Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers
- URL: http://arxiv.org/abs/2502.10446v2
- Date: Tue, 18 Feb 2025 04:14:33 GMT
- Title: Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers
- Authors: Sompote Youwai, Tipok Kitkobsin, Sutat Leelataviwat, Pornkasem Jongpradist,
- Abstract summary: This study presents an explainable parallel transformer architecture for soil liquefaction prediction.
The architecture processes data from 165 case histories across 11 major earthquakes.
The model achieves 93.75% prediction accuracy on cross-regional validation sets.
- Score: 0.0
- License:
- 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.
Related papers
- Multiclass Post-Earthquake Building Assessment Integrating Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers [0.0]
We introduce a framework that combines high-resolution post-earthquake satellite imagery with building-specific metadata relevant to the seismic performance of the structure.
Our model achieves state-of-the-art performance in multiclass post-earthquake damage identification for buildings from the Turkey-Syria earthquake on February 6, 2023.
arXiv Detail & Related papers (2024-12-05T23:19:51Z) - Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms.
We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds.
Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
arXiv Detail & Related papers (2024-07-26T10:29:16Z) - Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation [48.66623377464203]
Our novel approach introduces the Dynamic One-For-All (DOFA) model, leveraging the concept of neural plasticity in brain science.
This dynamic hypernetwork, adjusting to different wavelengths, enables a single versatile Transformer jointly trained on data from five sensors to excel across 12 distinct Earth observation tasks.
arXiv Detail & Related papers (2024-03-22T17:11:47Z) - Controllable seismic velocity synthesis using generative diffusion models [4.2193475197905705]
We propose conditional generative diffusion models for seismic velocity synthesis.
This approach enables the generation of seismic velocities that closely match the expected target distribution.
We demonstrate the flexibility and effectiveness of our method through training diffusion models on the OpenFWI dataset.
arXiv Detail & Related papers (2024-02-09T09:41:26Z) - Attention with Markov: A Framework for Principled Analysis of
Transformers via Markov Chains [48.146073732531605]
We study the sequential modeling capabilities of transformers through the lens of Markov chains.
Inspired by the Markovianity of natural languages, we model the data as a Markovian source.
We show the existence of global minima and bad local minima contingent upon the specific data characteristics and the transformer architecture.
arXiv Detail & Related papers (2024-02-06T17:18:59Z) - IntraSeismic: a coordinate-based learning approach to seismic inversion [14.625250755761662]
IntraSeismic is a novel hybrid seismic inversion method that seamlessly combines coordinate-based learning with the physics of the post-stack modeling operator.
Key features of IntraSeismic are unparalleled performance in 2D and 3D post-stack seismic inversion, rapid convergence rates, and ability to seamlessly include hard constraints.
Synthetic and field data applications of IntraSeismic are presented to validate the effectiveness of the proposed method.
arXiv Detail & Related papers (2023-12-17T00:29:25Z) - FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model
for Fault Recognition [13.339333273943842]
This paper introduces an approach to enhance seismic fault recognition through self-supervised pretraining.
We have employed the Swin Transformer model as the core network and employed the SimMIM pretraining task to capture unique features related to discontinuities in seismic data.
Experimental results demonstrate that our proposed method attains state-of-the-art performance on the Thebe dataset, as measured by the OIS and ODS metrics.
arXiv Detail & Related papers (2023-10-27T08:38:59Z) - A deep scalable neural architecture for soil properties estimation from
spectral information [20.981200039553144]
We propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures.
'Results, compared with other state-of-the-art methods, confirm the effectiveness of the proposed solution'
arXiv Detail & Related papers (2022-10-26T16:50:06Z) - CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE)
At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales.
We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
arXiv Detail & Related papers (2022-03-03T05:58:49Z) - TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
We propose a versatile method that estimates joint distributions using an attention-based decoder.
We show that our model produces state-of-the-art predictions on several real-world datasets.
arXiv Detail & Related papers (2022-02-07T21:37:29Z) - Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences [77.68028443709338]
We propose a fully Bayesian formulation of the Epidemic Type Aftershock Sequence (ETAS) model.
The occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process.
arXiv Detail & Related papers (2020-02-05T10:11:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.