Deep learning for high-resolution multichannel seismic impedance inversion
Bibliographic record
Abstract
ABSTRACT Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill posed as characterized by unreliability and nonuniqueness of solutions. Regularization techniques relying on certain prior information often are introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods usually have difficulty achieving satisfactory accuracy and resolution. We develop a deep-learning (DL)-based multichannel impedance inversion method that flexibly incorporates prior information by training with numerous realistic structural 2D impedance models based on the features of field data. The DL framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. A novel hybrid loss function, combining ℓ1 loss and multiscale structural similarity loss, is introduced to enhance the network’s capacity for learning structural features. Synthetic and field data examples demonstrate that our method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.
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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 | 0.000 | 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 | 0.001 |
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".