1. High-Performance Computing (HPC) IS Transforming Seismology

2. Southern San Andreas Earthquake
TeraShake 1 (Olsen et al. 2006) 1012 flops
One important motivation behind this study is to provide reliable strong motion predictions for earthquake-prone areas such as southern California. This is a picture of recent “terashake” simulation. The color shows the ground velocity caused by a magnitude 7.7 earthquake that ruptures a 230-km segment of southern San Andreas fault. The accuracy of such forward simulations depends on seismologists to infer, from observations of real ground motion, the seismic source that generates seismic waves and the geological medium through which seismic waves propagate.
Southern San Andreas Earthquake
M 7.7, scaled Denali slip
SCEC CVM3 (600 km x 300 km x 80 km)
3000 x 1500 x 400 = 1.8 G nodes (200 m)
20,000 time steps (0.01 s)
19,000 SU per run
47 TB of simulation data (150,000 files) per run

3. Energy Funneling Effect (Olsen et al., 2006)

4. Data
Synthetic
Blue: data
Red: synthetic
16 Jun 2005, ML4.9, Yucaipa earthquake

5. Reference model: SCEC Community Velocity Model 3.0
For areas with complex subsurface structures, such as southern California, preliminary 3D seismic velocity models are already available. This is a fence diagram of the Southern California Earthquake Center Community Velocity Model version 3.0. The color shows the S velocity at cross-sections indicated by the red lines. Our analysis shows that in general this 3D model provides a much better prediction of ground motion than laterally homogeneous or certain path-averaged 1D structure models that are commonly used in regional seismic study. This 3D model has some drawbacks. For example, its S velocity in the basins is not derived from direct S wave tomography, but from P velocity using certain empirical scaling laws. But in spite of these drawbacks, its performance is still very encouraging, because it can serve as a reference model, a starting point, from which we can derive velocity perturbations by comparing observed waveforms with predicted ones and refine this model in a series of linearized inversions.

6. HPC makes seismic wave propagation simulations more realistic and more accurate, opens up the possibility for physics-based, deterministic, seismic hazard analysis.
Let’s watch a video made by SCEC.

8. Two Problem Areas
Develop simulation capability for physics-based seismic hazard and risk analysis
TeraShake platform
CyberShake project
2. Improve physical models for SHA
- Inversion of large data sets for Unified Structural Representation
AWM: Anelastic Wave Model
FSM: Fault-system Model
RDM: Rupture Dynamics Model
SRM: Site-response Model
SCEC computational pathways

9. Realistic 3D Earth Structure Model (CVM) +
High-Performance Computing (HPC) =
CyberShake

10. Receiver Green Tensor (RGT)
Obtain Green tensors from a receiver to all grid points by finite difference simulations (3 runs for 3 orthogonal forces at receiver).
3D Earth Structural Model
Reciprocity states that the Green tensors from all the grid points to the receiver is the transpose of the RGT obtained above.
The point-source synthetics used to make those GSDF measurements were computed using the Receiver Green Tensors and Reciprocity. The receiver green tensor is the spatial-temporal displacement generated by three orthogonal point forces acting at the location of the receiver. The source side green tensor at this receiver can be obtained by applying the reciprocity principle. Here, this G is for a source located at ri and recorded at the receiver location rR.
Synthetic seismograms due to an arbitrary point source s at receiver r and their gradients with respect to source locations can be retrieved from the RGT database.

11. Confirm Reciprocity rS (l-1, m, n) (l, m, n) (l+1, m, n) h (l, m+1, n)
Yorba Linda Earthquake to basin station BRE
Numerical differentiation to get receiver strain Green tensor
The synthetic seismogram for a double-couple point source is actually proportional to the gradient of the source-side green tensor. In order to compute the gradients both for synthetics and for computing Frechet derivatives of FMT parameters, we extract a small, source-centered volume from the RGT database and numerically differentiate the source-side green tensors. The synthetics computed using RGT and reciprocity, which are red dash lines here, have almost no difference from the synthetics computed by propagating the wavefield from source to receiver, which are blue solid lines here.
Red dash line: synthetics from RGT and reciprocity
Blue solid line: synthetics from forward wave propagation

12. Physics-based Seismic Hazard Analysis (CyberShake)
Callaghan et al. (2006)

13. Red: empirical ground motion model (Abrahamson & Silva 1997)
Black: CyberShake (Callaghan 2006)

14. Two Problem Areas
Develop simulation capability for physics-based seismic hazard and risk analysis
TeraShake platform
CyberShake project
2. Improve physical models for SHA
- Inversion of large data sets for Unified Structural Representation
AWM: Anelastic Wave Model
FSM: Fault-system Model
RDM: Rupture Dynamics Model
SRM: Site-response Model
SCEC computational pathways

15. Seismic Source Parameter Inversion
Isotropic Point Source (IPS)
Centroid Moment Tensor (CMT)
Finite Moment Tensor (FMT)
Fault
Slip Distribution (FSD)
Number of parameters
(5) (8-10) (13-20) (>100)
Three types of representations for seismic sources are commonly employed in Southern California: Isotropic point source representation, specified by an origin time, a hypocenter and a local magnitude, the number of parameters is 5; the centroid moment tensor, specified by a symmetric seismic moment tensor, a centroid time and a centroid location, the number of parameters ranges from 8 to 10 depending on whether we constrain our source to be double-couple or not; fault slip distribution, which is specified by a displacement discontinuity across a predetermined fault plane, depending on parameterization and kinematic assumptions, the number of parameters can easily exceed 100. A full FSD imaging is usually applied to large earthquakes with a large data set. We expect our FMT representation to be most useful for earthquakes in the magnitude range from 4 to 6, where finite source effects can be resolved from regional broadband data but are too small to warrant a full FSD analysis.
Magnitude

16. Rapid CMT Inversion Using Waveforms computed in a 3D Earth Structural Model
Numerical tests to verify inversion algorithm
Waveform inversion using 3D RGT synthetics .vs. first-motion focal mechanisms

17. A new left-lateral fault?
Fontana Trend
Yorba Linda Cluster
Using those 3D synthetics, we were able to implement an automated procedure to determine CMT solutions. We use the focal mechanisms determined from first-motion data as the initial solutions and then refined them through a series of gradient-based optimization processes that minimize the GSDF amplitude-reduction time measurements.

18. Resolving Fault-plane-ambiguity for Small Earthquakes
We computed Frechet derivatives for the second order spatial moment using our 3D synthetics and resolved fault-plane-ambiguity for 45 small earthquakes by testing the two nodal planes of our CMT solutions expecting the correct fault plane to give a prediction of the GSDF measurements that is better correlated with the real measurements. The preferred fault planes are indicated by red lines on these beach balls. The numbers indicate the confidence level of our solution based on a bootstrap method. One interesting phenomena is the cluster of events in the Yorba Linda area. Not only the magnitude 4.8 Yorba Linda event, but also several other smaller events in surrounding area show left-lateral source mechanisms. Recent studies show that a high proportion of north-south shortening in the Los Angeles metropolitan region is accommodated by conjugate strike-slip faulting and east-west escaping of crustal blocks This cluster of events might be associated with such “escaping” tectonics. The other interesting cluster of events is the incipient faulting on the Puente Hills-Fontana seismicity trend located to the southwest of the right-lateral San Jacinto fault and the west-striking and north-dipping Cucamonga fault. This seismicity trend is not associated with any mapped fault traces. We resolved fault-plane-ambiguity for 18 small events in this cluster and they show right lateral mechanisms conjugate to the southwest trend of the epicenter distribution and parallel to San Jacinto fault. We are still investigating the nature of this cluster of events and whether there is any connections between this cluster and the events in Yorba Linda area. The ability to resolve fault-plane-ambiguity certainly provides us important constraints.

19. A new representation of finite moment tensor

20. Fréchet Kernel for Full-wave Tomography
Born Approximation:
Born Kernels

21. Data functional:
Seismogram perturbation kernel:
Fréchet kernel:
Receiver Green Tensor

22. δtp= -0.4 (s)
This is an example of our 3D sensitivity kernel for the P wave phase-delay measurement. Red color indicates if you increase P wave velocity here, you will increase delta tp. The seismograms are from the Yorba Linda earthquake 2 years ago to basin station DLA. The reference model is SCEC CVM3.0. The distribution of this kernel suggests the measured phase-delay anomaly of about minus 0.4 sec is likely acquired from the basin underneath the station. In other words, if we invert this phase-delay time for a velocity perturbation using this kernel, we will probably get positive perturbation in the basin.

24. LAB Inversion Computational Cost For One GN Iteration

25. F3DT for Southern California (TERA3D)
Target frequency: 1.0 Hz for body-waves and 0.5 Hz for surface waves
Starting model: 3D SCEC CVM4
Grid-spacing 200m, spatial grid points 1871M
150 stations, 200 earthquakes, 650 simulations, 5.2M CPU-Hrs
Octree-based data compression, 895TB storage