Imaging and monitoring of the shallow subsurface using spatially windowed surface-wave analysis with a single permanent seismic source
Bibliographic record
Abstract
ABSTRACT Development of shallow subsurface monitoring systems is important for monitoring the ground stability of shallow formation, and also for conventional deep seismic monitoring because with current techniques, temporal changes in shallow seismic velocities can influence monitoring results for the deep subsurface. We have developed an effective shallow seismic imaging and monitoring system with high spatiotemporal resolution and accuracy using a continuous and controlled source system, the accurately controlled routinely operated signal system (ACROSS). The method applies surface-wave analysis to characterize and monitor the shallow subsurface from the spatiotemporal variation of phase velocities. Because the number of available ACROSS units is usually limited, estimating a shallow subsurface with high spatial resolution is a challenging issue in ACROSS-based monitoring. To overcome this problem, we introduced a 2D spatial window into multichannel analysis of surface waves. We analyzed continuous ACROSS data acquired during seven different data periods from 2014 to 2016 at the Aquistore CO2 storage site in Canada. As a result, we clearly estimated spatial variations of phase velocities using only a single ACROSS unit. The numerical experiments of our method suggested that the spatial variations could be associated with shallow geologic boundaries in the study area. We identified clear seasonal variations of phase velocities in winter, possibly related to ground freezing in shallow sediments, and we showed the high temporal stability of our monitoring approach in warmer seasons. These results indicated that our approach would have the potential to identify spatiotemporal change in shallow subsurface associated with natural phenomena or fluid leakage.
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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 | 0.001 |
| 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) | 0.000 | 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".