Deep neural networks for 1D impedance inversion
Why this work is in the frame
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Bibliographic record
Abstract
SummaryWe investigate the applicability of different types of deep neural networks for the estimation of subsurface properties from seismic data. The pre-trained networks can predict velocity models from new data in a few milliseconds, which makes this data-driven approach especially important for multidimensional inversion, where conventional methods inversion methods suffer from large computational cost. At the same time, realistic one-dimensional models such as the 160-layer velocity model used as an example in this study require large synthetic datasets for training, which are not always possible to obtain. Hence, we also study the impact of extending the training data by adding random noise to the modelled examples. We observe that enlarging training datasets by adding synthetic noise to existing samples improves the quality of inversion without a significant increase in computational complexity.
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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) | 0.001 | 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 it