Geoinformatics: Community Computational Platforms for Developing ThreeDimensional Models of Earth Structure (EAR-1226343)
Progress Report for Project Year 2: August 2013 to August 2014;
PI: Thomas Jordan.
The following figures are referenced in our Geoinformatics Project Year 2 Progress
Report.
Strike-Slip Model
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5 −
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0 −
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5 −
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0 −
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planar free surface ( x2 = 0 )
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km
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km
rough
fault surface
x3
x1
x2
nucleation center at (x1, x2) = (0 km, 12 km)
Figure 1: We integrated more realistic physics into our ground motion simulations
by introducing small-scale heterogeneities into both dynamic rupture and ground
motion simulations. This image shows the rough-fault geometry of a M7.2
earthquake scenario used in a dynamic rupture simulation that produced highfrequency ground motions. Both colors and topography depict the deviation of the
fault from a planar surface.
Figure 2: As we increase the maximum simulated frequencies in our ground motion
simulations, our simulation methods must model frequency dependent attenuation.
Comparison of peak spectral accelerations at a period of 0.3 sec for simulations with
Q(f), constant Q, and leading GMPEs as a function of distance from the fault for a M7.2
scenario. Note how the Q(f) results mimic the GMPE attenuation, while the constant Q
curve attenuates much too fast.
Figure 3: Our geoinformatics research results from this year show that ground
motion simulations above 1Hz must model plastic yielding especially near the fault.
This image shows results from our improved simulation method that includes
plastic yielding at higher frequencies above 1Hz. Our simulation results showed a
reduction in horizontal peak ground velocities (%) obtained with respect to the
visco-elastic ShakeOut solution. Note reductions up to 70% of the linear PGVs in the
waveguide due to including plastic effects.
Stress as Input to Compute Velocity
Velocity
Communication
Velocity before computation
Valid
Data
Velocity after computation
Invalid
Data
Velocity
as Input to
Compute
Stress
Velocity after communication
Inner region
(1: nx, 1: ny, 1: NZ)
Inner region + 2 ghost cells
(-1: nx + 2, -1: ny + 2, 1: NZ)
Stress after computation
Inner region + 4 ghost cells
(-3: nx + 4, -3: ny + 4, -3: NZ)
Figure 4: We achieved significant ground motion simulation software performance
improvements by developing GPU-based versions of our scientific codes. This images shows
communication strategy for seismic applications on heterogeneous systems that extend
ghost cell region with 2 extra layers and utilize computation instead of communication to
update the ghost cell region before stress computation. The 2D XY plane represents 3D subdomain, no communication is required in Z direction due to 2D decomposition for GPUs.
This approach revolutionizes the reduction of communication needs for general seismic
applications.
Figure 5: This shows our recently development map of the Central Valley
structure, comprised of the Sacramento and San Joaquin basins, with basin outline
shown in blue. We have developed new basin velocity models that complements
existing velocity models for the Bay Area (Brocher et al., 2005). Green dots
represent petroleum wells with sonic logs that are available for this study; black
dots represent wells that provide formation tops and other constraints on basin
structure.
Figure 6: These figures summarizing the model components that were used to
generate the CVM-H San Joaquin basin structure. Topography, geologic
horizons, and a regional Moho surface were combined to develop a structural
framework for the model. Sediment velocities were parameterized within
these enveloping surfaces using well logs and stacking velocity data
Figure 7: This images shows an earthquake simulation based on an rupture model of
the Mw 7.9 Denali fault model (including initial thrust) mapped to San Andreas. The
rupture is to the southeast. The simulation region used was 1200 km x 600 km x 400
km deep with the newly developed CVM-H San Joaquin topography/bathymetry
velocity model embedded in simple crust + mantle model (Andreas Plesch) using a
4.7 million element mesh with 312 million grid points. The effects of shaking are
more pronounced in the southern San Joaquin basin. An earthquake source on the
White Wolf fault, for example, would make the basin effect much stronger.
Figure 8: The full 3D tomography approach we used to improve CVM-S4 into CVMS4.26 uses various waveforms for seismic tomography including body waves &
surface waves as well as earthquake recordings & ambient noise Green’s functions.
These images show how the CVM-S4.26 updated basin models (bottom images) are
highly consistent with gravity data from the USGS (top image).
Figure 9: The boundaries of new imaged basins in CVM-S4.26 show strong correlation
with active faults. These images show how CVM-S4.26 reveals many velocity contrasts
across faults. The map view (top image) shows major strike-slip faults. The cross
sections (lower images) show velocity contrasts across the 3D fault models.
.
Figure 10: These images show how our updated southern California CVM-S4.26 model
produces improved ground motion simulation results for the Los Angeles region. Black lines
are observed ground motion data. Red lines are ground motion simulation synthetic results
using CVM-S4.26, CVM-S4, CVM-H v11.9, and a Los Angeles area 1D averaged model (SC).
Figure 11: These images show our 3D starting model for central California that is
based on integration of multiple existing California 3D models including the USGS
CenCal model, Lin background model, and CVM-S4.26 model. We will use our full 3D
tomographic processing to improve this model.