OBSPicker: A generalized transfer-learned OBS phase picker
Bibliographic record
Abstract
Offshore earthquakes recorded by Ocean Bottom Seismometers (OBS) are crucial to studying tectonic activities in the subduction zones and mid-ocean ridges. In recent years, the ever-advancing Machine Learning (ML)-based phase pickers have shown promise in land earthquake monitoring, but there are few available ML models to handle OBS data due mainly to the lack of labelled training sets and low signal-to-noise ratios. Though land ML-based phase pickers may roughly work for OBS data, they introduce a large number of false negatives and false positives, leading to numerous events being missing and fake.In this study, we create a tectonically inclusive OBS training data set and develop a generalized deep-learning OBS phase picker - OBSPicker using the EQTransformer (EQT; Mousavi et al., 2019) and the transfer learning approach. To create an inclusive OBS training data set, we collect earthquake waveforms from routine catalogues recorded at 11 OBS networks worldwide with different tectonic settings and geographic locations. Earthquakes are recorded in local and regional distances with diverse magnitudes (ML 0.0-5.8), source depths (0-250 km), and epicentral distances (0-3 deg). To label their phase picks, we adopt a sequence of processing steps including 1) initial phase arrival detection and picking by EQT, 2) identifying and discarding samples with multiple (unwanted) events using STA/LTA method, and 3) refining phase picks using the Generalized Phase Detection method (GPD, Ross et al., 2018), resulting in ~38,000 well-labelled earthquake samples. In addition, we also collect ~150,000 OBS noise samples from the same OBS networks for training augmentation instead of using the commonly adopted Gaussian noises. Those OBS noise samples are used to simulate low-magnitude earthquakes under different marine environments. Initial results show that our transfer-learned OBS phase picker outperforms the EQTransformer base model in both accuracy and precision, especially in presence of higher levels of noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".