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Record W2123829981 · doi:10.1190/geo2011-0487.1

3D seismic imaging of volcanogenic massive sulfide deposits in the Flin Flon mining camp, Canada: Part 1 — Seismic results

2012· article· en· W2123829981 on OpenAlexaffabout
Don White, Dave Secord, M. Malinowski

Bibliographic record

VenueGeophysics · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsGeological Survey of Canada
FundersFundacja na rzecz Nauki Polskiej
KeywordsGeologyHorizonVolcanogenic massive sulfide ore depositSeismologyReflection (computer programming)GeochemistryMineralogyMining engineeringGeometryHydrothermal circulation

Abstract

fetched live from OpenAlex

ABSTRACT A 17-km2 3D-3C seismic survey was conducted within the active Flin Flon mining camp located in Manitoba, Canada. The results for the vertical component data as obtained by conventional dip-moveout and prestack time-migration processing sequences and comparison of images from the 3D seismic volume with the subsurface location of known ore zones and the mine horizon generally showed a very good correlation. A well-defined diffraction response from the shallowest ore zone was observed in the unmigrated data with a corresponding phase reversal in the migrated data at the transition from intact ore to backfilled ore zone. The geometry of unmined and backfilled ore zones compared well with strong reflection amplitudes on corresponding cross sections to depths of ∼1000 m. At greater depths, the ore zone had a weaker seismic signature due to a combination of effects, including imaging conditions, ore composition, and the increased presence of rhyolite within the mine horizon. In the case of the deeper ore zones that were characterized by low signal-to-noise levels, poststack migration was important in focusing weak ore-related reflections. The 3D data demonstrated the feasibility of detecting and accurately locating ore zones as small as a few million tons to depths of up to 1500 m.

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 score0.566
Threshold uncertainty score0.963

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 head0.011
GPT teacher head0.198
Teacher spread0.186 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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