Seismic Interferometry Using Persistent Noise Sources for Temporal Subsurface Monitoring
Bibliographic record
Abstract
Abstract In passive source seismology, seismic interferometry typically refers to the cross correlation of ambient noise to construct an estimate of the Green's function between sensors. The presence of persistent natural and/or anthropogenic sources can bias or prevent the retrieval of these estimated Green's functions. Here we show how these strong persistent sources can be used to measure small changes in the medium between a source and either (or both) source‐sensor pairs. The method relies on localizing the sources and using this information to identify and select cross‐correlation functions for each source of interest. We illustrate this method by monitoring growth of a block cave at an underground mine using three nearly continuously operating ore crushers which dominate the wavefield. This technique should work equally well in natural environments using sources such as volcanic tremor, hydrothermal bubble cavitation, and microseisms.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".