Articles | Volume 16, issue 4/5
https://doi.org/10.5194/se-16-367-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-16-367-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2025
Research article |
| 15 May 2025
| 15 May 2025
Unbiased statistical length analysis of linear features: adapting survival analysis to geological applications
Gabriele Benedetti
CORRESPONDING AUTHOR
Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy
Invited contribution by Gabriele Benedetti, recipient of the EGU Energy, Resources and the Environment Outstanding Student and PhD candidate Presentation Award 2024.
Stefano Casiraghi
Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy
Daniela Bertacchi
Dipartimento di Matematica e Applicazioni, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy
Andrea Bistacchi
Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy
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Stefano Casiraghi, Gabriele Benedetti, Daniela Bertacchi, Silvia Mittempergher, Federico Agliardi, Bruno Monopoli, Fabio La Valle, Mattia Martinelli, Francesco Bigoni, Cristian Albertini, and Andrea Bistacchi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1398, https://doi.org/10.5194/egusphere-2025-1398, 2025
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Traditional methods for investigating the subsurface cannot properly investigate fractures between 1m and 100–200m. Digital outcrop models (DOMs) provide a framework for the collection of extensive datasets in outcrop analogues. Here we present a comprehensive workflow, with a solid statistical foundation, for the characterization of all the fracture network parameters that can be obtained from this type of support, including best practices for an optimal outcrop selection and data acquisition.
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Traditional methods for investigating the subsurface cannot properly investigate fractures between 1m and 100–200m. Digital outcrop models (DOMs) provide a framework for the collection of extensive datasets in outcrop analogues. Here we present a comprehensive workflow, with a solid statistical foundation, for the characterization of all the fracture network parameters that can be obtained from this type of support, including best practices for an optimal outcrop selection and data acquisition.
Andrea Bistacchi, Silvia Mittempergher, Mattia Martinelli, and Fabrizio Storti
Solid Earth, 11, 2535–2547, https://doi.org/10.5194/se-11-2535-2020, https://doi.org/10.5194/se-11-2535-2020, 2020
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We present an innovative workflow for the statistical analysis of fracture data collected along scanlines. Our methodology is based on performing non-parametric statistical tests, which allow detection of important features of the spatial distribution of fractures, and on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), which allows the boundaries of stationary domains to be defined in an objective way.
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Short summary
At any scale, the limited size of a study area introduces a bias in the interpretation of linear features, defined as right-censoring bias. We show the effects of not considering such bias and apply survival analysis techniques to obtain unbiased estimates of multiple parametrical distributions in three censored length datasets. Finally, we propose a novel approach to select the most representative model from a sensible candidate pool using the probability integral transform technique.
At any scale, the limited size of a study area introduces a bias in the interpretation of linear...
