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Record W2982112915 · doi:10.1111/1365-2478.12895

Surface‐wave analysis for static corrections in mineral exploration: A case study from central Sweden

2019· article· en· W2982112915 on OpenAlexaff
Myrto Papadopoulou, F. Da Col, Binbin Mi, E. Bäckström, Paul Marsden, Bojan Brodic, Alireza Malehmir, Laura Valentina Socco

Bibliographic record

VenueGeophysical Prospecting · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsStaticsGeologyDispersion (optics)Reflection (computer programming)Context (archaeology)Regional geologySurface wavePassive seismicSeismic waveWorkflowSeismologyEnvironmental geologyEconomic geologyData processingGeophysicsMineralogyOpticsVolcanismComputer science

Abstract

fetched live from OpenAlex

ABSTRACT In mineral exploration, increased interest towards deeper mineralizations makes seismic methods attractive. One of the critical steps in seismic processing workflows is the static correction, which is applied to correct the effect of the shallow, highly heterogeneous subsurface layers, and improve the imaging of deeper targets. We showed an effective approach to estimate the statics, based on the analysis of surface waves (groundroll) contained in the seismic reflection data, and we applied it to a legacy seismic line acquired at the iron‐oxide mining site of Ludvika in Sweden. We applied surface‐wave methods that were originally developed for hydrocarbon exploration, modified as a step‐by‐step workflow to suit the different geologic context of hard‐rock sites. The workflow starts with the detection of sharp lateral variations in the subsurface, the existence of which is common at hard‐rock sites. Their location is subsequently used, to ensure that the dispersion curves extracted from the data are not affected by strong lateral variations of the subsurface properties. The dispersion curves are picked automatically, windowing the data and applying a wavefield transform. A pseudo‐2D time‐average S‐wave velocity and time‐average P‐wave velocity profile are obtained directly from the dispersion curves, after inverting only a reference curve. The time‐average P‐wave velocity profile is then used for the direct estimation of the one‐way traveltime, which provides the static corrections. The resulting P‐wave statics from the field data were compared with statics computed through conventional P‐wave tomography. Their difference was mostly negligible with more than 91% of the estimations being in agreement with the conventional statics, proving the effectiveness of the proposed workflow. The application of the statics obtained from surface waves provided a stacked section comparable with that obtained by applying tomostatics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.257
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2019
Admission routes1
Has abstractyes

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