TAMC-FRANJAMAR v3 — Reproducible Multistation Seismic Analysis Framework Key idea:The relevant signal is not peak amplitude at individual stations, but the emergence of temporally sustained, network-coherent structure. This repository provides a fully reproducible implementation of a retrospective pipeline designed to analyze collective statistical behavior in multistation seismic networks. The framework operates strictly a posteriori, using fixed parameters and predefined temporal windows. It does not perform prediction or real-time forecasting. Methodological principles:- Fixed parameters (no event-specific tuning)- Matched control windows- Empirical null models These constraints ensure consistent statistical evaluation and avoid overfitting. Rapid-response example: Applied to the 2026-04-20 M7.4 Miyako (Japan) earthquake, the framework detects a sharp emergence of network-coherent structure at the time of the mainshock, followed by a structured temporal decay. The same signal is also recovered in continuous monitoring mode without prior temporal alignment, showing that significant events can be identified directly from ongoing data streams. Live monitoring dashboard: An experimental, non-predictive monitoring interface implementing the same pipeline is available at:https://franjamar-monitor.streamlit.app/ The system runs the identical pipeline in a continuous monitoring configuration (T–1 h delayed window), enabling near-real-time exploration of multistation coherence patterns across multiple seismic and volcanic regions. Quick start:- conda create -n tamc python=3.10 -y- conda activate tamc- pip install -r requirements.txt- python full_pipeline_franjamarv3.py Reproducibility:All analyses are fully reproducible under fixed parameters and consistent data ingestion. Disclaimer:This framework is diagnostic and non-predictive.