Seismic Facies Analysis: A Deep Domain Adaptation Approach
Bibliographic record
Abstract
Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data but often fail to do so when labeled data are scarce. DNNs sometimes fail to generalize on test data sampled from different input distributions. Unsupervised deep domain adaptation (DDA) techniques have been proven useful when no labels are available and when distribution shifts are observed in the target domain (TD). In this study, experiments are performed on seismic images of the F3 block 3-D dataset from offshore Netherlands [source domain (SD)] and Penobscot 3-D survey data from Canada (TD). Three geological classes from SD and TD that have similar reflection patterns are considered. A DNN architecture named EarthAdaptNet (EAN) is proposed to semantically segment the seismic images when few classes have data scarcity, and we use a transposed residual unit to replace the traditional dilated convolution in the decoder block. The EAN achieved a pixel-level accuracy >84% and an accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 70$ </tex-math></inline-formula> % for the minority classes, showing improved performance compared to existing architectures. In addition, we introduce the correlation alignment (CORAL) method to the EAN to create an unsupervised deep domain adaptation network (EAN-DDA) for the classification of seismic reflections from F3 and Penobscot to demonstrate possible approaches when labeled data are unavailable. Maximum class accuracy achieved was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 99$ </tex-math></inline-formula> % for class 2 of Penobscot with an overall accuracy >50%. Taken together, the EAN-DDA has the potential to classify TD seismic facies classes with high accuracy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".