Receiver comparison for a non-repeatable earthquake source on a low-frequency seismic reflection experiment
Bibliographic record
Abstract
CREWES, in conjunction with Husky Energy, Geokinetics, INOVA and Nanometrics, conducted a low-frequency 2D seismic experiment near Hussar, Alberta, Canada, in September of 2011. The purpose of the experiment was to study acquisition of low-frequency data in order to improve inversion results. Sources included three different Vibroseis units, and dynamite. Receivers on the ground were ION-sensor SM-7 10 Hz 3C geophones at 10 m station spacing, VectorSeis 3-C accelerometers at 10 m spacing, Sunfull 4.5 Hz 1C geophones at 20 m spacing, a partial line of SM-24 10 Hz high-sensitivity geophones at 20 m spacing, and Nanometrics compact broadband seismometers at 200 m spacing. Total receiver line length was 4.5 kilometers. On the last day of acquisition, a magnitude 6.3 earthquake occurred offshore Vancouver Island, British Columbia, Canada, approximately 1050 kilometers from the test line. The predominant frequency of earthquake arrivals was about 0.4 Hz, which is well out of the frequency range of 4.5 and 10 Hz geophones. However, the earthquake was recorded by all sensors that were part of the low-frequency experiment, and after correcting the data for geophone response, it is clear that data less than 1 Hz can be recorded on these geophones, for a sufficiently energetic source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".