Graphics Processing Unit-Based Match and Locate (GPU-M&L): An Improved Match and Locate Method and Its Application
Bibliographic record
Abstract
Abstract Microearthquake detection and location are critical for understanding earthquake mechanisms and mitigating seismic hazards. Match and locate (M&L) is an effective method for simultaneously detecting and locating small earthquakes. However, the heavy computational demands of the M&L make it challenging to apply to big data. In this article, we develop an improved M&L method—called graphics processing unit-based M&L (GPU-M&L). The GPU-M&L differs from the M&L in two ways: (1) adding weighting factor for each component of templates to improve the detection ability and (2) implementing the M&L method on GPU to accelerate the computation. Synthetic tests show the GPU-M&L can not only handle smaller earthquakes than the M&L but also perform 4.5 times faster than the M&L parallelly programed on central processing unit. As an example, we utilize the GPU-M&L to study the seismic activity during seven days after the 2015 Ms 5.8 Alxa, China, earthquake (from 15 to 21 April 2015). Using 38 cataloged earthquakes as templates, we detect ∼20 times more events than in the routine catalog. The distribution of those detected events, along with focal mechanisms of large events, suggests that the 2015 Ms 5.8 earthquake occurred on an east–west-trending hidden strike-slip fault.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".