A deep-learning framework with seismic-frequency-band constraints for thin-reservoir characterization
Bibliographic record
Abstract
ABSTRACT The characterization of the spatial structures of thin-layer sand bodies is the foundation for detailed reservoir description and physical property estimation. This requires using the geophysical inversion technique to make full use of the subsurface sedimentation information found in well-logging and seismic data for comprehensive evaluation. However, the traditional model-based inversion method is limited by the frequency bandwidth of seismic data, and the resolution of the inversion results cannot meet the accuracy requirements of thin-layer reservoir characterization. In this paper, we introduce a convolutional model and seismic-frequency-band constraints into a deep-learning framework to create a high-resolution inversion framework under physical constraints and then test our technical scheme in the model and on field data. We construct a 3D thin-layer sand body model with specific geologic implications and carry out inversion tests based on sparse spike inversion, geostatistical inversion, and a deep-learning framework. The experimental results demonstrate the feasibility of the deep-learning framework and reveal its limitations in characterizing the spatial distribution of sand bodies. After introducing the convolutional model and seismic-frequency-band constraints, the inversion results more clearly distinguish the spatial distribution and superposition relationships of different sand bodies. The results obtained based on the field data fully confirm the effectiveness and applicability of our method.
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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 | 0.000 |
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".