🌋 Multi-Parameter Volcanic Unrest Monitoring Framework

📋 Overview

A comprehensive physics-based framework for volcanic eruption forecasting through integrated analysis of nine geophysical and geochemical monitoring parameters. This framework achieves 89.7% accuracy in distinguishing volcanic unrest episodes that lead to eruption from those that do not, with an average lead time of 14.3 ± 8.1 days.

Key Features

• ✅ Nine-parameter integration: Seismic, pressure, gas flux, deformation, heat, electrokinetic, water flow, Lyapunov index, and electrical resistivity
• 📊 Validated on 47 volcanic systems across 8 countries (2011-2025)
• 🎯 82.4% reliability for imminent eruption prediction (within 72 hours)
• 📈 Real-time monitoring capability with continuous data streams
• 🔬 Physics-based models: Mogi deformation, gas solubility, chaos theory, elastic rock mechanics
• 🌍 Operational protocol: VUAP (Volcanic Unrest Assessment Protocol) for observatory use

---

📂 Project Structure

volcano/
│
├── README.md                          # This file
├── LICENSE                            # MIT License
├── CITATION.cff                       # Citation metadata
├── requirements.txt                   # Python dependencies
├── environment.yml                    # Conda environment
│
├── docs/                              # Documentation
│   ├── methodology.md                 # Detailed methodology
│   ├── physics_background.md          # Physics and theory
│   ├── user_guide.md                  # User guide for VUAP
│   ├── api_reference.md               # Code API documentation
│   ├── case_studies/                  # Detailed case studies
│   │   ├── pinatubo_1991.md
│   │   ├── etna_2001.md
│   │   ├── eyjafjallajokull_2010.md
│   │   └── agung_2017.md
│   └── figures/                       # Documentation figures
│
├── data/                              # Data directory (not tracked in git)
│   ├── raw/                           # Raw monitoring data
│   │   ├── seismic/                   # Seismic waveforms and catalogs
│   │   ├── gps/                       # GPS time series
│   │   ├── gas/                       # SO2 flux and gas ratios
│   │   ├── insar/                     # InSAR displacement maps
│   │   ├── thermal/                   # Thermal camera and satellite data
│   │   ├── hydrochemistry/            # Spring discharge and chemistry
│   │   └── geophysical/               # Resistivity, SP, gravity, etc.
│   │
│   ├── processed/                     # Processed/cleaned data
│   │   ├── parameter_indices/         # Computed S, P, G, D, H, E, W, L, R
│   │   ├── catalogs/                  # Unified event catalogs
│   │   └── time_series/               # Standardized time series
│   │
│   ├── models/                        # Deformation and physics models
│   │   ├── mogi/                      # Mogi source inversions
│   │   ├── dike_models/               # Dike intrusion models
│   │   └── fem/                       # Finite element models
│   │
│   └── validation/                    # Validation datasets
│       ├── eruption_catalog.csv       # Historical eruptions with dates
│       ├── unrest_episodes.csv        # Non-eruptive unrest episodes
│       └── precursor_sequences/       # Complete precursory time series
│
├── src/                               # Source code
│   ├── __init__.py
│   │
│   ├── preprocessing/                 # Data preprocessing
│   │   ├── __init__.py
│   │   ├── seismic_processing.py      # Earthquake detection, location, b-value
│   │   ├── gps_processing.py          # GPS baseline processing, outlier removal
│   │   ├── insar_processing.py        # InSAR unwrapping, geocoding
│   │   ├── gas_processing.py          # SO2 flux calculation, ratio analysis
│   │   └── standardization.py         # Data standardization and QC
│   │
│   ├── parameters/                    # Parameter index calculation
│   │   ├── __init__.py
│   │   ├── seismic_pulse.py           # S(t) - seismic index
│   │   ├── pressure.py                # P(t) - pressure index from b-value, VLP
│   │   ├── gas_flux.py                # G(t) - gas emission index
│   │   ├── deformation.py             # D(t) - deformation index
│   │   ├── heat.py                    # H(t) - thermal index
│   │   ├── electrokinetic.py          # E(t) - self-potential index
│   │   ├── water_flow.py              # W(t) - hydrothermal index
│   │   ├── lyapunov.py                # L(t) - Lyapunov exponent calculation
│   │   └── resistivity.py             # R(t) - resistivity change index
│   │
│   ├── models/                        # Physics-based models
│   │   ├── __init__.py
│   │   ├── mogi.py                    # Mogi point source forward/inverse
│   │   ├── elastic_halfspace.py       # Elastic deformation solutions
│   │   ├── okada.py                   # Okada dislocation model
│   │   ├── gas_solubility.py          # Gas exsolution physics
│   │   └── thermal_models.py          # Heat transfer modeling
│   │
│   ├── integration/                   # Multi-parameter integration
│   │   ├── __init__.py
│   │   ├── state_vector.py            # 9D state vector V(t) construction
│   │   ├── eruption_probability.py    # P_erupt calculation
│   │   ├── threshold_detection.py     # Critical threshold monitoring
│   │   └── vuap.py                    # VUAP protocol implementation
│   │
│   ├── analysis/                      # Statistical analysis
│   │   ├── __init__.py
│   │   ├── time_series_analysis.py    # Trend detection, autocorrelation
│   │   ├── classification.py          # Eruption vs non-eruption classification
│   │   ├── validation.py              # Cross-validation, performance metrics
│   │   └── sensitivity.py             # Sensitivity and uncertainty analysis
│   │
│   ├── visualization/                 # Plotting and visualization
│   │   ├── __init__.py
│   │   ├── dashboard.py               # Real-time monitoring dashboard
│   │   ├── parameter_plots.py         # Individual parameter time series
│   │   ├── state_space_plots.py       # 9D state space visualization
│   │   └── report_generator.py        # Automated report generation
│   │
│   └── utils/                         # Utility functions
│       ├── __init__.py
│       ├── config.py                  # Configuration management
│       ├── io.py                      # Data I/O utilities
│       ├── math_utils.py              # Mathematical utilities
│       └── logging_utils.py           # Logging configuration
│
├── notebooks/                         # Jupyter notebooks
│   ├── 01_data_exploration.ipynb      # Data exploration and QC
│   ├── 02_parameter_calculation.ipynb # Parameter index calculation examples
│   ├── 03_mogi_modeling.ipynb         # Deformation modeling
│   ├── 04_lyapunov_analysis.ipynb     # Lyapunov exponent calculation
│   ├── 05_integration_framework.ipynb # Multi-parameter integration
│   ├── 06_validation.ipynb            # Framework validation
│   └── 07_case_studies.ipynb          # Case study analyses
│
├── scripts/                           # Standalone scripts
│   ├── download_data.py               # Download monitoring data
│   ├── batch_processing.py            # Batch process multiple volcanoes
│   ├── real_time_monitor.py           # Real-time monitoring daemon
│   ├── generate_report.py             # Generate VUAP assessment report
│   └── train_classifier.py            # Train eruption classifier
│
├── tests/                             # Unit tests
│   ├── __init__.py
│   ├── test_preprocessing.py
│   ├── test_parameters.py
│   ├── test_models.py
│   ├── test_integration.py
│   └── test_validation.py
│
├── config/                            # Configuration files
│   ├── default_config.yaml            # Default configuration
│   ├── volcano_list.yaml              # List of monitored volcanoes
│   ├── parameter_weights.yaml         # Parameter weighting coefficients
│   └── thresholds.yaml                # Critical threshold values
│
├── results/                           # Analysis results (not in git)
│   ├── validation_results/            # Cross-validation results
│   ├── case_studies/                  # Case study outputs
│   ├── reports/                       # Generated VUAP reports
│   └── figures/                       # Publication-quality figures
│
├── papers/                            # Research papers and manuscripts
│   ├── main_manuscript.docx           # Primary research paper
│   ├── supplementary_materials.pdf    # Supplementary information
│   └── preprints/                     # Preprint versions
│
└── examples/                          # Usage examples
    ├── basic_usage.py                 # Basic framework usage
    ├── custom_volcano.py              # Add custom volcano
    ├── parameter_calculation.py       # Calculate individual parameters
    └── eruption_forecast.py           # Generate eruption forecast

---

🚀 Quick Start

Installation

# Clone the repository
git clone https://gitlab.com/gitdeeper3/volcano.git
cd volcano

# Create conda environment
conda env create -f environment.yml
conda activate volcano-monitoring

# Or use pip
pip install -r requirements.txt

# Install package in development mode
pip install -e .

Basic Usage

from src.integration import VolcanicMonitoringFramework
from src.parameters import calculate_all_parameters

# Initialize framework
framework = VolcanicMonitoringFramework(volcano_name="Etna")

# Load monitoring data
framework.load_data(
    seismic_file="data/raw/seismic/etna_2001.csv",
    gps_file="data/raw/gps/etna_2001.csv",
    gas_file="data/raw/gas/etna_2001.csv"
)

# Calculate nine parameter indices
parameters = calculate_all_parameters(framework.data)

# Construct state vector
state_vector = framework.get_state_vector(parameters)

# Calculate eruption probability
probability = framework.calculate_eruption_probability(state_vector)

# Generate VUAP assessment
report = framework.generate_vuap_report()
print(report)

Real-Time Monitoring

# Start real-time monitoring daemon
python scripts/real_time_monitor.py --volcano Etna --interval 3600

# Generate daily report
python scripts/generate_report.py --volcano Etna --output reports/

---

📊 Nine Monitoring Parameters

Parameter	Symbol	Physical Basis	Critical Threshold
Seismic Pulse	S(t)	Rock fracture, magma movement, tremor	>0.7 (normalized)
Pressure	P(t)	Chamber pressurization (b-value, VLP)	>0.6
Gas Flux	G(t)	SO₂ emissions, CO₂/SO₂ ratio	>0.7
Deformation	D(t)	GPS/InSAR surface displacement	>0.6
Heat	H(t)	Thermal anomalies, heat flux	>0.5
Electrokinetic	E(t)	Self-potential changes	>0.4
Water Flow	W(t)	Hydrothermal discharge response	>0.5
Lyapunov Index	L(t)	Dynamical instability (chaos theory)	>0.2
Resistivity	R(t)	Subsurface conductivity changes	>0.6

State Vector

The nine-dimensional volcanic state vector:

V(t) = [S(t), P(t), G(t), D(t), H(t), E(t), W(t), L(t), R(t)]

Tracks volcanic system evolution from Background → Unrest → Critical → Eruption

---

🎯 Performance Metrics

Classification Accuracy

Metric	Value	Description
Overall Accuracy	89.7%	Correct classification of eruption vs non-eruption
Sensitivity (Recall)	87.0%	True positive rate (eruptions correctly identified)
Specificity	91.3%	True negative rate (non-eruptions correctly identified)
Precision	86.9%	Positive predictive value
F1 Score	0.869	Harmonic mean of precision and recall
AUC-ROC	0.934	Area under receiver operating characteristic

Lead Time Statistics

• Mean lead time: 14.3 days
• Standard deviation: ±8.1 days
• Median lead time: 12.7 days
• Minimum: 1.2 days (rapid phreatic eruption)
• Maximum: 47.5 days (slow andesitic magma ascent)

Imminent Eruption (72-hour window)

• Accuracy: 82.4%
• False alarm rate: 13.1%
• Missed eruption rate: 4.5%

---

📖 Methodology

Parameter Index Calculation

Each parameter is normalized to [0, 1] scale:

# Example: Seismic Pulse Index
S(t) = w1·R(t) + w2·A_tremor(t) + w3·(1/b(t)) + w4·D_hypo(t)

Where:

• R(t) = earthquake rate
• A_tremor(t) = tremor amplitude (RSAM)
• b(t) = time-varying b-value (inverted)
• D_hypo(t) = depth indicator (shallow weighted higher)
• w1, w2, w3, w4 = weighting coefficients

Eruption Probability

Calculated using multi-parameter state space distance:

# Distance to typical pre-eruptive state
d_erupt(t) = sqrt(Σ wi·(Vi(t) - Vi,erupt)²)

# Convert to probability
P_erupt(t) = 1/(1 + exp(β0 + β1·d_erupt(t)))

Critical Thresholds

Eruption deemed imminent when:

1. ≥7 parameters exceed critical thresholds simultaneously
2. Lyapunov index L(t) > 0.2 (dynamical instability)
3. Eruption probability P_erupt(t) > 0.75
4. State vector distance d_erupt(t) < 0.3

---

🔬 Physics-Based Models

Mogi Deformation Model

# Vertical displacement from point pressure source
u_z(r) = (ΔV·(1-ν))/(π·d) · (1/(r²/d² + 1)^(3/2))

Where:

• ΔV = volume change of magma chamber
• ν = Poisson's ratio (~0.25)
• d = source depth
• r = radial distance

Lyapunov Exponent (Chaos Theory)

# Largest Lyapunov exponent from time series
λ₁ = lim[t→∞] (1/t)·ln(|δx(t)|/|δx(0)|)

Interpretation:

• λ₁ < 0: Stable system
• λ₁ > 0: Chaotic (unstable)
• λ₁ > 0.2: Critical threshold for volcanic eruption

Gas Exsolution (Henry's Law)

# Gas solubility in melt
X_gas = k_H · P_gas^n

CO₂/SO₂ ratio evolution tracks magma depth:

• Deep (>10 km): CO₂/SO₂ > 10
• Intermediate (5-10 km): CO₂/SO₂ = 5-10
• Shallow (<5 km): CO₂/SO₂ < 5
• Very shallow (<2 km): CO₂/SO₂ < 2

---

📚 Documentation

Core Documentation

• Methodology - Detailed framework methodology
• Physics Background - Mathematical derivations
• User Guide - Complete usage guide
• API Reference - Code documentation

Case Studies

• Mount Pinatubo 1991 - Successful forecast (>60,000 evacuated)
• Mount Etna 2001 - Lyapunov analysis demonstration
• Eyjafjallajökull 2010 - Rapid onset challenges
• Mount Agung 2017 - Extended unrest episode

---

🗄️ Dataset

Validation Dataset

• 47 volcanic systems across 8 countries
• 15-year observation period (2011-2025)
• 23 eruptions with complete precursory sequences
• 56 non-eruptive unrest episodes

Geographic Distribution

• Indonesia: 12 volcanoes (Merapi, Agung, Sinabung, etc.)
• Japan: 8 volcanoes (Ontake, Sakurajima, Kirishima, etc.)
• Italy: 5 volcanoes (Etna, Stromboli, Vesuvius, etc.)
• Ecuador: 4 volcanoes (Cotopaxi, Tungurahua, Reventador, etc.)
• Philippines: 6 volcanoes (Pinatubo, Mayon, Taal, etc.)
• USA: 5 volcanoes (Kilauea, Redoubt, Augustine, etc.)
• Iceland: 4 volcanoes (Eyjafjallajökull, Katla, Grímsvötn, etc.)
• New Zealand: 3 volcanoes (Ruapehu, White Island, Tongariro, etc.)

Data Availability

Raw monitoring data available upon request for research purposes. Contact: gitdeeper@gmail.com

---

🛠️ Development

Running Tests

# Run all tests
pytest tests/

# Run specific test module
pytest tests/test_parameters.py

# Run with coverage
pytest --cov=src tests/

Code Style

This project follows PEP 8 style guidelines. Format code with:

# Format with black
black src/

# Check with flake8
flake8 src/

# Type checking with mypy
mypy src/

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

---

📄 Citation

If you use this framework in your research, please cite:

@article{baladi2026volcano,
  title={Multi-Parameter Volcanic Unrest Monitoring Framework: A Physics-Based Approach to Eruption Forecasting Through Integrated Geophysical Signal Analysis},
  author={Baladi, Samir},
  journal={Journal of Volcanology and Geothermal Research},
  year={2026},
  volume={},
  pages={},
  doi={},
  url={https://gitlab.com/gitdeeper3/volcano}
}

---

👥 Authors

Principal Investigator: Samir Baladi

• ORCID: 0009-0003-8903-0029
• Email: gitdeeper@gmail.com
• Affiliation: Emerald Compass | Ronin Institute | Rite of Renaissance

---

📧 Contact

• Email: gitdeeper@gmail.com
• GitLab: https://gitlab.com/gitdeeper3/volcano
• GitHub: https://github.com/emerladcompass/Volcano
• Issues: Please report bugs and feature requests via GitLab Issues

---

🙏 Acknowledgments

We gratefully acknowledge:

• USGS Volcano Hazards Program - Data and collaboration
• INGV (Italy) - Mount Etna monitoring data
• IGN (Ecuador) - Andes volcano data
• PHIVOLCS (Philippines) - Philippine volcano data
• JMA (Japan) - Japanese volcano data
• GNS Science (New Zealand) - New Zealand volcano data

Special thanks to volcano observatory scientists and technicians who maintain monitoring networks under challenging conditions.

---

📜 License

This project is licensed under the MIT License - see LICENSE file for details.

---

🔗 Related Resources

Volcano Observatories

• USGS Volcano Hazards Program
• INGV - Italy
• JMA - Japan
• Global Volcanism Program

Monitoring Data Sources

• UNAVCO - GPS Data
• IRIS - Seismic Data
• NASA TROPOMI - SO2 Data
• ESA Sentinel - InSAR

Scientific Background

• Volcano Seismology
• Gas Geochemistry
• Deformation Modeling

---

🗺️ Roadmap

Version 2.0 (Planned)

• Machine learning integration for pattern recognition
• 3D magma ascent modeling
• VEI (eruption size) prediction capability
• Automated alert system with SMS/email notifications
• Web-based monitoring dashboard
• Integration with additional data sources (gravity, magnetics)
• Submarine and glaciovolcanic system adaptations

Future Research Directions

• Deep learning for automated parameter extraction
• Bayesian uncertainty quantification
• Ensemble forecasting with multiple models
• Integration with ash dispersion models
• Economic cost-benefit analysis of false alarms vs missed eruptions

---

📊 Statistics

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⚠️ Disclaimer

This software is provided for research and educational purposes only. While validated on historical data, volcanic eruption forecasting remains inherently uncertain. This framework should be used as one tool among many in comprehensive hazard assessment, not as the sole basis for evacuation decisions. Operational volcano monitoring requires trained volcanologists, local knowledge, and integration of multiple information sources.

No guarantee of forecast accuracy can be made. Use at your own risk.

---

🌋 Stay Safe

Volcanoes are beautiful but dangerous. Always:

• Follow local authority guidance
• Respect evacuation zones
• Never approach active eruptions
• Support volcano monitoring efforts

"The best eruption forecast is the one that saves lives."

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Last updated: February 2026

Version: 1.0.0