Advances in controlled‐source seismic imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current rapid growth in the number of seismometers available to the research community, combined with increasing computer power, will allow improvement in the type and quality of seismic images of the crust and lithosphere. An example of improved imaging capability is the inversion of the full seismic waveform, rather than solely travel times, in controlled‐source surveys (seismic refraction or reflection using human‐induced ground shaking). At the 12th Deep Seismic Methods workshop in 2003, sponsored by the International Association of Seismology and Physics of the Earth's Interior (IASPEI), an analysis of computer‐generated data exemplified the potential of increased source and station density. The synthetic seismic data set was generated from a geologic model that includes large‐, medium‐, and small‐scale stochastic variation. The source and seismometer spacing mimic imminent community capabilities. The Earth model was kept secret, and the data were made available for analysis (http://crust. geol.vt.edu/hole/ccss/). Figure 1 illustrates the results of blind travel time and waveform tomography applied to the data.The images, described in more detail below, illustrate an excellent match to the true Earth model.
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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 | 0.000 | 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 it