Prediction of Subsurface Physical Properties Through Machine Learning - The case of the Riotinto Mine

Aug 4, 2026

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Predicting subsurface physical properties and lithofacies in the Riotinto mine through supervised machine learning. The workflow uses quality-controlled petrophysical property records from surface rock samples and nine wells with lithology descriptions and density logs. Models including mathematical and supervised ML approaches, plus Multi-Layer Perceptron neural networks for outlier handling, are compared for stability and efficiency. Apparent density, total porosity and P-wave velocity estimate lithofacies at ~80% accuracy, reducing uncertainty and supporting 3D lithological characterization by integrating well logs and surface measurements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

EGU General Assembly 2024

Prediction approach and datasets

Model development, quality control, and comparison

Lithofacies prediction results and implications

EGU24-5512, updated on 02 May 2024 [https://doi.org/10.5194/egusphere-egu24-5512](https://doi.org/10.5194/egusphere-egu24-5512)[ ](https://doi.org/10.5194/egusphere-egu24-5512)EGU General Assembly 2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Prediction of Subsurface Physical Properties Through Machine Learning: The case of the Riotinto Mine. Abraham Balaguera1, Pilar Sánchez-Pastor1, Sen Du2, Montserrat Torné1, Martin Schimmel1, José Fernández2, Jordi Díaz1, Jaume Vergés1, Ramon Carbonell1, Susana Rodríguez3, and Diego Davoise3 1Geosciences Barcelona, Geo3BCN-CSIC, Barcelona, Spain. 2 Institute of Geosciences Madrid, IGEO-CSIC, Madrid, Spain. 3Atalaya Mining, Minas de Riotinto. Huelva (España) . Recently, under the umbrella of a public-private collaboration project (CPP2021-009072), Atalaya Riotinto Minera S-L. and the CSIC through its institutes IGEO-Madrid and Geo3BCN-Barcelona, have undertaken an ambitious and innovative initiative to validate the applicability of state-of-theart monitoring and prospecting systems for better tracking of deformations that may occur in the mine’s environment and to study the petrophysical properties and 3D structure of the mine subsurface. In this work, we present the results of machine learning (ML) models developed to predict various physical properties of rock (PPR) for classifying main lithologies. This analysis is based on over a thousand surface rock samples and nine wells with lithology descriptions and density logs. These data sets have allowed us to characterize the main geological units and formations comprising the subsurface of the Riotinto (RT) mine. A quality control process was applied to the PPR database through lithology and intervals to identify and correct outlier values. Multi-Layer Perceptron neural networks were employed to predict these outliers. Various mathematical and supervised machine learning models were developed to understand and predict PPR associated with different geological units. The models were compared to identify the most efficient and stable one. Additionally, new machine learning models were implemented to predict lithofacies based on PPR. These models were then used to predict PPR and classify lithofacies in wells within a mining site. The results suggest that machine learning-based PPR prediction reduces uncertainty, providing a clearer understanding of the anisotropic characteristics of geological units. Apparent density, total porosity, and P-wave velocity properties were found to predict lithofacies with an accuracy of approximately 80% . In conclusion, this advancement not only redefines the precision with which lithofacies can be identified in the Riotinto mine but also establishes a new methodology for the lithological characterization of the subsurface, leveraging both well logs and direct measurementson surface samples. This study demonstrates the potential of using new ML techniques in mining and geology, as well as opening the door to the use of these models for 3D characterization of lithological units by integrating geophysical data at the exploratory level. This work, financed with reference CPP2021 009072, has been funded by MCIN/AEI/10.13039/501100011033 (Ministry of Science, Innovation and Universities/State Innovation Agency) with funds from the European Union Next Generation/PRTR (Recovery, Transformation, and Resilience Plan) . Keywords: Machine Learning, Mining, Petrophysical Properties and Geological Characterization.

FAQs

What data sources are used to build the machine learning models in this study?

The models are trained using more than a thousand surface rock samples plus nine wells that include lithology descriptions and density logs.

How does the study handle unreliable petrophysical property values?

A quality control process uses lithology and interval information to identify and correct outlier values in the petrophysical property database.

Which predicted properties are most effective for lithofacies classification, and with what accuracy?

Apparent density, total porosity, and P-wave velocity predict lithofacies with approximately 80% accuracy.

Aug 4, 2026

Upload by Violet