MLP-Mixer based surrogate model for seismic ground motion with spatial source and geometry parameters

Hirotaka Hachiya, Yuto Kuroki, Asako Iwaki, Takahiro Maeda, Naonori Ueda, Hiroyuki Fujiwara

Keywords: surrogate model, MLP-Mixer, seismic ground motion

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TL;DR: We developed an MLP-Mixer-based surrogate model for efficient and accurate seismic ground motion prediction, leveraging spatial and channel information from large-scale spatiotemporal simulations​​.

Abstract: Seismic motion simulations enable high-precision predictions but are computationally demanding. This study introduces a deep learning surrogate model using the MLP-Mixer architecture to address this challenge. Traditional models using independent Multi-layer Perceptrons (MLPs) fail to capture spatial correlations, while U-shaped Neural Operators (U-NOs) require high computational costs for high-resolution inputs and outputs. Our proposed model, the Multiple MLP-Mixer (Multi-MLP-Mixer), integrates global and local spatial information through multiple MLP-Mixer blocks and dual patch-wise affine transformations. We demonstrate the effectiveness of our method with simulation data from anticipated megathrust earthquakes in the Nankai Trough, achieving performance comparable to state-of-the-art models with significantly improved computational efficiency.

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

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Student Author: No

Submission Number: 72