Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition

• URL: http://arxiv.org/abs/2412.17333v1
• Date: Mon, 23 Dec 2024 06:56:28 GMT
• Title: Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition
• Authors: Jaeheun Jung, Jaehyuk Lee, Chang-Hae Jung, Hanyoung Kim, Bosung Jung, Donghun Lee,
• Abstract summary: We propose a specialized Latent Diffusion Model (LDM) that reliably generates realistic waveforms after learning from real earthquake data. We construct the time-aligned earthquake dataset using Southern California Earthquake Data Center (SCEDC) API. Our model surpasses all comparable data-driven methods in various test criteria not only from waveform generation domain but also from seismology.
• Score: 3.1285630695933686
• License:
• Abstract: Earthquakes are rare. Hence there is a fundamental call for reliable methods to generate realistic ground motion data for data-driven approaches in seismology. Recent GAN-based methods fall short of the call, as the methods either require special information such as geological traits or generate subpar waveforms that fail to satisfy seismological constraints such as phase arrival times. We propose a specialized Latent Diffusion Model (LDM) that reliably generates realistic waveforms after learning from real earthquake data with minimal conditions: location and magnitude. We also design a domain-specific training method that exploits the traits of earthquake dataset: multiple observed waveforms time-aligned and paired to each earthquake source that are tagged with seismological metadata comprised of earthquake magnitude, depth of focus, and the locations of epicenter and seismometers. We construct the time-aligned earthquake dataset using Southern California Earthquake Data Center (SCEDC) API, and train our model with the dataset and our proposed training method for performance evaluation. Our model surpasses all comparable data-driven methods in various test criteria not only from waveform generation domain but also from seismology such as phase arrival time, GMPE analysis, and spectrum analysis. Our result opens new future research directions for deep learning applications in seismology.

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