Physically driven feature engineering for deep learning applications in seismo-volcanic signal analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The progressive growth of seismological databases has motivated the exploration of novel methodologies for common tasks such as detection and phase-picking, with a focus on maintaining reliability comparable to human performance. This goal consistently involves leveraging deep learning techniques, which emulate sensory processing in the human brain through numerical simulations. This study introduces a physically driven feature engineering approach that capitalizes on the inherent information within seismic data. While many contemporary studies train their models via robust raw datasets, practical alternatives tailored for smaller databases are often overlooked. Feature engineering in seismological contexts aims to develop deep learning models with tangible physical significance, specifically those that target event detection and phase-picking tasks across both local and regional seismic environments. Our approach leverages physically driven feature transformations for the joint detection and phase-picking task. This includes incorporating the energy signal envelope for effective seismic event classification, using amplitude spectra from signals filtered at predefined frequency bands, and calculating spatial features (such as wave incidence and azimuth) for accurate phase-picking. This integrated feature set optimizes model performance, especially when dealing with small volcanic seismology datasets. The proposed joint methodology is particularly pertinent in seismo-volcanic contexts, where accurate discrimination and characterization of seismic signals are pivotal for monitoring and risk assessment purposes. The incorporation of significant physical information from seismic signals into pattern recognition is crucial, as many feature engineering applications lack a contextual understanding of the data, which can lead to distortions, particularly within geophysical domains. Our results demonstrate human-level performance in these common tasks, harnessing the capabilities of statistical learning algorithms as a practical, resource-efficient solution for addressing these challenges on a large scale.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it