Preface to the special issue of JVGR, Pattern to Process: Remotely Sensed Observations of Volcanic Deposits and Their Implications for Surface Processes
Bibliographic record
Abstract
Seismic interferometry, which recovers the impulse response of the Earth by cross-correlation of ambient noise recorded at sets of two receivers, has found several applications, including the generation of virtual shot gathers for use in seismic reflection processing. To evaluate the effectiveness of this passive recording technique in mineral exploration in a hard-rock environment, 336 receivers recorded 300 h of ambient noise over the volcanogenic massive sulphide deposit of the recently discovered Lalor mine in the Canadian Flin Flon greenstone belt. A novel time-domain beamforming algorithm was developed to search for individual source locations, demonstrating that the vast majority of noise originated from the mine and ventilation shafts of the Lalor mine. The results of the beamforming were utilized in conjunction with frequency-wavenumber filtering to remove undesirable, mostly monochromatic surface wave noise originating from nearby sources. Virtual shot gathers were generated along three receiver lines, each of which was processed as a separate 2-D reflection line. Two of the resulting unmigrated reflection profiles are compared against coincident dipmoveout-stacked data from a larger, coincident 3-D dynamite seismic survey that was also acquired over the Lalor mine in 2013. Using knowledge of the local geology derived from numerous boreholes, coherent events recovered in the passive reflection profiles are inferred to be either spurious arrivals or real reflections, some of which can be interpreted in terms of geological contacts, indicating the future potential of passive recording surveys in hard rock settings.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.066 |
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".