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ZENODO
Software . 2025
Data sources: ZENODO
ZENODO
Software . 2025
Data sources: Datacite
ZENODO
Software . 2025
Data sources: Datacite
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FIO-QO3: Fractal Information Ontology for Seismic Risk Detection

Authors: Chechelnitsky, Igor;

FIO-QO3: Fractal Information Ontology for Seismic Risk Detection

Abstract

FIO-QO3 Global is a physics-based framework for detecting pre-event regimes in seismic catalogs using Fractal Information Ontology (FIO). This repository contains the methodology, source code, and validation results for analyzing seismic activity through the lens of complex systems and information theory. Core Methodology The system analyzes the temporal structure of earthquake sequences by identifying "informational compression" and phase transitions in the Earth's crust. It utilizes four key invariants: b-value (Aki-Utsu): A measure of differential stress accumulation. A significant drop (b 1 indicates a transition toward a self-organized critical (SOC) state. Information Entropy: A detector for regime transitions. Decreasing Shannon entropy indicates a focusing of the seismic process. Seismic Information Deficit (SID): A novel metric (introduced in T15) that measures the deviation from the background informational state. Validation Results (Japan, JMA Data 2017–2023) The framework has been rigorously tested using strict temporal train/test splits to prevent data leakage: Skill Score: 0.08–0.10 (statistically significant improvement). PR-AUC: Observed 4–5x improvement over stationary Poisson baselines for rare events (M >= 6.5). Feature Importance: FIO components contribute approximately 50% of the model's predictive power. Scientific Positioning and Constraints CRITICAL DISTINCTION: This framework is designed for REGIME DETECTION, not deterministic earthquake prediction. No Determinism: It provides probabilistic risk stratification, not a "time-location-magnitude" forecast. Vulnerability Assessment: The system identifies states of elevated seismic vulnerability (pre-event regimes). Academic Honesty: This work represents an incremental improvement in seismic risk assessment through complex systems physics.

All commercial usage, deployment, and sublicensing rights are strictly reserved by the Author, Igor Chechelnitsky. Unauthorized commercial use is prohibited. For commercial licensing inquiries, contact via Facebook. This software is part of the QADMON Canonical Research Framework.

If you use this software, please cite it as below.

Keywords

earthquake forecasting, FIO, QO3, seismic hazard, forecasting, fractal analysis, seismology, Tohoku, coefficient of variation, risk detection, machine learning, Japan, earthquake, b-value, ETAS, SOC, self-organized criticality, Gutenberg-Richter, Seismology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average