Bibliographic record
Abstract
ABSTRACT Utilising ambient seismic energy naturally propagating in the Earth as an alternative approach to active body‐wave seismic investigations has been a topic of interest for a number of decades. However, because ambient surface‐wave arrivals typically are of much greater amplitude than ambient body‐wave energy, significant data signal processing and long recording times are required to mitigate this and other coherent noise sources, and to correlate sufficient reflected body‐wave energy to converge to a stable image. Even for these scenarios, identifying and validating imaged body‐wave reflection events remain challenging. In active‐source investigations, extended imaging condition gathers are used to examine velocity (in)accuracy. Herein, we develop an ambient direct migration approach that uses a novel ambient (deconvolution) extended imaging condition. We simulate synthetic ambient‐wavefield seismic data for two different models and use a field data set from Lalor Lake in Manitoba, Canada, to conduct a series of numerical experiments to demonstrate the velocity sensitivity and long‐term stationarity of ambient‐wavefield seismic data in the migration image domain. Tests with varying global velocity perturbations show a characteristic reflector moveout in deconvolution extended imaging condition gathers that can serve as a diagnostic of reflected ambient body‐wave energy. We illustrate that this imaging formalism, under idealised circumstances, gives comparable results to conventional seismic methods, which extends the use of extended imaging condition gather‐based image validation to ambient‐wavefield seismic data scenarios. We assert that this may be a valuable tool for the validation of ambient migration techniques that to date have yielded largely inconclusive results.
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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 | 0.000 | 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 | 0.000 |
| 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 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".