Comparative Analysis of Variants of Geomagnetic Diurnal Variation Ratio Method for Earthquake Precursor Detection
Bibliographic record
Abstract
The application of electromagnetic signals in earthquake study has been applied by previous researchers through the monitoring of geomagnetic variations. The previous studies have revealed inconsistencies in the implementation of the diurnal variation ratio (DVR) method and the results were also found to be limited in specific events. This study sought to enhance the reliability of earthquake forecasting by implementing two different variants of the DVR method in investigating the magnetic responses prior to earthquakes (EQ). Global EQ events that occurred between 2000-2020 with magnitude above 5.0 were observed. The anomalies were detected as early as 60 days to 1 day prior to the EQ events for DVR using threshold value (Method 1), and 30 days to 15 days prior to the EQ events for DVR using the comparison with 1-year background geomagnetic data (Method 2). All geomagnetic N, E, and Z components showed anomalous behaviour during the quiet days but with temporal lags between the components. It can be concluded that Method 1 approach, yielded results with significantly more precursor presence than Method 2. The relationship of the geomagnetic variations with earthquake properties such as magnitude and focal depth showed higher rate of precursor presence in both the strong and mid-focus EQ. Future studies will be conducted to correlate geomagnetic variations with seismo-ionospheric response and physical ground movement prior to the events. The outcomes of this study will be able to provide insights of effective analysis for precursor study particularly in seismic hazard.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".