Cosmic Rays and Earthquakes: A Rigorous Replication Study

A reproducible, GPU-accelerated statistical pipeline that tests the claimed correlation between galactic cosmic-ray flux and global seismicity (Homola et al. 2023).

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Summary of findings

Stage	Key result
In-sample replication (1976–2019)	r(+15 d) = +0.31 raw; drops to +0.04 after solar-cycle detrending
Global surrogate test (IAAFT, 100 k surrogates)	p = 1.00 after detrending — not significant
Geographic localisation (34 stations × 207 cells)	No distance–lag dependence; β = −0.45 d/1000 km, p = 0.21
Out-of-sample validation (2020–2025)	Results in results/out_of_sample_report.md

The raw r = 0.31 is an artefact of the shared ~11-year solar cycle modulating both cosmic-ray flux and seismicity. After removing this trend the signal is indistinguishable from phase-randomised noise.

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Repository structure

scripts/          Analysis pipeline (run in order)
  01_download_data.py            Download NMDB / USGS / SIDC data
  02_homola_replication.py       Replicate Homola et al. cross-correlation
  03_stress_test.py              Surrogate significance test (CPU + GPU)
  04_detrended_analysis.py       HP-filter / sunspot detrending
  05_geographic_localisation.py  Station × grid-cell BH-FDR scan
  06_check_data_availability.py  Determine reliable OOS data window
  07_out_of_sample.py            Pre-registered out-of-sample validation
  08_combined_timeseries.py      1976-to-present sinusoid fit + Bayes factor
  benchmark_gpu.py               GPU vs CPU surrogate benchmark

src/crq/          Python package
  ingest/         NMDB, USGS, SIDC, station-roster loaders
  preprocess/     Hodrick-Prescott and linear detrending
  stats/          Phase-randomisation / IAAFT surrogates (CPU + GPU)

results/          Generated outputs (committed)
  prereg_predictions.md          Pre-registration (timestamped before OOS run)
  data_availability.json         Reliable data window determination
  homola_replication.json        In-sample cross-correlation results
  detrended_results.json         Post-detrending results
  geo_localisation.json          Geographic localisation scan
  out_of_sample_metrics.json     OOS validation metrics (post-run)
  figs/                          Plots

config/
  stations.yaml   NMDB station list with coordinates
tests/            pytest suite (29 tests)

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Quickstart

# 1. Clone and install
git clone https://github.com/pingud98/cosmicraysandearthquakes.git
cd cosmicraysandearthquakes
python -m venv .venv && source .venv/bin/activate
pip install -e .

# 2. Download data (NMDB, USGS M≥4.5, SIDC sunspots)
python scripts/01_download_data.py

# 3. Run in-sample analysis
python scripts/02_homola_replication.py
python scripts/03_stress_test.py --n-surrogates 10000
python scripts/04_detrended_analysis.py

# 4. Geographic scan (GPU recommended)
python scripts/05_geographic_localisation.py --n-surrogates 1000

# 5. Check data availability for out-of-sample window
python scripts/06_check_data_availability.py

# 6. Pre-registered out-of-sample validation (writes prereg BEFORE analysis)
python scripts/07_out_of_sample.py --study-start 2020-01-01 --study-end 2025-04-29

# 7. Combined timeseries with sinusoid fit
python scripts/08_combined_timeseries.py

GPU (CUDA) is used automatically when available. Scripts fall back to CPU with a warning. The surrogate tests were benchmarked on a Tesla M40 (12 GB):

Method	CPU	GPU	Speedup
Phase randomisation	61.7 s	20.9 s	2.9×
IAAFT	227.8 s	175.6 s	1.3×

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Data sources

Source	Content	Access
NMDB	Hourly neutron monitor counts, pressure-corrected	Free, HTTP
USGS FDSN	M ≥ 4.5 global catalogue	Free, HTTP
SIDC SILSO	Daily international sunspot number	Free, HTTP

Data are downloaded by the scripts and cached locally in data/. No data files are committed to this repository.

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Pre-registration

results/prereg_predictions.md was committed to git before any out-of-sample data were loaded (UTC 2026-04-22T00:44:30, commit 1832f73). This prevents post-hoc hypothesis adjustment. Verify with:

git log --diff-filter=A results/prereg_predictions.md

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Statistical methods

• Surrogate test: Phase randomisation preserves the power spectrum of the cosmic-ray series; 100,000 surrogates give p-value resolution of 10⁻⁵.
• IAAFT: Iterated amplitude-adjusted FT surrogates (preserves amplitude distribution as well as power spectrum).
• Detrending: Hodrick-Prescott filter (λ = 1.29 × 10⁵) for in-sample window; linear detrending for OOS (< 1 solar cycle).
• FDR control: Benjamini-Hochberg at q = 0.05 for the geographic scan.
• Bayes factor: BIC approximation comparing sinusoidal vs constant model on the full 1976-to-present correlation timeseries.

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Requirements

• Python ≥ 3.10
• numpy, pandas, scipy, matplotlib, pyyaml, requests
• CuPy ≥ 12 (optional, for GPU acceleration)

Install: pip install -e .

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Citation

If you use this pipeline please cite:

Homola P. et al. (2023). Indication of Correlation between Cosmic-Ray Flux and Lightning Activity. Remote Sensing 15(1), 200. https://doi.org/10.3390/rs15010200

and link to this repository.

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Licence

MIT