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FacundoFirmenich/sysmic

Sysmic v8.0.0 — Fractal Tomography of Seismicity

License: GPL v3 Version JOSS Open in Colab Docker DOI

Sysmic is an open-source Python framework for precision-calibrated Bayesian fractal tomography of seismic catalogs. It implements the Grassberger–Procaccia correlation integral with MCMC inference of latent dimensionality ($D_3$), resolving the dual paradoxes caused by finite location uncertainty.

Companion software to: Firmenich, Firmenich & Firmenich (2026). Fractal Tomography and the Fisher Information Barrier of Seismicity. JGR: Solid Earth. Under review.
Preprint: doi:10.31223/x5rf3v (EarthArXiv, Dec 2025)


Key Results (Empirical — Zero Synthetic Data)

Region Network $\sigma_h$ (km) $D_2$ $D_3$ IQI Score
Noto, Japan Hi-Net 0.5 2.12 2.82 ± 0.05 ✅ 0.995
Tohoku, Japan Hi-Net 0.7 2.09 2.83 ± 0.06 ✅ 0.966
Tokachi, Japan Hi-Net 0.6 2.15 2.86 ± 0.07 ✅ 0.972
Valais, Switzerland Swiss-SED 0.8–1.2 1.50 1.911 ± 0.066 ✅ 0.988
SCSN baseline SCSN 0.1 ✅ 0.999
Cook Strait, NZ GeoNet* <1.0 2.24 2.53 ± 0.17 ✅ >0.90
Bay of Plenty, NZ GeoNet* <1.0 1.91 2.20 ± 0.20 ✅ >0.90
Sumatra GEOFON 7.5 2.21 2.998 ± 0.002 ❌ 0.000
Cascadia USGS 6.1 2.21 → 3.0 ❌ 0.329

*High-precision local relocations (not national GeoNet catalog).

Fisher Information Barrier: $\sigma_c = 2.3 \pm 0.4$ km — empirically validated precision threshold above which Bayesian $D_3$ inference collapses to the embedding-dimension prior.

Tectonic Hierarchy ($D_2$): Rifting (1.50) → Transform (1.81) → Subduction (2.12) → Collision (2.24) → Deep Slab (1.26).


Repository Structure

sysmic/ Core Python library (v8.0.0)
├── core.py Grassberger-Procaccia estimator (O(N log N), k-d tree)
├── bayesian_d3.py MCMC D3 inference (emcee, Uniform[1.5,3.0] prior)
├── statistics.py Bootstrap, Theil-Sen, b-value (Aki 1965)
├── geometry.py Steiner formula, deck-of-cards model (Q=2/3)
└── zaccagnino_stability.py Mmin-independence stability test (IQI criteria $S$)
data/ 22 empirical CSVs (all from real catalogs)
figures/ 14 manuscript figures (PNG 300 dpi)
figs_gen.py Reproducible figure generation (all 14 figs)
notebooks/
├── sysmic_colab.ipynb Interactive Google Colab (no install required)
└── tutorial.ipynb Step-by-step tutorial
app/
└── streamlit_app.py Interactive web dashboard
joss/ JOSS submission files (paper.md, paper.bib)
docs/
└── methods.md Extended methods reference
tests/
└── test_core.py Unit tests (6 test classes, 25+ assertions)
setup.py / pyproject.toml pip-installable package
Dockerfile Reproducible containerized environment
CITATION.cff Machine-readable citation metadata
.zenodo.json Zenodo archive metadata

Quickstart

Option 1 — Google Colab (zero install)

Open in Colab

Option 2 — Docker (fully reproducible)

docker build -t sysmic .
docker run -p 8501:8501 sysmic
# Open http://localhost:8501

Option 3 — Local install

pip install -r requirements.txt
python figs_gen.py # Reproduce all 14 figures
pytest tests/ -v # Run unit tests
streamlit run app/streamlit_app.py # Launch interactive dashboard

Option 4 — pip (editable install)

pip install -e .

Reproducing the Manuscript Figures

python figs_gen.py

All 14 figures are generated deterministically from the empirical CSVs in data/. No internet connection required. Output: figures/*.pdf (600 dpi) and figures/*.png (300 dpi). Runtime: ~2–3 min on a standard laptop.


MCMC Configuration (per manuscript §3.3)

Parameter Value
Prior Uniform[1.5, 3.0]
Walkers 32
Steps 10 000
Burn-in 2 000
ESS target > 5 000
Convergence $\hat{R} &lt; 1.01$

Data Sources

All data from publicly accessible catalogs:

Dataset Source Access
Hi-Net Japan (JUICE-relocated) NIED doi:10.17598/NIED.0003 Registered researchers
USGS Pan-American earthquake.usgs.gov Open
GeoNet New Zealand geonet.org.nz Open
GEOFON Sumatra geofon.gfz-potsdam.de Open
Swiss-SED Valais seismo.ethz.ch Open
ISC-GEM v12.1 doi:10.31905/D808B825 Open

Data Source (primary): United States Geological Survey (USGS)


Citation

@article{Firmenich2026,
 author = {Firmenich, Facundo and Firmenich, Pau and Firmenich, Le\'{o}n},
 title = {Fractal Tomography and the Fisher Information Barrier of Seismicity:
 Resolving Dual Paradoxes via Precision-Calibrated Bayesian Inference},
 journal = {Journal of Geophysical Research: Solid Earth},
 year = {2026},
 note = {Under review},
 doi = {10.31223/x5rf3v}
}
@software{Firmenich2026sysmic,
 author = {Firmenich, Facundo and Firmenich, Pau and Firmenich, Le\'{o}n},
 title = {Sysmic: Fractal Tomography of Seismicity},
 version = {8.0.0},
 year = {2026},
 doi = {10.5281/zenodo.18480821},
 url = {https://github.com/cedesur/sysmic}
}

License

GPLv3 — see LICENSE. Free to use, study, modify, and share.


April 8, 2026 — Sysmic v8.0.0

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Open-Source Intelligence Framework for High-Precision Fractal Tomography and Real-Time Seismic Monitoring. Topics: seismology, fractal-analysis, python, open-science, earthquake-forecasting, topological-data-analysis

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