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Many years of cooperation help geophysicists | EurekAlert!

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image: Simulation of seismic waves from a multi-physics simulation of the 2004 Indian Ocean earthquake. View more

Credit: Dr. Thomas Ulrich

Nearly a decade ago, researchers at Ludwig-Maximilians-Universität (LMU) München and the Technical University of Munich (TUM) fostered a healthy collaboration between geophysics and computer scientists to try to solve one of humanity’s most terrifying problems. Despite advances in recent decades, researchers are still largely unable to predict when and where earthquakes might strike.

Under the right conditions, a few violent minutes of shaking can portend a greater threat to follow—certain types of earthquakes beneath the ocean floor can rapidly displace huge amounts of water, creating massive tsunamis that can, in some cases, be up to minutes Only after the earthquake itself ends causing havoc.

Very violent earthquakes do not always cause tsunamis. Relatively mild earthquakes still have the potential to create dangerous tsunami conditions. LMU geophysicists are determined to help protect vulnerable coastal residents by better understanding the underlying dynamics that lead to these events, but they realize that data from ocean, land and atmospheric sensors is insufficient to paint the full picture. As a result, in 2014 the team turned to using modeling and simulation to better understand these events. Specifically, it began using the high-performance computing (HPC) resources at the Leibniz Supercomputing Center (LRZ), one of the three centers that make up the Gauss Supercomputing Center (GCS).

“The growth of HPC devices has made this work possible in the first place,” said Professor Dr. Alice Agnes Gabriel, a professor at LMU and researcher on the project. “We need to understand the basics of how mega-fault systems work, as they will help us assess subduction zone risks. It is unclear what geological faults can actually produce earthquakes of magnitude 8 and above, and also which have the greatest risks of triggering a tsunami.”

Through years of computational work at the LRZ, the team has developed high-resolution simulations of previous violent earthquake and tsunami events. Incorporating different types of observational data, LMU researchers have now identified three key characteristics that play an important role in determining the potential for an earthquake to fuel a tsunami — stress along the fault line, rock hardness and the strength of sediment layers. The LMU team recently published its findings in Nature Geoscience.

Lessons from the past

The previous team worked on modeling past earthquake and tsunami events in order to test whether the simulations were able to recreate the conditions that actually occurred. The team put a lot of effort into modeling the 2004 Sumatra-Andaman earthquake – one of the deadliest natural disasters ever recorded, consisting of a magnitude 9 earthquake and tsunami waves more than 30 meters in height. The disaster killed nearly a quarter of a million people, and caused billions of dollars in economic losses.

Simulating such a complex, fast-moving event requires huge computational muscles. Researchers must break down the study area into a fine computational grid where they can solve equations to determine the physical behavior of water or land (or both) in each space, then move their computations forward in time very slowly so that they can observe how and when changes occur.

Despite being at the forefront of computational modeling efforts, the team used the vast majority of SuperMUC Phase 2 in 2017, at the time LRZ’s flagship supercomputer, and was only able to design a single high-resolution earthquake simulation. During this period, the groups’ collaboration with computer scientists at TUM led to the development of the “local time” method, which essentially allows researchers to focus time-consuming calculations on regions that are changing rapidly, skipping over regions where objects are not changing throughout the simulation. By incorporating this local time-setting method, the team was able to run a simulation of the Sumatra-Andaman earthquake in 14 hours instead of the eight days it previously took.

The team continued to improve its code to run more efficiently, improving input/output methods and communications between nodes. At the same time, in 2018 LRZ installed a next-generation SuperMUC-NG system, much more powerful than the previous generation. Results? The team was able to not only unify the earthquake simulation itself with the motions of tectonic plates and the physical laws of how rocks break and slide, but also realistically simulate the growth and spread of tsunamis. Gabriel noted that none of these simulations would be possible without access to HPC resources such as those at LRZ.

“We’re really using hardware-aware optimization,” she said. “Computer science achievements are essential for us to make advances in computational geophysics and seismology, which are increasingly data-rich but still model-poor. With further improvements and advances in hardware, we can implement as many of these scenarios as possible to allow sensitivity analysis to find out the most important initial conditions. To understand big earthquakes.”

After obtaining the simulation data, the researchers set out to work on understanding the characteristics that appear to play the biggest role in making this earthquake so devastating. After identifying stress, rock hardness, and sediment strength as playing the largest roles in determining earthquake strength and its tendency to cause large tsunamis, the team helped bring HPC into the guide for scientists and government officials for tracking, mitigating, and preparing for earthquake and tsunami disasters moving forward.

Urgent computing in the HPC era

Gabriel noted that the team’s computing advances are well in line with the emerging sense within the HPC community that these world-class resources should be available in a “rapid response” fashion during disasters or emergencies. Because of its longstanding collaboration with LRZ, the team was able to quickly model the 2018 Palu earthquake and tsunami near Sulawesi, Indonesia, which killed more than 2,000 people and provide insight into what happened.

“We need to understand the basics of how submerged fault systems work, as they will help us assess earthquake as well as secondary cascading hazards. Specifically, the deadly consequences of the Palu earthquake came as a complete surprise to the scientists,” Gabriel said. “We must have physics-based HPC models for rapid response computing, so that we can respond quickly after hazardous events. When we modeled the Palu earthquake, we had the first data-integrated models ready to try and explain what happened within two weeks. If scientists knew the geological structures that may cause geographic risks, we can trust some of these models “which provide information on risk assessment and operational risk mitigation.”

In addition to being able to perform many permutations of the same scenario with slightly different inputs, the team is also focused on taking advantage of new artificial intelligence and machine learning methods to help comb through the vast amounts of data generated during the team’s simulations asking for help in cleaning up. The least relevant and potentially distracting data that comes from team simulations.

The team is also involved in the ChEESE Project, an EU-funded initiative that aims to prepare mature HPC tokens for exascale, or next-generation systems capable of 1 billion billion calculations per second, or more than twice the speed of most of the day. A powerful supercomputer, the Fugaku system in Japan.

Funding for SuperMUC-NG was provided by the Bavarian Ministry of Science and Arts and the German Federal Ministry of Education and Research through the Gauss Center for Supercomputing (GCS).

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natural earth sciences

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Simulation / computational modeling

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