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High-Performance Computing (HPC) IS Transforming Seismology

This article explores the revolutionary impact of High-Performance Computing (HPC) on seismology, highlighted by TeraShake and CyberShake projects. The TeraShake platform enables ultra-detailed seismic wave propagation simulations, achieving 1012 FLOPS and generating vast datasets (47 TB) for effective risk analysis and hazard assessment. We discuss the implementation of physics-based models, the significance of realistic seismic hazard analysis, and the advancement of computational pathways. The study emphasizes the development and improvement of physical models, including inversion strategies, to enhance our understanding of seismic events. ###

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High-Performance Computing (HPC) IS Transforming Seismology

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

  2. TeraShake 1 (Olsen et al. 2006) 1012 flops • 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

  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.

  7. 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

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

  9. 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. • Synthetic seismograms due to an arbitrary point source s at receiver rand their gradients with respect to source locations can be retrieved from the RGT database.

  10. (l, m, n+1) (l, m-1, n) (l, m, n) (l-1, m, n) (l+1, m, n) rS h (l, m+1, n) (l, m, n-1) Confirm Reciprocity Yorba Linda Earthquake to basin station BRE Numerical differentiation to get receiver strain Green tensor Red dash line: synthetics from RGT and reciprocity Blue solid line: synthetics from forward wave propagation

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

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

  13. 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

  14. 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) 1 2 3 4 5 6 7 8 Magnitude

  15. 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

  16. Yorba Linda Cluster Fontana Trend A new left-lateral fault?

  17. Resolving Fault-plane-ambiguity for Small Earthquakes

  18. A new representation of finite moment tensor

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

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

  21. δtp= -0.4 (s)

  22. LAB Inversion Computational Cost For One GN Iteration

  23. 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

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