Self-supervised Deep Learning Framework for Multi-Source Full Wave Inversion
Bibliographic record
Abstract
Summary Full Wave Inversion (FWI) is an effective tool for estimating subsurface velocity models; however, its high nonlinearity presents certain limitations, including significant computational costs in terms of time and hardware resources. Accelerating the FWI inversion process without compromising performance remains a challenging task. To address this issue, we propose the FreqSiameseFWI framework, a deep learning-based misfit function designed to expedite the FWI inversion process and support Multi-source FWI (MSFWI) by mitigating the impact of cross-talk noise. FreqSiameseFWI employs a self-supervised learning approach integrated within the FWI framework, enabling iterative updates of its parameters without introducing significant additional overhead costs. The seismic data are converted to the frequency domain through the Fast Fourier Transform (FFT), allowing the Siamese network to extract spectral features from the seismic data and achieve robust inversion performance. The proposed FreqSiameseFWI effectively mitigates the influence of cross-talk noise. The performance of FreqSiameseFWI, evaluated using the Overthrust model, has yielded promising results that outperform conventional misfit functions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".