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Record W4408484463 · doi:10.5194/egusphere-egu25-20337

Geophysical Multimethod Joint Analysis for Assessing Multi-Layer Covers on Mine Tailings at Two Different Scales

2025· preprint· en· W4408484463 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversité du Québec en Abitibi-TémiscaminguePolytechnique MontréalGDG EnvironnementÉcole de Technologie Supérieure
Fundersnot available
KeywordsTailingsJoint (building)GeologyLayer (electronics)Tailings damGeophysicsEnvironmental scienceMining engineeringEngineeringCivil engineeringMaterials science

Abstract

fetched live from OpenAlex

Mine tailings storage is a major challenge for the mining industry due to the risks associated with contaminated mine drainage. Tailings can contain sulfides that, when exposed to atmospheric oxygen and precipitation, generate acidity that can spread downstream from tailings storage facilities. To mitigate this issue, the construction of multi-layer cover systems designed to divert infiltrating water from the tailings represents a promising solution. However, such cover systems are susceptible to deteriorate over time, and their effectiveness must therefore be regularly assessed.Unlike traditional destructive methods, non-invasive geophysical techniques offer a rapid and cost-effective solution for analysing these cover systems. However, each geophysical technique has its own limitations when used individually. In particular, the ERT-IP (Electrical Resistivity Tomography and Induced Polarization) and MASW (Multi-channel Analysis of Surface Waves) methods can be used to characterize the volumetric properties of soils, such as variations in electrical resistivity and seismic velocities, but often lack the precision to delineate fine interfaces clearly. GPR (Ground Penetrating Radar) and seismic refraction, on the other hand, offer a better resolution for identifying the boundaries between layers but have difficulty in accurately describing the physical properties in volume.This project aims to demonstrate the potential of a multimethod approach that combines these techniques by jointly analyzing the results to leverage their respective advantages while overcoming individual limitations and biases. Ultimately, the goal is to develop a joint inversion methodology to further refine the imaging of multi-layer cover systems, which are generally shallow and are made from a large range of materials.This study presents the results from a field campaign conducted on a tailings storage facility where inclined multi-layer cover systems have been constructed to limit water infiltration (~1 m thick). Two longitudinal profiles were analyzed at two different scales. A high-resolution profile (32 m-long, 7% slope), with 64 collocated geophones and electrodes spaced by 50 cm intervals was used to focus on fine-scale variations in the cover layer system. Measurements were taken before, during, and after an infiltration test. A longer profile (100 m-long), with 64 collocated geophones and electrodes spaced by 1 m covered two instrumented sections (a 7% slope and a 28% slope) to provide a larger-scale view and greater depth of investigation. The ERT-IP data (collected using the Wenner protocol) and seismic data were coupled with GPR profiles conducted in continuous mode using 200 MHz and 1500 MHz antennas. All geophysical datasets were surveyed to allow comparison between techniques.The results are interpreted jointly, in order to exploit the interface detection capabilities of GPR and refraction techniques along with the volumetric characterization provided by ERT and MASW at two different scales, which could improve the applicability of geophysical methods to assess the in situ performance of multi-layer cover systems installed on tailings storage facilities across larger scales.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.367
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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