Prestack waveform inversion: An onshore application in the U S Gulf Coast
Bibliographic record
Abstract
Onshore exploration depends mostly on imaging to define structure and stratigraphy. The amplitude from stack cube or AVO response are used qualitatively to gauge fluid content. In this onshore case study, we have well control as well as proposed locations. The prestack waveform inversion was performed after our initial campaign of drilling so new well information could be incorporated in the model. The goal of the inversion is to help in evaluating whether to participate in a well or not. The inversion result gives us extra information to make such decision. Prestack waveform inversion techology has seen a rather limited application over the last 20 years in the seismic industry. A few applications of prestack waveform inversion have been reported in the past few years (Mallick 1999, Roy, et. al 2004, Lau et al, 2005). The main reason for the limited application was the lack of robustness of the prestack waveform inversion and the computational inefficiency of the numerical optimization employed. This case study demonstrates the accuracy of the methodology by virtue of elastic parameter prediction ahead of the well drilling and its computational efficiency in terms of turn around time.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".