Siamese Neural Network for Multi-parameter Elastic Full Wave Inversion
Bibliographic record
Abstract
Summary Using an appropriate data misfit measure in Full Waveform Inversion (FWI) is essential for robust inversion performance. This challenge arises from our simplified Earth assumptions during modeling, resulting in simulated waveforms that differ from recorded data. To address this, we extend the Siamese framework for multi-parameter Elastic FWI (EFWI) by employing Haar wavelet transforms, which help the network identify key features for misfit measurement. The Siamese network consists of two identical convolutional neural network (CNN) branches, utilizing wavelet coefficients and applying the Euclidean distance for loss measurement. This self-supervised model optimizes its parameters during the EFWI process. A skip connection between the input and output enhances stability during initial iterations, allowing the framework to behave initially similarly to conventional EFWI while the network is still learning. After a few epochs, the network effectively learns significant features, improving inversion accuracy with minimal additional computational cost. Results from field data examples will be shared during the presentation of this work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".