Surface‐wave analysis for static corrections in mineral exploration: A case study from central Sweden
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".