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Record W4285009082 · doi:10.1111/1365-2478.13242

3D reflection seismic imaging of the Zinkgruvan mineral‐bearing structures in the south‐eastern Bergslagen mineral district (Sweden)

2022· article· en· W4285009082 on OpenAlexaff
Alba Gil, Alireza Malehmir, P. Ayarza, Stefan Buske, R. Carbonell, D. Orlowsky, Jorge Carriedo, Anja Hagerud

Bibliographic record

VenueGeophysical Prospecting · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsLundin Mining (Canada)
Fundersnot available
KeywordsGeologySeismogramMineral explorationEconomic geologySeismologyTectonicsMineral resource classificationMineralogySeismic refractionReflection (computer programming)MineralMining engineeringGeochemistryVolcanismComputer science

Abstract

fetched live from OpenAlex

Abstract Mineral exploration is facing greater challenges nowadays because of the increasing demand for raw materials and the lesser chance of finding large deposits at shallow depths. To be efficient and address new exploration challenges, high‐resolution and sensitive methods that are cost‐effective and environmentally friendly are required. In this work, we present the results of a sparse 3D seismic survey that was conducted in the Zinkgruvan mining area, in the Bergslagen mineral district of central Sweden. The survey covers an area of 10.5 km 2 for deep targeting of massive sulphides in a polyphasic tectonic setting. A total of 1311 receivers and 950 shot points in a fixed 3D geometry setup were employed for the survey. Nine 2D profiles and a smaller 3D mesh were used. Shots were generated at every 10 m, and receivers were placed at every 10–20 m, along the 2D profiles, and 40–80 m in the mesh area. An analysis of the seismic fold coverage at depth was used to determine the potential resolving power of this sparse 3D setup. The data processing had to account for cultural noise from the operating mine and strong source‐generated surface waves, which were attenuated during both pre‐ and post‐stack processing steps. The processing workflow employed a combination of 2D and 3D refraction static corrections, and post‐stack FK filters along inlines and crosslines. The resulting 3D seismic volume is correlated with downhole data (density and P‐wave, acoustic impedance, reflection coefficient), synthetic seismograms, surface geology and a 3D model of mineral‐bearing horizons in order to suggest new exploration targets at depth. The overall geological architecture at Zinkgruvan is interpreted as two EW overturn folds, an antiform and a synform, affected by later NS‐trending folding. Two strong sets of shallow reflections, associated with the Zn–Pb mineralization, are located at the hinge of an EW‐trending antiform, while a strong set of reflections, associated with the main mineralization, is located at the overturned apex of the EW synform. The NS Knalla fault that crosses the study area terminates the continuation of the mineral‐bearing deposits at depth towards the west, a conclusion solely based on the reflectivity character of the seismic volume. This study illustrates that sparse 3D data acquisition, while it has its own challenges, can be a suitable replacement for 2D profiles while line cutting, and environmental footprints can totally be avoided.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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