Quantifying and Analyzing the Signal-to-Noise Ratio in Down-Hole Seismic Testing
Why this work is in the frame
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Bibliographic record
Abstract
Seismic traces are obtained during the seismic cone penetration test (SCPTu). These traces are compared to determine the propagation time, which is used to calculate seismic wave velocities. We present a method to quantify the quality of the trace by calculating the signal to noise ratio (SNR). We analyzed a set of 25 SCPTu profiles to investigate how SNR degrades with increased penetration depth. We show that signal-stacking repeated seismic tests can be used to mitigate the loss of signal to noise ratio without a significant penalty to the production rate of the seismic cone penetration test at typical test depths. Our work has implications in the development of best-practice down-hole seismic testing. This may improve the confidence in the reported shear wave velocities and lead to improved shear wave velocity applications.
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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 | 0.001 | 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 it