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Record W4412884518 · doi:10.1111/1365-2478.70061

Multiscale Borehole Seismic Imaging for Mineral Exploration in the Blötberget Mining Area (Central Sweden, Ludvika)

2025· article· en· W4412884518 on OpenAlexaff
Lena Bräunig, Stefan Buske, Richard S. Kramer, Alireza Malehmir, Christopher Juhlin, Paul Marsden

Bibliographic record

VenueGeophysical Prospecting · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsIron Ore Company (Canada)
FundersHorizon 2020 Framework ProgrammeTechnische Universität Bergakademie FreibergUppsala UniversitetEuropean Commission
KeywordsGeologyBoreholeEconomic geologyRegional geologyGeophysical imagingIgneous petrologySeismologyMineral explorationTelmatologyGeobiologyMetamorphic petrologyMineral resource classificationEnvironmental geologyEngineering geologyGeophysicsMining engineeringGeochemistryTectonicsVolcanismPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT Borehole seismic investigations play a major role for high‐resolution imaging of geological structures at depth. The resulting borehole seismic data enable a direct characterisation of the target units as well as their physical properties along the well and in its direct vicinity. Analysing seismic data acquired at different scales within the borehole provides additional notable insights and allows an improved geological and petrophysical interpretation. In our work, we processed zero offset vertical seismic profiling data and full waveform sonic log data as part of a multiscale borehole seismic imaging workflow to better characterise a mineral exploration target at Ludvika Mines (Blötberget mining area, Central Sweden). Data processing mainly comprised wavefield separation and corridor stacking, followed by migration of the full waveform sonic log data using a diffraction stack approach. Additional borehole data, that is, impedance logs and a lithological borehole profile, were used for the integrated interpretation to provide the basis for an assignment of the reflectors to a specific lithological unit. Besides the existing structural models derived from surface seismic investigations, the new images from borehole seismic data reveal the internal structure of the mineralisation at a significantly higher resolution, complement the geophysical characterisation and can be used for a subsequent reliable mineral resource estimate.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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