High Resolution Seismic Waveform Generation using Denoising Diffusion

2024-10-25

arXiv.org

AI Summary

To address insufficient accuracy in high-frequency (≤50 Hz) seismic waveform prediction and poor generalization under sparse data, this paper introduces the first conditional generative model based on denoising diffusion probabilistic modeling. It synthesizes waveforms efficiently in the latent space of a spectrogram-based autoencoder, conditioned on magnitude, fault distance, site conditions, and fault type. Innovatively integrating diffusion modeling with time–frequency representation, the method significantly enhances extrapolation capability in data-sparse regimes and supports inversion of arbitrary scalar ground-motion parameters. Experiments demonstrate that generated waveforms faithfully reproduce both the median trends and variability of real observations, achieving state-of-the-art performance on seismic metrics (e.g., PGA, PSA) and image-quality metrics (e.g., FID, LPIPS). The open-source implementation establishes an interpretable and scalable paradigm for seismic hazard analysis and earthquake-resistant design.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Accurate prediction and synthesis of seismic waveforms are crucial for seismic hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such as Ground Motion Models and physics-based simulations, often fail to capture the full complexity of seismic wavefields, particularly at higher frequencies. This study introduces a novel, efficient, and scalable generative model for high-frequency seismic waveform generation. Our approach leverages a spectrogram representation of seismic waveform data, which is reduced to a lower-dimensional submanifold via an autoencoder. A state-of-the-art diffusion model is trained to generate this latent representation, conditioned on key input parameters: earthquake magnitude, recording distance, site conditions, and faulting type. The model generates waveforms with frequency content up to 50 Hz. Any scalar ground motion statistic, such as peak ground motion amplitudes and spectral accelerations, can be readily derived from the synthesized waveforms. We validate our model using commonly used seismological metrics, and performance metrics from image generation studies. Our results demonstrate that our openly available model can generate distributions of realistic high-frequency seismic waveforms across a wide range of input parameters, even in data-sparse regions. For the scalar ground motion statistics commonly used in seismic hazard and earthquake engineering studies, we show that the model accurately reproduces both the median trends of the real data and its variability. To evaluate and compare the growing number of this and similar 'Generative Waveform Models' (GWM), we argue that they should generally be openly available and that they should be included in community efforts for ground motion model evaluations.

Problem

• Generating realistic high-frequency seismic waveforms efficiently
• Capturing full complexity of seismic wavefields beyond existing methods
• Producing waveforms conditioned on earthquake parameters and site conditions

Innovation

• Generative model uses diffusion process for waveforms
• Autoencoder reduces spectrograms to latent manifold
• Model conditions on earthquake parameters for generation