Summary of Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition, By Jaeheun Jung et al.
Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
by Jaeheun Jung, Jaehyuk Lee, Chang-Hae Jung, Hanyoung Kim, Bosung Jung, Donghun Lee
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Geophysics (physics.geo-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Latent Diffusion Model (LDM) that generates realistic ground motion data for earthquakes using real earthquake data with minimal conditions: location and magnitude. The model is trained on a domain-specific dataset constructed from the Southern California Earthquake Data Center (SCEDC) API, which includes multiple observed waveforms time-aligned and paired to each earthquake source. The proposed training method exploits the traits of the earthquake dataset, including seismological metadata such as earthquake magnitude, depth of focus, and locations of epicenter and seismometers. The model outperforms comparable data-driven methods in various test criteria, including phase arrival times, GMPE analysis, and spectrum analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make fake earthquake waveforms that are very realistic. It uses real earthquake data and some special training to make sure the fake waveforms are good enough for scientists to use. The model is tested against other methods and does better in many ways. This could be important for people who study earthquakes and want to test new ideas or predict what might happen in the future. |
Keywords
» Artificial intelligence » Diffusion model
