Diffraction pattern recognition using deep semantic segmentation
Bibliographic record
Abstract
ABSTRACT Diffraction imaging can help better understand small‐scale geological structures. Due to their often‐weak signal, in order to image them, it is necessary to separate diffraction signals from the rest of the wavefield. Many different methods have been developed for diffraction wavefield separation, and the newest trend includes the application of artificial neural networks and deep learning. Available case studies with a deep‐learning approach for diffraction separation show good results when applied to synthetic and sedimentary setting datasets where diffraction signals are either strong or have pronounced characteristics. Examples, however, are missing from crystalline or hardrock geological settings where the signal‐to‐noise ratio is by far lower and diffraction signals are usually within a complex reflectivity medium, have steep tails and are usually incomplete. In this study, we showcase the application of a deep semantic segmentation model on synthetic seismic, real ground‐penetrating radar, and hardrock seismic datasets. Synthetic seismic sections were generated using different random noise levels and coherent noise resembling a complex reflectivity pattern interfering with diffraction tails. For the real GPR dataset, diffraction signals were successfully delineated, although in some locations reflections were picked up because of their similar pixel values as the apex of the diffractions. As for the real seismic dataset, through a number of approaches, we were able to completely delineate a single diffraction within several inlines that was generated from a massive sulphide body. The algorithm also enabled us to recognize an incomplete diffraction, at the edge of the seismic cube, which was never labelled. This diffraction originated from outside of the seismic volume and may be a target for future mineral exploration programmes, thanks to the deep semantic segmentation algorithm providing this possibility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".