Articles | Volume 15, issue 12
Geosci. Model Dev., 15, 4689–4708, 2022
https://doi.org/10.5194/gmd-15-4689-2022

Special issue: The Loop 3D stochastic geological modelling platform – development...

Geosci. Model Dev., 15, 4689–4708, 2022
https://doi.org/10.5194/gmd-15-4689-2022
Methods for assessment of models
20 Jun 2022
Methods for assessment of models | 20 Jun 2022

loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification

Guillaume Pirot et al.

Related authors

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022,https://doi.org/10.5194/gmd-15-3641-2022, 2022
Short summary
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022,https://doi.org/10.5194/essd-14-381-2022, 2022
Short summary
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021,https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021,https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Contaminant source localization via Bayesian global optimization
Guillaume Pirot, Tipaluck Krityakierne, David Ginsbourger, and Philippe Renard
Hydrol. Earth Syst. Sci., 23, 351–369, https://doi.org/10.5194/hess-23-351-2019,https://doi.org/10.5194/hess-23-351-2019, 2019
Short summary

Related subject area

Climate and Earth system modeling
Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352, https://doi.org/10.5194/gmd-16-335-2023,https://doi.org/10.5194/gmd-16-335-2023, 2023
Short summary
Evaluation of native Earth system model output with ESMValTool v2.6.0
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023,https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023,https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023,https://doi.org/10.5194/gmd-16-179-2023, 2023
Short summary
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023,https://doi.org/10.5194/gmd-16-135-2023, 2023
Short summary

Cited articles

Ahmed, N., Natarajan, T., and Rao, K. R.: Discrete cosine transform, IEEE T. Comput., 100, 90–93, 1974. a
Ailleres, L.: The Loop 3D stochastic geological modelling platform – development and applications, GMD Special Issue, https://gmd.copernicus.org/articles/special_issue1142.html (last access: 8 June 2022), data available at: https://loop3d.github.io/ (last access: 8 June 2022), 2020. a
Boisvert, J. B., Pyrcz, M. J., and Deutsch, C. V.: Multiple point metrics to assess categorical variable models, Nat. Resour. Res., 19, 165–175, 2010. a, b
Chen, M., Tompson, A. F., Mellors, R. J., and Abdalla, O.: An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty, Appl. Energ., 137, 352–363, 2015. a
Dagan, I., Lee, L., and Pereira, F.: Similarity-based methods for word sense disambiguation, in: Proceedings of the 35th ACL/8th EACL, arXiv preprint, 56–63, https://doi.org/10.48550/arXiv.cmp-lg/9708010, 1997. a, b, c
Download
Short summary
Results of a survey launched among practitioners in the mineral industry show that despite recognising the importance of uncertainty quantification it is not very well performed due to lack of data, time requirements, poor tracking of interpretations and relative complexity of uncertainty quantification. To alleviate the latter, we provide an open-source set of local and global indicators to measure geological uncertainty among an ensemble of geological models.