Support Data and Code for: A Pore Network Model Based on Evolving Conduit Geometry for Resistivity Prediction in Hydrate-Bearing Sediments

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Lei Wang, Lijuan Gu, Linsen Zhan, Hailong Lu
DOI: 10.5281/zenodo.18645743
Cited by: 0

Abstract

This repository contains the source code, training datasets, and pre-trained models for the paper 'Physics-Informed Deep Learning for Resistivity Prediction in Hydrate-Bearing Sediments: An Efficient Pore Network Modeling Framework' published in JGR: Solid Earth."

Fields of Study & Subjects

Geophysical and Geoelectrical Methods Methane Hydrates and Related Phenomena Hydrocarbon exploration and reservoir analysis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
15 February 2026
Language
EN
Document type
Dataset