Potential of legacy 2D seismic data for deep targeting and structural imaging at the Neves–Corvo massive sulphide‐bearing deposit, Portugal
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Seismic methods are becoming an established choice for deep mineral exploration after being extensively tested and employed for the past two decades. To investigate whether the early European mineral‐exploration datasets had potential for seismic imaging that was overlooked, we recovered a low‐fold legacy seismic dataset from the Neves–Corvo mine site in the Iberian Pyrite Belt in southern Portugal. This dataset comprises six 4–6 km long profiles acquired in 1996 for deep targeting. Using today's industry‐scale processing algorithms, the world‐class, ca. 150 Mt, Lombador massive sulphide and other smaller deposits were better imaged. Additionally, we also reveal a number of shallow but steeply dipping reflections that were absent in the original processing results. This study highlights that legacy seismic data are valuable and should be revisited regularly to take advantage of new processing algorithms and the experiences gained from processing such data in hard‐rock environments elsewhere. Remembering that an initial processing job in hard rock should always aim to first obtain an overall image of the subsurface and make reflections visible, and then subsequent goals of the workflow could be set to, for example understanding relative amplitude ratios. The imaging of the known mineralization implies that this survey could likely have been among one of the pioneer studies in the world that demonstrated the capability of directly imaging massive sulphide deposits using the seismic method.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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