Correlelations between the Anomalous Behaviour of the Ionosphere and the Seismic Events for VTX-MALDA VLF Propagation
Bibliographic record
Abstract
One of the most important application of the VLF signals is that it contains possible information about the lithosphere‐ionosphere coupling. In other words, in near future, it may be possible to predict seismic events by judging signatures of VLF signals. In this paper, we present the result of the monitoring of the VLF signals collected in the Malda branch of ICSP, located in Malda, West Bengal, for four years (2005, 2007–09) and we try to find out the co‐relations, if any, between the ionospheric activities and the earthquakes. Here we use that VLF signals which are transmitted from the VTX station (18.2 KHz), located near Vijayanarayanam in Tamilnadu, about 2290 km away from the receiver. To find out the co‐relation of the ionospheric activities with the seismic events such as earthquake, first we have to study the average signal throughout the year. For this, we plot the so‐called standardized calibration curve using the four years data. Here we use a total of 481 no. of data. To establish the co‐relation between the ionospheric activities and the seismic events, we use the data of the year 2008 and we found that the deviations of the anomalous data are co‐related with the seismic event. We found that the highest deviation takes place one day prior to the seismic events. We also calculated the 'D‐layer preparation time' (DLPT) and the 'D‐layer disappearance time' (DLDT) for the data of 2008 and tried to establish the co‐relation between the anomalous DLPT and DLDT with the seismic events, if any. We compare our result with the VLF signals received from other places.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".