Visibility Graph Complex Network Analysis on Seismic-Induced Variation in Groundwater Level of Nanxi Well, China
Bibliographic record
Abstract
Groundwater level in wells, i.e., well water level (WWL) is an important index in hydrological monitoring during earthquakes. Due to the complex dynamics of groundwater, the WWL might change under seismic actions. This paper attempts to identify the long-term correlation between WWL and earthquakes, and disclose the topological features of groundwater dynamics. Taking Nanxi Well as an example, the authors conducted state space analysis on the raw series and trend of WWL to eliminate interferences like barometric pressure, rainfall, and solid tide, creating the trend time series. Then, the raw series and trend time series were converted into the raw visible graph (VG) network and trend VG network, respectively. Further, the global period was divided into five local time windows, and the two VG networks were compared by global aspect, local aspect, and topological properties of complex networks. The results show that the nodes of high degrees are closely related to the seismic response of the WWL in Nanxi Well; all VG networks are scale free and hierarchical; the seismic response of the WWL in the well is reflected by degree correlation; the community division of raw VG network was basically the same as that of trend VG network. The research findings provide insights to the seismic response of WWL and the dynamic fluctuation of groundwater level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".