Wavelet Packet-Based Diffusion Model for Ground Motion Generation with Multi-Conditional Energy and Spectral Matching

TL;DR

This paper introduces a multi-conditional diffusion model utilizing wavelet packet decomposition and Transformer-based encoding to generate realistic ground motion signals that match spectral and energy constraints, improving control and diversity.

Contribution

It presents a novel diffusion framework that explicitly incorporates temporal energy and spectral matching for ground motion synthesis, enhancing realism and control over generated signals.

Findings

Improved control of energy onset and duration in generated motions.

Maintains spectral matching and stable diversity sampling.

Enhances uncertainty quantification in ground motion generation.

Abstract

Temporal energy distribution strongly affects nonlinear structural response and cumulative damage. We propose a multi-conditional diffusion framework for ground motion synthesis that simultaneously matches temporal energy evolution and target response spectra. Wavelet packet decomposition provides the signal representation and enables direct waveform reconstruction via orthogonal filter banks. A Transformer-based conditional encoder with cross-attention integrates heterogeneous conditions, including spectral ordinates, Arias intensity, temporal parameters, and Husid curves. The framework adopts the Elucidating Diffusion Model (EDM) with second-order Heun sampling to improve inference efficiency without sacrificing quality. Tests on the NGA-West2 database show that explicit temporal-energy constraints markedly improve control of energy onset and significant duration while preserving spectrum matching and maintaining stable diversity sampling. The framework yields spectrum-compatible motions with realistic energy evolution and supports uncertainty quantification via conditional diversity sampling.