Working with data from the Lander earthquake that shook Southern California in 1992, a team of researchers from San Diego State University has advanced earthquake simulation capability by improving a widely-used wavefield simulation code and adapting it for improved 3D modeling and for use on high-end HPC systems. Their research delivered new insight into strike-slip earthquakes such as the Lander earthquake which was magnitude 7.3 and leveled homes, sparked fires, cracked roads and caused one death.

The code used by the team is their updated version of anelastic wave propagation (AWP-ODC) – the ODC standing for developers Kim Olsen and Steven Day at San Diego State University (SDSU) and Yifeng Cui at the San Diego Supercomputer Center. Daniel Roten, a computational research seismologist at SDSU, led the studies.

The research showed how one earthquake can deliver building-collapsing shakes to some areas but not to others, and the Landers simulation helps solve a long-standing puzzle (more below) of earthquake science. The work was supported by a Department of Energy Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award and by the Southern California Earthquake Center (SCEC). A full account of the work is posted on the DoE Office of Science web site.

Many earthquake simulations use either linear models of forces in three dimensions or nonlinear models of forces in one or two dimensions. These demand less computer time and memory than three-dimensional nonlinear models but they do not capture true relationships between the forces and their effects. For example, linear models typically predict more violent shaking than actually occurs, producing inaccurate risk and hazard assessments.

Key to the team’s success was its ability to model nonlinear phenomena in three dimensions using a code that can be scaled up to run well on large HPC systems, namely the Blue Waters machine at the University of Illinois at Urbana-Champaign and Titan at the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science user facility. The team also used two additional OLCF systems to process data and to store results and input data files.

The researchers plan to continue to develop the code for faster HPC systems. Roten says they developed the nonlinear method for CPU systems, which use standard central processing units, before they ported the implementation to the GPU version of the code, one employing graphics processing units to accelerate calculations. The code runs about four times faster on a GPU system than on CPUs, he adds. The team optimized the code to run even faster and to reduce the amount of memory it required, since GPUs have less memory available than CPUs.

“Parallel file systems and parallel I/O (input/output) are also important for these simulations, as we are dealing with a lot of input and output data,” Roten says. “Our input source file alone had a size of 52 terabytes.”

The GPU version does not presently handle all of the features needed. For now, the code takes advantage of Blue Waters’ mix of CPU and faster GPU nodes while the team develops the code to work exclusively on Titan’s GPU nodes. “Titan would have enough GPUs to further scale up the problem, which is what we plan to do,” Roten says.

The long-standing puzzle the Landers quake simulation addressed pertained to strike-slip earthquakes, in which rocks deep underground slide past each other, or slip, causing surface rock and soils to shift with them. Slips can cause dramatic surface changes, such as broken roads that are shifted so the lanes no longer line up.

But after studying the Landers earthquake and other strike-slip quakes with magnitudes higher than 7, scientists realized these observations were not as straightforward as they seemed. “Geologists and geophysicists were surprised to see that the slip at depth, inferred from satellite observations, is larger than slip observed at the surface, from shifts measured by geologists in the field,” Roten says.

Source: DoE
Link to full article: http://ascr-discovery.science.doe.gov/2018/01/the-big-one-in-3-d/