Understanding seismic attributes through forward modeling
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
This paper examines the use of forward modeling to understand seismic attributes. Our intent is to present working interpreters with a methodology for gaining insights into the physical basis for relationships that may sometimes be established empirically between seismic attributes and rock/reservoir properties. Some publications have focused on the statistical or computational aspects of establishing these empirical relationships (e.g., Hampson et al., 2001) and others have presented case studies (e.g., Hart and Balch, 2000; Leiphart and Hart, 2001). However, few papers explicitly examine the physical basis for links between attributes and properties. Using simple examples, we seek to show that adding seismic modeling to an attribute study can allow an interpreter to undertake hypothesis-driven, physically based, and repeatable work, thereby adding a measure of scientific rigor that attribute studies are commonly missing.
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.
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 it