Crosscoherence-based interferometry for the retrieval of first arrivals and subsequent tomographic imaging of differential weathering
Bibliographic record
Abstract
ABSTRACT Exhumation of crust exposes rocks to weathering agents that weaken the rocks’ mechanical strength. Weakened rocks will have lower seismic velocity than intact rocks and can therefore be mapped using seismic methods. However, if the rocks are heavily weathered, they will attenuate controlled-source seismic waves to such a degree that the recorded wavefield would become dominated by ambient noise and/or surface waves. Therefore, we have examined the structure of differential weathering by first-break traveltime tomography over a seismic profile extending approximately 3.5 km and acquired at a mining site in Zambia using explosive sources and a source based on the swept-impact seismic technique (SIST). Seismic interferometry has been tested for the retrieval of supervirtual first arrivals masked by uncorrelated noise. However, use of crosscorrelation in the retrieval process makes the method vulnerable to changes in the source signal (explosives and SIST). Thus, we have developed a crosscoherence-based seismic-interferometry method to tackle this shortcoming. We investigate the method’s efficiency in retrieving first arrivals and, simultaneously, correctly handling variations in the source signal. Our results illustrate the superiority of the crosscoherence- over crosscorrelation-based method for retrieval of the first arrivals, especially in alleviating spurious ringyness and in terms of the signal-to-noise ratio. These benefits are observable in the greater penetration depth and the improved resolution of the tomography sections. The tomographic images indicate isolated bodies of higher velocities, which may be interpreted as fresh rocks embedded into a heavily weathered regolith, providing a conspicuous example of differential weathering. Our study advances the potential of seismic methods for providing better images of the near surface (the critical zone).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".