Applications of low-rank compressed seismic data to full-waveform inversion and extended image volumes
Bibliographic record
Abstract
ABSTRACT Conventional oil and gas fields are increasingly difficult to explore and image, resulting in the call for more complex wave-equation-based inversion algorithms that require dense long-offset samplings. Consequently, there is an exponential growth in the size of data volumes and prohibitive demands on computational resources. We have developed a method to compress and process seismic data directly in a low-rank tensor format, which drastically reduces the amount of storage required to represent the data. Seismic data exhibit a low-rank structure in a particular transform domain, which can be exploited to compress the dense data in one extremely storage-efficient tensor format when the data are fully sampled or can be interpolated when the data have missing entries. In either case, once our data are represented in the compressed tensor form, we have developed an algorithm to extract source or receiver gathers directly from the compressed parameters. This extraction process can be done on the fly directly on the compressed data, and it does not require scanning through the entire data set to form shot gathers. We apply this shot-extraction technique in the context of stochastic full-waveform inversion as well as forming full subsurface image gathers through probing techniques and reveal the minor differences between using the full and compressed data, while drastically reducing the total memory costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".