Distributed acoustic sensing for mineral exploration: a pilot study from central Sweden
Bibliographic record
Abstract
The iron-oxide mine of Blötberget in the Bergslagen mineral-endowed district in central Sweden has been the target of several geophysical studies in recent years. Among these studies, a series of seismic surveys aim to delineate the lateral and depth extent of the deposits for methodological and technological testing given the wealth of borehole data available for validation. The ore in Blötberget primarily comprises high-quality iron-oxides in the form of magnetite and hematite, partly enriched with apatite. The deposits occur in sheets, ranging from 10-50 m in thickness, with an approximately 45° eastward dip along an NNE-trending zone.In June 2022, an additional seismic dataset was acquired to investigate the potential of distributed acoustic sensing (DAS) measurements in imaging the iron-oxide deposits. The use of DAS data presents several challenges, one of the most important being the directional sensitivity of the DAS cable. To date, there are only limited borehole and surface DAS applications for mineral exploration and in hardrock settings, and there is hence good potential to develop new how-to solutions for these purposes.This study presents the seismic data generated by a vibrator truck and recorded by a 2200 m long straight fiber cable, deployed on the downdip of the mineralization. The cable was covered with gravel to improve its coupling to the ground. While complex, the results are promising, revealing distinct seismic signatures generated from the mineralization in specific data segments. AcknowledgmentsThis work is partly sponsored by the Smart Exploration Research Center. The center has received funding from the Swedish Foundation for Strategic Research (SSF) agreement no. CMM22-0003. This is publication SE25-001.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".