Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers

TL;DR

This paper introduces an explainable multi-modal transformer model for predicting earthquake-induced soil liquefaction, integrating seismic, stratigraphic, and site data, achieving high accuracy and interpretability validated on real earthquake data.

Contribution

It presents a novel multi-modal transformer architecture with interpretability for liquefaction prediction, combining seismic, soil, and site data in a unified framework.

Findings

Achieved 93.75% prediction accuracy on validation data.

Model generalizes well to unseen earthquake data.

Provides interpretable insights via SHAP analysis.

Abstract

This study presents an explainable parallel transformer architecture for soil liquefaction prediction that integrates three distinct data streams: spectral seismic encoding, soil stratigraphy tokenization, and site-specific features. The architecture processes data from 165 case histories across 11 major earthquakes, employing Fast Fourier Transform for seismic waveform encoding and principles from large language models for soil layer tokenization. Interpretability is achieved through SHapley Additive exPlanations (SHAP), which decompose predictions into individual contributions from seismic characteristics, soil properties, and site conditions. The model achieves 93.75% prediction accuracy on cross-regional validation sets and demonstrates robust performance through sensitivity analysis of ground motion intensity and soil resistance parameters. Notably, validation against previously unseen ground motion data from the 2024 Noto Peninsula earthquake confirms the model's generalization capabilities and practical utility. Implementation as a publicly accessible web application enables rapid assessment of multiple sites simultaneously. This approach establishes a new framework in geotechnical deep learning where sophisticated multi-modal analysis meets practical engineering requirements through quantitative interpretation and accessible deployment.