Statistical Analysis of Pre‐earthquake Electromagnetic Anomalies in the ULF Range
Bibliographic record
Abstract
Abstract Assessing the statistical significance of electromagnetic anomalies in the ultralow frequency (ULF) range observed prior to earthquakes is a necessary step toward determining whether these perturbations constitute actual earthquake precursors. A statistical epoch analysis (SEA) was recently performed by Han et al. (2014, https://doi.org/10.1002/2014JA019789 ) to analyze earthquakes happening between 2001 and 2010 near the geomagnetic observatory of Kakioka, Japan; the authors found a significant number of anomalies 6 to 15 days prior to the earthquake day within 100 km from Kakioka, while no significant pre‐earthquake activity was observed for the farther region 100 to 216 km from the observatory. In this current paper, we describe the application of our independent software implementation of their method. Despite using a different outlier rejection scheme, we manage to approximate their results. Upon validation of our program, we conduct multiple sensitivity studies. First, we explore how different outlier rejection schemes impact the results. We then restrict the analysis to only mantle earthquakes, highlighting a marginally significant number of anomalies prior to the earthquake day. Next, we test a higher band‐pass filter than the one initially used but find no anomalous pre‐earthquake activity in this higher‐frequency band. We then use a different catalog to establish the list of qualifying “earthquake days” which also leads the anomalous pre‐earthquake episode to vanish, thus raising concerns about the robustness of the results. Finally, we apply the SEA to another time window, ranging from 2013 to 2018: No significant pre‐earthquake episode can be observed for this interval. We conclude our study by providing guidelines for upcoming work.
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 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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".