Surface‐wave analysis for static corrections in mineral exploration: A case study from central Sweden
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it