Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition

Earthquakes are rare but correlated.

Abstract

Shock waves caused by earthquakes can be devastating. Generating realistic earthquake-caused ground motion waveforms help reducing losses in lives and properties, yet generative models for the task tend to generate subpar waveforms. We present High-fidelity Earthquake Groundmotion Generation System (HEGGS) and demonstrate its superior performance using earthquakes from North American, East Asian, and European regions. HEGGS exploits the intrinsic characteristics of earthquake dataset and learns the waveforms using an end-to-end differentiable generator containing conditional latent diffusion model and hi-fidelity waveform construction model. We show the learning efficiency of HEGGS by training it on a single GPU machine and validate its performance using earthquake databases from North America, East Asia, and Europe, using diverse criteria from waveform generation tasks and seismology. Once trained, HEGGS can generate three dimensional E-N-Z seismic waveforms with accurate P/S phase arrivals, envelope correlation, signal-to-noise ratio, GMPE analysis, frequency content analysis, and section plot analysis.

Publication
In the International Conference on Machine Learning
Jaeheun Jung
Jaeheun Jung
Ph.D. Candidate

Inventing AI methods using mathematics

Jaehyuk Lee
Jaehyuk Lee
Ph.D. Student

I am professional ggureogi

Chang-Hae Jung
Chang-Hae Jung
M.S. in Mathematics

Researching through models of various modalities

Hanyoung Kim
Hanyoung Kim
Ph.D. Student

I’m the last M.S. in Financial Engineering graduate managed from Econ department! You can easily find my name on most Korean keyboards :)

Bosung Jung
Bosung Jung
M.S. Student

Completing coursework at all the schools within the Korea Central Education Institute.

Donghun Lee
Donghun Lee
Assistant Professor

Connecting artificial intelligence and mathematics, in both directions.