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TWCC High Performance Computing Services promotes seismic research in Taiwan

TWCC High Performance Computing Services promotes seismic research in Taiwan


Taiwan is located in the Pacific Belt and suffers from frequent earthquakes. Damage from large earthquakes is devastating, so the study of earthquakes is one of the largest academic scientific research in Taiwan. Accurate measurement of P and S waves will help seismologists calculate the distance from the epicenter. Analysis of seismic data can lead to a better understanding of warning signs of an earthquake. The combination with accurate seismic wave data will improve the accuracy and speed of early warning systems.

Interpreting seismic wave motions requires a lot of effort, especially the P and S wave records from multiple seismometers, which take time for experienced analysts or engineers to select and analyze the data for further calculation and explanation. Compared to the P waves which are easy to analyze, the S-waves standard will have some differences between different seismologists.

The Taiwan government has deployed more seismograph stations on the island to collect more huge data than before. Dealing with large amounts of data under conditions of tight time and lack of analysts, puts severe pressure on public sectors including the Taiwan Central Weather Bureau (CWB). When an earthquake occurs, massive data travels from different stations. The possibility of using automation systems to assist or even replace traditional manual operation has become an important trend. Artificial Intelligence (AI) technology is a new star to help meet needs.

The ARRU seismic phase picker project of the Department of Earth Sciences at National Cheng Kung University (NCKU) aims to use deep learning models to select the correct waves of big data. The project leader is Will Liao, a first-year PhD student. In this interview, he highly appreciates the effort and time that the Central Weather Bureau and its research colleagues have put in the community to work on preserving seismic data for earthquakes over the past decades. These efforts also allow the following researchers to continue the business as if they were standing on the shoulders of these giants.

Liao explains that the detection rate of the conventional automatic P-Wave automatic analysis method is about 75% while there is only 30% for S-Wave due to refraction or interference from the P-wave. To improve this phenomenon, it takes analysts and engineers a lot of time to correct errors by means of Re-audit and re-comment to obtain a reasonable number. The traditional automation method is problematic, but it is not easy to use and instead causes more errors due to the large variance in S-Wave. Some will encounter more faults while multiple earthquakes occur in a short period of time; So the results of the automation method lag far behind the results of human labor.

This artificial intelligence-related project will include seismic data from the Central Weather Bureau and other research organizations. There are about 30 to 35 gigabytes of data files reserved for use in AI training. These huge data sets require massive GPU computing power. Initially, Liao used a GeForce RTX 2080Ti GPU acceleration card in the NCKU lab that only took two weeks to run the AI ​​training, but things got better. By participating in the “TWCC STAR TREK” start-up company for artificial intelligence hosted by the National Center for High Performance Computing (NCHC), this project gets support from the Taiwan Computing Cloud (TWCC) platform, and now it only takes 4 or 5 days for AI training which is the source Important.

The current stage uses TWCC’s single GPU acceleration card for the first trials. In the next step, it will enable multiple GPU acceleration cards to train AI by tweaking Python codes. The goal is to train more than 100 AI models. So far, with nearly two weeks of experience, Liao has seen the preliminary results. In the next stages, it is expected to achieve 5 times more computing performance with the help of the V100 GPU and more memory capacity.

Initial results using the current effective AI models have somewhat improved the detection rate of P-wave and S-wave reaching 98% and 90% respectively compared to the previous results of 75% and 30%, the results are very impressive. Along with conventional physics models, it shows convincing results and has a better use in studying earthquake models associated with seismic structures. This research was submitted to the Q1 Classification Journals in October 2020. The future goal of this project is to bring an AI inference system to advanced computing platforms and deploy it to various locations to help the Central Weather Bureau in Taiwan increase the accuracy and speed of earthquake detection.

Will Liao, PhD student at National Cheng Kung University

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