Imagen de Portada

Investigating the effect of CPT in lateral spreading prediction using Explainable AI

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Hsiao, Cheng-Hsi, Rathje, Ellen, Kumar, Krishna
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Geophysics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Explainable AI models for predicting liquefaction-induced lateral spreading
por: Hsiao, Cheng-Hsi, et al.
Publicado: (2024)

Domain-informed explainable boosting machines for trustworthy lateral spread predictions
por: Hsiao, Cheng-Hsi, et al.
Publicado: (2026)

Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
por: Sordo, Brent, et al.
Publicado: (2025)

A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures
por: Sordo, Brent, et al.
Publicado: (2024)

Modeling liquefaction-induced runout of a tailings dam using a hybrid finite element and material point method approach
por: Sordo, Brent, et al.
Publicado: (2024)

Sequential hybrid finite element and material point method to simulate slope failures
por: Sordo, Brent, et al.
Publicado: (2024)

A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering
por: Kumar, Krishna
Publicado: (2025)

Explainable AI for microseismic event detection
por: Abdullin, Ayrat, et al.
Publicado: (2025)

From images to properties: a NeRF-driven framework for granular material parameter inversion
por: Hsiao, Cheng-Hsi, et al.
Publicado: (2025)

An effective physics-informed neural operator framework for predicting wavefields
por: Ma, Xiao, et al.
Publicado: (2025)

Inverse analysis of granular flows using differentiable graph neural network simulator
por: Choi, Yongjin, et al.
Publicado: (2024)

Gas trap prediction from 3D seismic and well test data using machine learning
por: Ivlev, Dmitry
Publicado: (2024)

Explainable artificial intelligence (XAI) for scaling: An application for deducing hydrologic connectivity at watershed scale
por: Ye, Sheng, et al.
Publicado: (2025)

Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
por: Cheng, Shijun, et al.
Publicado: (2025)

In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
por: O'Malley, Daniel, et al.
Publicado: (2026)

SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
por: Erdinc, Huseyin Tuna, et al.
Publicado: (2026)

Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
por: Cheng, Shijun, et al.
Publicado: (2026)

Global River Forecasting with a Topology-Informed AI Foundation Model
por: Ren, Hancheng, et al.
Publicado: (2026)

GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
por: Fan, Ming, et al.
Publicado: (2024)

Bayesian geoacoustic inversion using mixture density network
por: Wu, Guoli, et al.
Publicado: (2020)

Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards
por: Kim, Jungho, et al.
Publicado: (2025)

Controllable seismic velocity synthesis using generative diffusion models
por: Wang, Fu, et al.
Publicado: (2024)

Physics-informed waveform inversion using pretrained wavefield neural operators
por: Huang, Xinquan, et al.
Publicado: (2025)

Machine Learning Aided Modeling of Granular Materials: A Review
por: Wang, Mengqi, et al.
Publicado: (2024)

Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
por: Strnad, Felix, et al.
Publicado: (2025)

Estimating properties of a homogeneous bounded soil using machine learning models
por: Kalimeris, Konstantinos, et al.
Publicado: (2025)

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
por: Huang, Xinquan, et al.
Publicado: (2023)

Geological and Well prior assisted full waveform inversion using conditional diffusion models
por: Wang, Fu, et al.
Publicado: (2024)

A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets
por: Song, Chao, et al.
Publicado: (2025)

Feature-based Inversion of 2.5D Controlled Source Electromagnetic Data using Generative Priors
por: Zhou, Hongyu, et al.
Publicado: (2026)

Towards Operational Streamflow Forecasting in the Limpopo River Basin using Long Short-Term Memory Networks
por: Tlhomole, James, et al.
Publicado: (2026)

Enhancing lithological interpretation from petrophysical well log of IODP expedition 390/393 using machine learning
por: Sahu, Raj, et al.
Publicado: (2025)

Diffusion priors enhanced velocity model building from time-lag images using a neural operator
por: Ma, Xiao, et al.
Publicado: (2025)

Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
por: Vashisth, Divakar, et al.
Publicado: (2022)

Towards physics-informed neural networks for landslide prediction
por: Dahal, Ashok, et al.
Publicado: (2024)

Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage
por: Nie, Wanshu, et al.
Publicado: (2025)

DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
por: Cheng, Shijun, et al.
Publicado: (2025)

Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space
por: Smith, Michael J., et al.
Publicado: (2025)

Bayesian full waveform inversion with sequential surrogate model refinement
por: Meles, Giovanni Angelo, et al.
Publicado: (2025)

Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
por: Mu, Xinru, et al.
Publicado: (2025)