Unsupervised seismic acoustic impedance inversion based on generative diffusion model
Bibliographic record
Abstract
ABSTRACT Seismic acoustic impedance, defined as the product of rock density and seismic velocity, is essential for identifying different rock layers and their contents. Seismic acoustic impedance inversion (SAII) is a crucial technique for deriving high-resolution impedance profiles, aiding in the interpretation of subsurface geologic structures, reservoir identification, and lithologic characterization. Although deep-learning-based methods have become a new inversion paradigm, they often require high-quality labeled data and accurate low-frequency impedance models to produce high-resolution impedance profiles. We develop an unsupervised SAII method based on a generative diffusion model to address these limitations. Our approach begins by training the generative diffusion model using low-frequency impedance models as the conditional input, allowing it to capture the complex prior distribution from the training data. We then incorporate an explicit physical measurement model into the diffusion model sampling process to approximate the posterior distribution. Our method mitigates the dependency on low-frequency impedance and enhances inversion performance. Notably, our method is an unsupervised inversion framework, effectively addressing the inverse problem without the constraints of measurements and labeled data requirements. Synthetic and field data experiments validate our method, demonstrating superior accuracy compared with supervised deep learning, unsupervised deep learning, and regularization methods.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".