Work

Entropy-Guided Multivariate Groundwater Network Design for Multi-Source Data Assimilation under Observational Uncertainty

Year: 2025

Type: article

Abstract: README Supporting Data and Code: 📁 Contents This repository contains data and source code associated with the parameterization of non-Gaussian permeability fields and multi-source data assimilation for subsurface inverse modeling. It includes two major folders: 1. DCGAN model/ Purpose: Dimensionality reduction and parameterization of non-Gaussian permeability fields using a Deep Convolutional Generative Adversarial Network (DCGAN). 2. Multi-source data assimilation/ Purpose: Integrating surrogate modeling, optimal monitoring network data, and parameters inversion procedure using noise multi-source observations. 🧪 Software Requirements Python 3.8+ PyTorch (for DCGAN and DCRN)

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15605284

Authors: Cao, Mingxu, Dai, Zhenxue, Chen, Junjun

Institutions: Jilin University, Qingdao University of Technology, China University of Mining and Technology

Language: English

Topic: Groundwater flow and contamination studies

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: green

OpenAlex ID: W6930771861

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