GAC - Newfoundland and Labrador Section Abstracts: 2019 Spring Technical Meeting
Bibliographic record
Abstract
Microearthquake locating has broad application in monitoring volcanic activities or human-induced earthquakes. However, typically large amounts of data are involved and those data are quite noisy. Hence, an automatic procedure is needed that can accurately locate these microearthquake events. In this research, we tackle this problem by stacking Characteristic Functions (CFs) in the Source Scanning Algorithm (SSA). The CFs allow us to consider waveform characteristics such as amplitude and polarization in the locating problem; and the SSA allows us to search the solution space automatically with minimum computing effort. Furthermore, we use multiscaled CFs to accommodate earthquake signals within a wide frequency band. We successfully locate synthetic events generated at 6km using the SIL Network in Reykjane Peninsula, SW Iceland. The SIL Network has a geometry of around 60 x 30 km. We also locate 215 events recorded by the same network with very similar results (less than 5% outliers with 80% of the result within an error of 2.5 km, 0.1s) to the manual picking method. In conclusion, stacking CFs in SSA is a noise-robust automatic method to locate microearthquakes in a reginal scale. It also avoids the need of manual phase picking and reduces human intervention.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".