University of Oulu

R. Mollehuara-Canales, E. Kozlovskaya, J.P. Lunkka, K. Moisio, D. Pedretti, Non-invasive geophysical imaging and facies analysis in mining tailings, Journal of Applied Geophysics, Volume 192, 2021, 104402, ISSN 0926-9851, https://doi.org/10.1016/j.jappgeo.2021.104402

Non-invasive geophysical imaging and facies analysis in mining tailings

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Author: Mollehuara-Canales, R.1; Kozlovskaya, E.1; Lunkka, J. P.1;
Organizations: 1Oulu Mining School, University of Oulu, Oulu 90570, Finland
2Dipartimento di Scienze della Terra “A. Desio”, Università degli Studi di Milano, 20133 Milan, Italy
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021101451073
Language: English
Published: Elsevier, 2021
Publish Date: 2021-10-14
Description:

Abstract

Stratigraphy and facies analysis in a mining waste domain such as in tailings storage facilities (TSFs) is still a complex task due to sparsely distributed field data. Geophysical techniques and the interpretation of geophysical data in terms of stratigraphy and facies get relevance for integrating geophysics with other models investigating mining waste domains (e.g., hydrogeological-geochemical).

In this paper, we introduce a conventional application of differential operators for interpreting geophysical data in terms of stratigraphy and facies analysis in TSFs. The geophysical data is acquired in a tailings area in the Pyhäsalmi mine, Finland, using seismic refraction (SR) and electric resistivity imaging (ERI) techniques. The SR inversion model constrained by a geological model approximated the ground and bedrock layers by delineating P-wave velocities (Vp). The SR layered model served as a constraint for the electrical resistivity (ρ) model in the ERI method. ERI inversion model data was used for facies analysis and interpretation in terms of other subsurface variables (e.g., water saturation, salinity). For this, a first-order derivative (gradient approach) and a second-order derivative combined with a Gaussian filter (Laplacian approach) were applied to highlight facies and transition zones. The approach embeds the data as scalar functions within a space domain defined by the model local structure. When applied to the ERI data, the gradient and the Laplacian functions captured the local extrema and the minimum threshold crossings respectively enhancing local geoelectric zones and layered contacts in line with field observations. This paper demonstrated that such image analysis can be proposed for interpretation of geophysical data in terms of segmentation and analysis of local facies, relevant in model conceptualization and parameterization of hydrogeological models.

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Series: Journal of applied geophysics
ISSN: 0926-9851
ISSN-E: 1879-1859
ISSN-L: 0926-9851
Volume: 192
Article number: 104402
DOI: 10.1016/j.jappgeo.2021.104402
OADOI: https://oadoi.org/10.1016/j.jappgeo.2021.104402
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
Subjects:
Funding: This project was supported by the European Union‘s Horizon 2020 research and innovation programme under the Marie Skoldowska-Curie grant agreement No 713606. The research work was funded by the K.H. Renlund foundation. Acknowledgments to Pyhäsalmi mine for providing access to the site and Oulu Mining School where the research is conducted.
Copyright information: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/