Work

Enhancing geological data interpolation by integrating convolutional neural networks (CNN) with the Ordinary Kriging (OK) method: a case study of Wuhan

Year: 2026

Type: article

Abstract: Rapid urbanization has driven a significant increase in demand for underground infrastructure, including transportation systems, utilities and sewage networks. However, the lack of a comprehensive, reliable subsurface geological model has posed challenges for engineers and urban planners. This work aims to address this issue by improving urban planning and risk assessment through access to a robust geological model. We develop predictive geological models by integrating a modified Ordinary Kriging (OK) approach to build a layered geological model that accounts for subsurface unconformity. To address the uneven spatial distribution of lithological drill data, we developed a framework using Convolutional Neural Networks (CNNs) to predict geological features in data-sparse regions. The results enhance urban planning by providing a clearer understanding of subsurface conditions and reducing risks associated with underground infrastructure projects. A dataset of 2340 lithological boreholes covering 16 different geological strata was cleaned and integrated. These models support underground infrastructure development by reducing uncertainty.

Source: Geocarto International

DOI: 10.1080/10106049.2026.2663612

Authors: Patric Patric, Luyi Shen, Fatima Soamanjary Alphonsine

Institutions: China University of Geosciences, China University of Geosciences (Beijing)

Language: English

Topic: Geological Modeling and Analysis

Subfield: Geochemistry and Petrology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,390