Multiresolution neural networks for tracking seismic horizons from few training images
Bibliographic record
Abstract
Detecting a specific horizon in seismic images is a valuable tool for geologic interpretation. Because hand picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Until now, most networks have been trained on data that were created by cutting larger seismic images into many small patches. This limits the networks ability to learn from large-scale geologic structures. Moreover, currently available networks and training strategies require label patches that have full and continuous horizon picks (annotations), which are also time-consuming to generate. We have developed a projected loss function that enables training on labels with just a few annotated pixels and has no issue with the other unknown label pixels. We use this loss function for training convolutional networks with a multiresolution structure, including variants of the U-net. Our networks learn from a small number of large seismic images without creating patches. Training uses all seismic data without reserving some for validation. Only the labels are split into training/testing. We validate the accuracy of the trained network using the horizon picks that were never shown to the network. Contrary to other work on horizon tracking, we train the network to perform nonlinear regression, not classification. As such, we generate labels as the convolution of a Gaussian kernel and the known horizon locations that communicate uncertainty in the labels. The network output is the probability of the horizon location. We examine the new method on two different data sets, one for horizon extrapolation and another data set for interpolation. We found that the predictions of our methodology are accurate even in areas far from known horizon locations because our learning strategy exploits all data in large seismic images.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".