The invention relates to the technical field of
seismic engineering, in particular to a multi-condition seismic oscillation generation method based on a
diffusion model, which comprises the following steps: firstly, extracting multi-dimensional condition parameters such as a
response spectrum, a peak acceleration, Arias intensity, energy
arrival time, significant duration and a Husid curve from a seismic oscillation
acceleration time history, and carrying out normalization
processing; the
acceleration time history is converted into a normalized logarithmic magnitude spectrum through short-time
Fourier transform to serve as a learning target; constructing a Transform condition
encoder to capture a
coupling relationship of multi-source conditions, designing a de-noising network based on U-Net, and integrating a cross attention mechanism to realize accurate condition injection; a denoising
diffusion probability model training strategy is adopted to optimize the
noise prediction network; during reasoning, a denoising
diffusion implicit model sampling strategy is adopted, phase information is iteratively reconstructed from an amplitude spectrum through a Griffin-Lim
algorithm, and a seismic oscillation
acceleration time history is obtained through inverse transformation. According to the invention, cooperative accurate control of the
response spectrum and the energy non-stationary characteristic is realized, training is stable, reasoning is efficient, and diversity generation is supported.