Low‐Frequency Extrapolation by Deep‐Learning for Cross‐Well Full‐Waveform Inversion – Case Study From the Aquistore CO2$\rm CO_{2}$ Storage Site
Bibliographic record
Abstract
ABSTRACT Full‐waveform inversion (FWI) of seismic data is a powerful method for estimating high‐resolution models of the subsurface. An accurate initial model and low‐frequency data are necessary to avoid cycle skipping and perform a successful FWI. In the absence of this information, FWI is likely to fail due to convergence in local misfit minima. With the recent advancements in artificial intelligence, studies have shown that absent low‐frequency data can be extrapolated using deep learning (DL). These studies have been mostly focused on surface seismic data whose frequency content is different from cross‐well data. In this study, we assess the use of DL for low‐frequency extrapolation for a cross‐well survey that was done at the Aquistore storage site in Saskatchewan. This assessment includes both numerical and field data examples. We extrapolate the low frequencies to increase the bandwidth of the acquired data at the Aquistore site and perform FWI. We evaluate the efficiency of this method by comparing the results with obtained velocity models from the conventional multiscale FWI. Our results for the Aquistore data show that the proposed strategy leads to an accuracy improvement of 39% and 20% in the model and data domains, respectively.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".