furthermore, we recall that the accuracy and correctness of the adaptive DG scheme for simple seismic wave propagation problems were already validated in previous the work of Dumbser et al. (2007), and therefore we concentrate our attention on the comparison of the efficiency of the method. To this end, we first show a simulation using the classical ADER-DG GTS scheme using a GTS defined by the smallest time step in the entire computational domain following equation (2.10). Then, we show the results of the same simulation using the LTS scheme with the same standard mesh partition. Finally, we improve the efficiency of that scheme by modifying the partitioning strategy to get a better load balance for multi-processor calculations.
The computational domain with the coordinate origin (x,y,z)=(0,0,0) centred at longitude 72°W, latitude 34°S and elevation 0 m.a.s.l. is shown in figure 7a, where five different geological units with distinct material properties are shown: (i) a 200 m thick sedimentary basin at the surface embedded into (ii) a wider and 500 m deep basin structure, (iii) a near-surface layer of 2000 m thickness over (iv) a large block of continental crustal material, and (v) an oceanic wedge indicating the subducting slab along a dipping curved interface. The material properties used are given in table 2.
The topography of the model is taken from a digital elevation model (Amante & Eakins 2008) and the source is placed close to the subduction interface at (xS,yS,zS)=(16 650,51 006,−30 000) m, where the hypocentre of the 1985 Santiago earthquake has been located. Synthetic seismograms are registered at 10 receivers with a regular distance of 3 km in increasing x-direction across the basins at the model surface as shown in figure 7b. The seismograms are used to check the consistency of the results obtained by the three different simulations.
The first simulation using an ADER-DG GTS scheme of order 4 and a standard Metis (Karypis & Kumar 1998) partition into 1020 subdomains of equal numbers of elements requires 43 256 s on 1020 Intel Itanium2 Montecito Dual Core 1.6 GHz cores. Leaving the simulation setup unchanged, but exchanging the GTS with an LTS scheme, the computational time significantly reduces to 29 444 s. However, the used standard mesh partition as shown in figure 7c is not adequate any more. Whereas for GTS schemes it is important that each subdomain consists of the same number of elements in order to ensure well-adjusted load balance, for LTS this strategy leads to problems. In fact, each element evolves its solution by an LTS and therefore the number of update cycles for each element to reach a certain time level is different. Furthermore, after each update cycle neighbouring information has to be exchanged via MPI communication between the different processors. Therefore, for LTS schemes it is important that the number of element updates determined by equation (3.1) in each subdomain is equal. This is a highly non-trivial task, as the number of element updates per cycle in each subdomain also changes with time. Therefore, a dynamic load-balancing technique should provide the best load balance. However, an efficient implementation of a dynamic load-balance algorithm for unstructured meshes using LTS is subject to future work and might result in significant MPI overhead. Nevertheless, our preliminary and effective solution is the partition of the mesh respecting the different geological units (zones). This way, each zone is partitioned into the desired number of subdomains as shown in figure 7d. This is similar to the case of the tsunami wave simulation in §5a where each cluster is partitioned separately. Finally, each processor obtains one subdomain from each geological zone that provides an improved load balance due to the fact that computationally expensive (small) and cheap (large) elements are distributed more evenly. Using the LTS scheme with the modified partition of figure 7d in a third simulation reduces the computational time to 26 976 s.
The resulting ground motion components u,v and w in x-, y- and z-direction, respectively, are shown as seismograms in figure 8 at four selected receivers obtained by the three different simulations. In fact, the seismograms appear as one line indicating that all the three simulations visually produce the same results. Receiver 2 located west outside the basin shows clear P- and S-wave arrivals, whereas receiver 4 inside the outer basin and receiver 6 inside the inner basin show a much richer seismogram structure due to trapped waves inside the basins. Trapped waves are a well-known basin effect, if the impedance contrast between a hard basement rock and a soft sedimentary basin rock allows only a small fraction of the wave energy inside the basin to escape from it. Therefore, subsequent internal reflections from the basin boundaries cause wave interference that can significantly amplify and prolongate the ground motion. Receiver 9 on the east side of the basin again shows the direct wave arrivals more clearly; however, followed by a surface wave coda mainly caused by the basin.
Plotting the seismograms of all stations as a cross section from west to east in figure 9a, the ground motion amplification and longer duration due to the basins are clearly visible. Furthermore, it is obvious that the S-wave arrival is causing the large amplitudes, while the direct P-waves do not show a significant coda. To emphasize the importance of considering the low-velocity basins even if extremely thin in their spatial extent, we also show a comparison with the results obtained by neglecting the basins. To this end, we leave the geometry of the problem unchanged but fill the basins (units 1 and 2) with the same material as the surrounding surface material of unit 3. Figure 9b displays the comparison with and without the basin at receiver 6. Without the basin there is only weak ground motion after about 16 s, i.e. after the direct S-wave has passed the receiver. Therefore, considering such fine, low-velocity structures is currently a key issue in realistic ground motion modelling and seismic hazard analysis, but remains a challenge for many non-adaptive numerical schemes.
We presented a space–time adaptive algorithm to be used in the context of fully discrete, one step high-order FV and DG finite-element methods on unstructured meshes. We empirically demonstrated the expected order of convergence up to fifth order. In addition to statically adapted meshes we apply the LTS approach to two real-scale geophysical applications in the FV and DG finite-element framework. In both cases the computational effort is reduced while keeping or even slightly improving the numerical accuracy. For large-scale problems where multi-processor computations are necessary, the LTS approach requires a new mesh partition strategy as the computational work may not be distributed equally. Therefore, we apply a partition technique subdividing clusters of elements or geological zones with equivalent elements separately into the desired number of MPI domains. However, further studies especially with respect to dynamic load-balancing algorithms need to be done for each method and different physical problems. Scalability is becoming an increasingly important issue when enlarging the number of processors to several thousands, which seems to be the future trend in high-performance computing.
The first two authors thank the DFG (Deutsche Forschungsgemeinschaft), as the work was supported through the Emmy Noether-Program (KA 2281/2-1). Furthermore, we thank the Leibniz-Rechenzentrum in München for use of their computational resources within the DEISA Consortium (www.deisa.eu), co-funded through EU FP6 projects RI-508830 and RI-031513, and for their support within the DEISA Extreme Computing Initiative.
One contribution of 10 to a Theme Issue ‘Mesh generation and mesh adaptation for large-scale Earth-system modelling’.
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