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Record W2955188838 · doi:10.1111/1365-2478.12836

Seismic depth imaging of iron‐oxide deposits and their host rocks in the Ludvika mining area of central Sweden

2019· article· en· W2955188838 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

VenueGeophysical Prospecting · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsIron Ore Company (Canada)
FundersEuropean Commission
KeywordsGeologyOverburdenGeophysical imagingSeismologyRegional geologyBoreholeEconomic geologyWorkflowEnvironmental geologyMineral explorationMineralogyGeophysicsMining engineeringTectonicsVolcanismPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT The development of cost‐effective and environmentally acceptable geophysical methods for the exploration of mineral resources is a challenging task. Seismic methods have the potential to delineate the mineral deposits at greater depths with sufficiently high resolution. In hardrock environments, which typically host the majority of metallic mineral deposits, seismic depth‐imaging workflows are challenged by steeply dipping structures, strong heterogeneity and the related wavefield scattering in the overburden as well as the often limited signal‐to‐noise ratio of the acquired data. In this study, we have developed a workflow for imaging a major iron‐oxide deposit at its accurate position in depth domain while simultaneously characterizing the near‐surface glacial overburden including surrounding structures like crossing faults at high resolution. Our workflow has successfully been showcased on a 2D surface seismic legacy data set from the Ludvika mining area in central Sweden acquired in 2016. We applied focusing prestack depth‐imaging techniques to obtain a clear and well‐resolved image of the mineralization down to over 1000 m depth. In order to account for the shallow low‐velocity layer within the depth‐imaging algorithm, we carefully derived a migration velocity model through an integrative approach. This comprised the incorporation of the tomographic near‐surface model, the extension of the velocities down to the main reflectors based on borehole information and conventional semblance analysis. In the final step, the evaluation and update of the velocities by investigation of common image gathers for the main target reflectors were used. Although for our data set the reflections from the mineralization show a strong coherency and continuity in the seismic section, reflective structures in a hardrock environment are typically less continuous. In order to image the internal structure of the mineralization and decipher the surrounding structures, we applied the concept of reflection image spectroscopy to the data, which allows the imaging of wavelength‐specific characteristics within the reflective body. As a result, conjugate crossing faults around the mineralization can directly be imaged in a low‐frequency band while the internal structure was obtained within the high‐frequency bands.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.956

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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