Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion
Bibliographic record
Abstract
In this paper, we formulate seismic full waveform inversion within a deep learning environment. We are motivated both by the possibilities of incorporating the training of multiple datasets with the relatively low dimensionality of theoryguided network design and by the fact that by doing so we implement an FWI algorithm ready-made for new computational architectures. A recurrent neural network is set up with rules enforcing elastic wave propagation, with the wavefield projected onto a measurement surface acting as the labeled data to be compared with observed seismic data. Training this network amounts to carrying out elastic FWI. Based on the Automatic Differential method, the gradients can be accurately and efficiently constructed by inspection and use of the computational graph, a gradient which acts to update the elastic model. Under the theory-guided network design, the Automatic Differential method provide efficiency and flexibility for different misfits and parameterization alterations. We use different misfits, which are the l2, l1 and Huber norm, to improve the inversion results for parameters in eFWI. We also prepare our approach to mitigate cross-talk, which is a general property of multiparameter full waveform inversion algorithms, by allowing relative freedom to vary the eFWI parameterizations. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: 351F Presentation Type: Oral
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".