Digitally Structured Subsurface Borehole Dataset for Geological Characterisation and Data‐Driven Modelling at CERN

ABSTRACT Tunnel performance can be influenced by various factors, among which geological conditions near the tunnel alignment play a critical role. This dataset provides subsurface geological information from two underground tunnel sections at European Organization for Nuclear Research (CERN): the Super Proton Synchrotron (SPS) and the Large Hadron Collider (LHC). The dataset includes spatial and geological attributes such as latitude, longitude, altitude, depth, ground surface elevation, borehole identifier and material/geological descriptions. In total, 73 boreholes are documented. The dataset is unique in that it converts previously unstructured PDF borehole reports into a structured, machine‐readable format, specifically capturing subsurface geology near the CERN tunnels. The structured dataset supports subsurface geological characterisation and can be used as input for future geological modelling and data‐driven analyses. It can also be combined with independent hydrogeological or infrastructure‐monitoring data where available. While the number of boreholes is limited, future integration with additional boreholes will allow development of a more comprehensive benchmark dataset. In the original digitised records, depth values are reported to a numerical resolution of 0.01 m where available. In addition to the original boundary‐based records, a separate derived dataset with an approximately 1 m vertical sampling interval is provided for convenient visualisation and subsequent computational use.

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Publication Details

Journal
Geoscience Data Journal
Published
2026-11-03
DOI
https://doi.org/10.1002/gdj3.70106
Primary Topic
Geological Modeling and Analysis
Type
article
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article

Digitally Structured Subsurface Borehole Dataset for Geological Characterisation and Data‐Driven Modelling at CERN

Andrea Visentin, Vanessa Di Murro, Aohui Ouyang, Zahra Hajighasemi et al.
Geoscience Data Journal
Geological Modeling and Analysis
article

Digitally Structured Subsurface Borehole Dataset for Geological Characterisation and Data‐Driven Modelling at CERN

Andrea Visentin, Vanessa Di Murro, Aohui Ouyang, Zahra Hajighasemi, Zili Li, Zhen Liu, John A. Osborne, Biao He
article en

Abstract

ABSTRACT Tunnel performance can be influenced by various factors, among which geological conditions near the tunnel alignment play a critical role. This dataset provides subsurface geological information from two underground tunnel sections at European Organization for Nuclear Research (CERN): the Super Proton Synchrotron (SPS) and the Large Hadron Collider (LHC). The dataset includes spatial and geological attributes such as latitude, longitude, altitude, depth, ground surface elevation, borehole identifier and material/geological descriptions. In total, 73 boreholes are documented. The dataset is unique in that it converts previously unstructured PDF borehole reports into a structured, machine‐readable format, specifically capturing subsurface geology near the CERN tunnels. The structured dataset supports subsurface geological characterisation and can be used as input for future geological modelling and data‐driven analyses. It can also be combined with independent hydrogeological or infrastructure‐monitoring data where available. While the number of boreholes is limited, future integration with additional boreholes will allow development of a more comprehensive benchmark dataset. In the original digitised records, depth values are reported to a numerical resolution of 0.01 m where available. In addition to the original boundary‐based records, a separate derived dataset with an approximately 1 m vertical sampling interval is provided for convenient visualisation and subsequent computational use.

Geoscience Data JournalVol. 14(1)
University College Cork (IE), European Organization for Nuclear Research (CH)
Openalex Percentile: Top 19%
Geological Modeling and Analysis
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