SiameseFWI: A Deep Learning Model for Full Waveform Inversion
Bibliographic record
Abstract
Summary Comparing simulated data with observed ones is an essential part of full waveform inversion (FWI). The kind of comparison employed is crucial to the success of FWI. We propose using a Siamese network to transform the observed and simulated data into a latent space so we compare the data representations. So, using two identical convolutional neural networks (CNN) with shared weights in FWI, we refer to as SiameseFWI, allows the networks to learn to extract key features from both simulated and observed data, and use the Euclidean distance to subsequently quantify the loss based on the transformed data. SiameseFWI operates as an unsupervised technique, eliminating the need for labeled data. In each FWI iteration, the Siamese network and the velocity model are sequentially updated to minimize the Euclidean distance loss. Empirical evaluation on the Marmousi2 model demonstrates that SiameseFWI achieves improved inversion performance compared to traditional FWI. Moreover, SiameseFWI exhibits superiority over the benchmark deep learning method while adding a small cost to the traditional FWI. We will share applications on real data in the presentation of this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".