Extracting preseismic electromagnetic signatures in terms of symbolic dynamics
Bibliographic record
Abstract
Abstract. When a heterogeneous material is strained, its evolution toward breaking is characterized by the nucleation and the coalescence of micro-cracks before the final break-up. Electromagnetic (EM) emission in a wide frequency spectrum ranging from very low frequencies (VLF) to very high frequencies (VHF) is produced by micro-cracks, which can be considered as the so-called precursors of general fracture. Herein we consider earthquakes (EQs) as large-scale fracture phenomena. We study the capability of nonlinear time series analysis to extract features from pre-seismic electromagnetic (EM) activity possibly indicating the nucleation of the impending EQ. In particular, we want to quantify and to visualize temporal changes of the complexity into consecutive time-windows of the time series. In this direction the original continuous time EM data is projected to a linguistic symbolic sequence and then we calculate the block entropies of the optimal partition. This analysis reveals a significant reduction of complexity of the underlying fracto-electromagnetic mechanism as the catastrophic events is approaching. We verify this result in terms of correlation dimension analysis. We point out that these findings are compatible with results from an independent linear method which uses a wavelet based approach for the estimation of fractal spectral characteristics. Field and laboratory experiments associate the epoch of low complexity in the tail of the precursory emission with the nucleation phase of the impending earthquake.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".