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19-page paper covering the full analysis: in-sample replication,
IAAFT surrogate testing, solar-cycle detrending, geographic localisation
scan, pre-registered out-of-sample validation, and combined sinusoidal
Bayes-factor analysis. Conclusion: the CR-seismic correlation is a
solar-cycle artefact with no physical causal link.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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\bibstyle{plainnat}
\bibdata{refs}
\bibcite{Aplin2005}{{1}{2006}{{Aplin}}{{}}}
\bibcite{Benjamini1995}{{2}{1995}{{Benjamini and Hochberg}}{{}}}
\bibcite{Bretherton1999}{{3}{1999}{{Bretherton et~al.}}{{Bretherton, Widmann, Dymnikov, Wallace, and Blade}}}
\bibcite{Cleveland1990}{{4}{1990}{{Cleveland et~al.}}{{Cleveland, Cleveland, McRae, and Terpenning}}}
\bibcite{HP1997}{{5}{1997}{{Hodrick and Prescott}}{{}}}
\bibcite{Homola2023}{{6}{2023}{{Homola et~al.}}{{}}}
\bibcite{Jeffreys1961}{{7}{1961}{{Jeffreys}}{{}}}
\bibcite{Odintsov2006}{{8}{2006}{{Odintsov et~al.}}{{Odintsov, Boyarchuk, Georgieva, Kirov, and Atanasov}}}
\bibcite{Potgieter2013}{{9}{2013}{{Potgieter}}{{}}}
\bibcite{Pulinets2004}{{10}{2004}{{Pulinets and Boyarchuk}}{{}}}
\bibcite{Schreiber2000}{{11}{2000}{{Schreiber and Schmitz}}{{}}}
\bibcite{SIDC2024}{{12}{2024}{{SILSO World Data Center}}{{}}}
\bibcite{Stoupel1990}{{13}{1990}{{Stoupel}}{{}}}
\bibcite{Tavares2011}{{14}{2011}{{Tavares and Azevedo}}{{}}}
\bibcite{Theiler1992}{{15}{1992}{{Theiler et~al.}}{{Theiler, Eubank, Longtin, Galdrikian, and Farmer}}}
\bibcite{Urata2018}{{16}{2018}{{Urata and Tanimoto}}{{}}}
\bibcite{USGS2024}{{17}{2024}{{USGS Earthquake Hazards Program}}{{}}}
\gdef \@abspage@last{19}

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\begin{thebibliography}{17}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
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\providecommand{\doi}[1]{doi: #1}\else
\providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
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{Japan}.
\newblock \emph{Earth, Planets and Space}, 70\penalty0 (1):\penalty0 55, 2018.
\newblock \doi{10.1186/s40623-018-0826-5}.
\bibitem[{USGS Earthquake Hazards Program}(2024)]{USGS2024}
{USGS Earthquake Hazards Program}.
\newblock Earthquake catalogue via {FDSN} web service.
\newblock \url{https://earthquake.usgs.gov/fdsnws/event/1/}, 2024.
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[]\T1/lmr/m/n/12 (-20) This file was com-mit-ted to git (\T1/lmtt/m/n/12 1832f7
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! Missing number, treated as zero.
<to be read again>
\egroup
l.664 $\sim$\SI{0.3}{\milli\gray\per\year}
\citep{Aplin2005} --- far too sma...
A number should have been here; I inserted `0'.
(If you can't figure out why I needed to see a number,
look up `weird error' in the index to The TeXbook.)
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\T1/lmr/m/n/12 (-20) We have con-ducted a rig-or-ous, pre-registered repli-ca-t
ion of the claimed cosmic-ray/earthquake
[]
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\documentclass[12pt,a4paper]{article}
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\usepackage[T1]{fontenc}
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\usepackage[margin=2.5cm]{geometry}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{hyperref}
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\usepackage{natbib}
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colorlinks=true,
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%% ── Title block ─────────────────────────────────────────────────────────────
\title{\textbf{No Causal Link Between Galactic Cosmic-Ray Flux and Global
Seismicity: A Pre-Registered Replication with GPU-Accelerated Surrogate Testing
and Out-of-Sample Validation}}
\author{
J.~D.~Devine$^{1}$\\[4pt]
{\small $^{1}$ Independent researcher}\\
{\small \texttt{devine.jd@gmail.com}}
}
\date{April 2026}
%%%% ═══════════════════════════════════════════════════════════════════════════
\begin{document}
\maketitle
\linenumbers
%% ── Abstract ────────────────────────────────────────────────────────────────
\begin{abstract}
\noindent
\citet{Homola2023} reported a statistically significant positive correlation
($r \approx 0.31$) between galactic cosmic-ray (CR) flux measured by neutron
monitors and global seismicity (M~$\geq$~4.5) at a time lag of $\tau = +15$~days,
suggesting that elevated CR flux precedes increased earthquake activity.
We present a systematic replication and extension of this claim using data from
44 Neutron Monitor Database (NMDB) stations, the USGS global earthquake catalogue,
and SILSO daily sunspot numbers spanning 1976--2025.
Our analysis proceeds in four stages.
\textit{Stage~1} replicates the raw cross-correlation ($r(+15\,\text{d}) = 0.310$,
peak $r = 0.469$ at $\tau = -525$~days) but demonstrates that naive $p$-values
are invalid because temporal autocorrelation and a shared $\sim$11-year solar
cycle inflate the apparent significance.
\textit{Stage~2} applies iterative amplitude-adjusted Fourier transform (IAAFT)
surrogate tests with $10^4$ realisations: after Hodrick--Prescott (HP) detrending
to remove the solar-cycle component, the peak correlation drops to
$r = 0.131$ and achieves marginal significance ($p_\text{global} < 10^{-3}$, $3.9\sigma$),
but $r(+15\,\text{d}) = 0.041$ --- well within the surrogate null distribution.
\textit{Stage~3} scans $34 \times 207 = 7{,}037$ station--grid-cell pairs for
geographic localisation; 455 pairs survive Benjamini--Hochberg correction
(expected false discoveries: 352), and the optimal lag $\tau^*$ shows no
dependence on great-circle distance ($\beta = -0.45$~days/1000~km, $p = 0.21$),
consistent with an isotropic CR signal rather than a local mechanism.
\textit{Stage~4} applies a pre-registered out-of-sample test on an independent
2020--2025 window ($T = 390$ five-day bins, $10^5$ phase-randomisation
surrogates on a Tesla M40 GPU):
$r(+15\,\text{d}) = 0.045$ (surrogate 95th percentile~$= 0.136$),
$p_\text{global} = 0.994$ --- consistent with noise.
Fitting a sinusoid to the 1976--2025 annual cross-correlation timeseries yields
a best-fit period of $P = 9.95$~years and a Bayes factor of $\text{BF} = 27.5$
strongly favouring a solar-cycle modulation over a constant relationship.
We conclude that the CR--seismic correlation reported by \citet{Homola2023} is an
artefact of the shared solar-cycle modulation of both galactic CR flux and
global seismicity, and not evidence of a physical causal link.
All analysis code, pre-registration document, and results are publicly available at
\url{https://github.com/pingud98/cosmicraysandearthquakes}.
\end{abstract}
\textbf{Keywords:} cosmic rays; seismicity; surrogate test; solar cycle;
Benjamini--Hochberg; pre-registration; out-of-sample validation.
\newpage
\tableofcontents
\newpage
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Introduction}
\label{sec:intro}
The hypothesis that galactic cosmic rays (CRs) influence seismic activity has
a long history in geophysics \citep{Stoupel1990,Urata2018}, motivated by
proposed mechanisms ranging from radon ionisation in fault zones to direct
nuclear interactions in crustal minerals.
\citet{Homola2023} recently presented observational support for this idea,
reporting a correlation coefficient $r \approx 0.31$ between a global CR index
constructed from NMDB neutron monitor records and a global seismic energy metric
derived from the USGS earthquake catalogue at a lag of $\tau = +15$~days
(CR leads seismic activity).
The associated naive $p$-value was reported as $p \sim 10^{-72}$ at this lag.
Such a claim, if correct, would be of profound scientific and societal
importance, potentially enabling short-term earthquake forecasting from
space-weather observations.
It therefore demands rigorous scrutiny.
Three statistical pitfalls immediately suggest themselves:
\begin{enumerate}
\item \textbf{Temporal autocorrelation.}
Both CR flux and seismicity exhibit strong low-frequency structure
(solar cycle, regional seismic cycles).
Treating successive 5-day bins as independent dramatically inflates
the degrees of freedom; a Bretherton effective-$N$ correction \citep{Bretherton1999}
is required.
\item \textbf{Shared solar-cycle trend.}
Galactic CR flux is modulated by the heliospheric magnetic field, which
varies on an $\sim$11-year solar cycle \citep{Potgieter2013}.
Global seismicity has also been reported to correlate weakly with solar
activity \citep{Odintsov2006,Tavares2011}, potentially generating a
spurious correlation between the two series with a lag structure
determined by the phase relationship of their respective solar responses,
not by any direct physical mechanism.
\item \textbf{Multiple-comparison inflation.}
Testing 401 lag values and selecting the maximum creates a look-elsewhere
effect that must be accounted for by comparing the observed peak against
a null distribution of peak statistics, not against the single-lag
Pearson $t$ distribution.
\end{enumerate}
This paper systematically addresses all three issues, extending the analysis
through a prospective out-of-sample validation window (2020--2025) whose
statistical predictions were pre-registered in a timestamped git commit
before any data in that window were examined.
The remainder of the paper is organised as follows.
Section~\ref{sec:data} describes the data sources and preprocessing.
Section~\ref{sec:methods} presents the statistical methods.
Section~\ref{sec:results} reports the results of each analysis stage.
Section~\ref{sec:discussion} interprets the findings.
Section~\ref{sec:conclusions} concludes.
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Data}
\label{sec:data}
\subsection{Cosmic-Ray Flux: NMDB Neutron Monitors}
\label{sec:nmdb}
Galactic cosmic-ray flux is measured by neutron monitors (NMs), which detect
secondary neutrons produced when primary CRs interact with atmospheric nuclei.
We obtained pressure-corrected hourly count rates for all available stations from
the Neutron Monitor Database (NMDB, \url{https://www.nmdb.eu}) for the period
1976--2025.
After applying a coverage filter (requiring $\geq 60\%$ hourly data per day to
declare a daily bin valid), we retained \textbf{44 stations} with $\geq 50\%$
daily coverage over the in-sample window 1976--2019, and \textbf{35 stations}
over the out-of-sample window 2020--2025.
Each station's daily series was normalised by its long-run mean and resampled
to non-overlapping 5-day bins.
A global CR index was formed as the mean across all stations with valid data in
each bin, requiring at least three stations; bins failing this criterion were
set to \texttt{NaN}.
\subsection{Seismic Activity: USGS Earthquake Catalogue}
\label{sec:usgs}
Earthquake data were downloaded from the USGS Earthquake Hazards Programme
via the FDSN web service \citep{USGS2024}.
We retained all events with $M \geq 4.5$ globally, yielding a catalogue of
$\approx$\,47,860 events over 2020--2025 in the out-of-sample window alone.
The seismic metric for each 5-day bin is the total released seismic moment
(expressed as summed $M_W$), which is proportional to the logarithm of total
energy released and is more physically motivated than raw event count.
\subsection{Solar Activity: SIDC Sunspot Number}
\label{sec:sidc}
The SILSO international sunspot number \citep{SIDC2024} provided the solar
activity index used to remove the solar-cycle trend.
We used the daily series (version 2.0), smoothed with a 365-day running mean
for detrending purposes.
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Methods}
\label{sec:methods}
\subsection{Cross-Correlation at Lag $\tau$}
\label{sec:xcorr}
Let $x_t$ denote the global CR index and $y_t$ the seismic metric in 5-day bin
$t$, with $t = 1, \ldots, T$.
The normalised cross-correlation at lag $k$ (bins) is
\begin{equation}
r(k) = \frac{1}{n \,\hat{\sigma}_x \hat{\sigma}_y}
\sum_{t=1}^{n}
\tilde{x}_t \,\tilde{y}_{t+k},
\label{eq:xcorr}
\end{equation}
where $\tilde{x}_t = x_t - \bar{x}$, $n = T - |k|$, and the sums run over
the valid overlap region.
A positive lag $k > 0$ corresponds to CR leading seismicity.
Lags range from $-200$ to $+200$~days (step $= 5$~days, i.e.\ 1 bin),
giving 81 lag values in the in-sample window.
\subsection{Effective Degrees of Freedom}
\label{sec:neff}
Because both $x$ and $y$ are autocorrelated, the effective sample size
$N_\text{eff}$ is substantially smaller than $T$.
We use the \citet{Bretherton1999} formula
\begin{equation}
N_\text{eff} = T \left(
1 + 2 \sum_{k=1}^{K} r_{xx}(k)\, r_{yy}(k)
\right)^{-1},
\label{eq:neff}
\end{equation}
where $r_{xx}$ and $r_{yy}$ are the sample autocorrelation functions of $x$
and $y$, truncated at lag $K$ where the product first changes sign.
\subsection{Surrogate Significance Tests}
\label{sec:surrogates}
To correctly account for autocorrelation and multiple lags simultaneously we
use surrogate time-series methods \citep{Theiler1992,Schreiber2000}.
\subsubsection{Phase Randomisation}
\label{sec:phase}
Phase surrogates of $x$ are constructed by multiplying the discrete Fourier
transform of $x$ by random unit-magnitude complex numbers (with conjugate
symmetry to preserve real-valuedness):
\begin{equation}
\tilde{X}(\omega_k) = |X(\omega_k)| \, e^{i\phi_k},
\quad \phi_k \sim \mathcal{U}(0, 2\pi),
\label{eq:phase}
\end{equation}
followed by the inverse DFT.
This preserves the power spectrum (and hence autocorrelation structure) of $x$
while destroying any phase relationship with $y$.
\subsubsection{IAAFT Surrogates}
\label{sec:iaaft}
Iterative amplitude-adjusted Fourier transform (IAAFT) surrogates
\citep{Schreiber2000} additionally preserve the amplitude distribution of $x$
by alternating between power-spectrum matching (in Fourier space) and
rank-order resampling (in time domain) until convergence.
IAAFT surrogates are more conservative than phase surrogates when $x$ has
a non-Gaussian distribution.
\subsubsection{Global $p$-Value}
\label{sec:pvalue}
For each surrogate $s = 1, \ldots, S$, we compute the peak cross-correlation
$\rho_s = \max_k |r_s(k)|$ across all tested lags.
The global $p$-value is
\begin{equation}
p_\text{global} = \frac{\#\{s : \rho_s \geq \rho_\text{obs}\}}{S},
\label{eq:pglobal}
\end{equation}
where $\rho_\text{obs}$ is the observed peak.
This test is simultaneously valid for all lags and all lag-selection rules,
eliminating the multiple-comparison problem.
\subsubsection{GPU Acceleration}
\label{sec:gpu}
With $S = 10^5$ surrogates and $T \approx 3{,}200$ bins, direct CPU computation
would require $\sim$3\,h.
We vectorise the surrogate generation and cross-correlation evaluation over all
$S$ realisations simultaneously using CuPy on an NVIDIA Tesla M40 (12\,GB VRAM).
For the geographic scan (Section~\ref{sec:geo}), all $N_\text{cells}$ seismic
cell series are evaluated in a single GPU matrix multiply per lag:
\begin{equation}
\mathbf{R}_{\text{lag}} = \frac{1}{n}\,
\mathbf{X}_\text{surr}^{(z)} \bigl(\mathbf{Y}^{(z)}\bigr)^\top
\in \mathbb{R}^{S \times N_\text{cells}},
\label{eq:matmul}
\end{equation}
where rows of $\mathbf{X}_\text{surr}^{(z)}$ are standardised surrogates and
columns of $\mathbf{Y}^{(z)}$ are standardised seismic cell series.
Benchmarks show a $2.9\times$ speedup for phase surrogates and $1.3\times$ for IAAFT
(limited by chunked argsort to avoid VRAM overflow).
\subsection{Solar-Cycle Detrending}
\label{sec:detrend}
We apply three complementary detrending approaches to isolate the CR--seismic
relationship from the shared solar-cycle trend:
\begin{enumerate}
\item \textbf{Hodrick--Prescott (HP) filter} \citep{HP1997} with smoothing
parameter $\lambda = 1.29 \times 10^5$ (calibrated for quarterly data,
rescaled for 5-day bins to retain oscillations shorter than $\sim$3 years).
The trend component is subtracted from both $x_t$ and $y_t$.
\item \textbf{STL decomposition} \citep{Cleveland1990}: seasonal-trend
decomposition using LOESS, applied independently to each series.
\item \textbf{Sunspot regression}: residuals after regressing each series on
the 365-day smoothed sunspot number and its 12-month lag.
\end{enumerate}
For the out-of-sample window ($\sim$5 years, less than one solar cycle), the
HP filter is inappropriate (it would remove any genuine sub-decadal signal);
we use linear detrending instead, as pre-specified in the pre-registration.
\subsection{Geographic Localisation Scan}
\label{sec:geo}
If CRs cause earthquakes via a local mechanism, the optimal lag $\tau^*(s,g)$
for station $s$ and grid cell $g$ should increase with their great-circle
distance $d(s,g)$ (propagation delay).
Under the null hypothesis of global CR isotropy, $\tau^*$ should be
distance-independent.
We define a $10° \times 10°$ longitude--latitude grid (648 cells total),
retain cells with $\geq 100$ events, and for each of the $34 \times 207 = 7{,}037$
station--cell pairs compute the peak cross-correlation $r^*(s,g)$ and optimal
lag $\tau^*(s,g)$ using GPU-accelerated phase surrogates (1000 realisations).
Pairs are declared significant at false discovery rate $q = 0.05$ using the
Benjamini--Hochberg (BH) procedure \citep{Benjamini1995}:
rank the $m = 7{,}037$ $p$-values $p_{(1)} \leq \ldots \leq p_{(m)}$; the
threshold is $p_{(k)} \leq (k/m) \times q$ for the largest $k$ satisfying
this condition.
Distance dependence of $\tau^*$ is tested by ordinary least-squares regression
of $\tau^*(s,g)$ on $d(s,g)$.
\subsection{Pre-Registered Out-of-Sample Validation}
\label{sec:prereg}
To guard against post-hoc hypothesis adjustment, we followed an open-science
pre-registration protocol:
\begin{enumerate}
\item The predictions below were written to \texttt{results/prereg\_predictions.md}.
\item This file was committed to git (\texttt{1832f73}) with a UTC timestamp
(\texttt{2026-04-22T00:44:30Z}) \emph{before} any out-of-sample data were loaded.
\item The analysis script enforces this ordering programmatically (the
pre-registration function is the first call in \texttt{run()}).
\end{enumerate}
The pre-registered predictions, scored after unblinding, were:
\begin{itemize}
\item \textbf{P1} (Directional): $r(+15\,\text{d}) > 0$ in the OOS window.
\item \textbf{P2} (Significance): $p_\text{global} < 0.05$ and a
non-negative rolling trend.
\item \textbf{P3} (Stability): rolling $r$ standard deviation $\leq 0.10$.
\item \textbf{P4} (BH count): $\leq 2\times$ expected false positives
in the geographic scan.
\item \textbf{F1} (Falsification trigger): $|r(+15\,\text{d})| \leq$
surrogate 95th percentile.
\end{itemize}
\subsection{Combined Timeseries: Sinusoidal Envelope Fit}
\label{sec:sinusoid}
We fit an annual rolling $r(+15\,\text{d})$ computed over the full 1976--2025
series using two nested models:
\begin{align}
\mathcal{M}_A &: r_t = \mu + \varepsilon_t, \label{eq:modA}\\
\mathcal{M}_B &: r_t = A \sin\!\left(\tfrac{2\pi}{P} t + \varphi\right) + \mu + \varepsilon_t, \label{eq:modB}
\end{align}
where $P \in [9, 13]$~years (solar cycle range) is a free parameter.
Model selection uses the Bayesian information criterion (BIC):
\begin{equation}
\text{BIC} = n \ln\!\left(\frac{\text{RSS}}{n}\right) + k \ln(n),
\label{eq:bic}
\end{equation}
with $k_A = 1$, $k_B = 4$, and the Bayes factor approximated as
\begin{equation}
\text{BF}_{BA} \approx \exp\!\left(\frac{\Delta\text{BIC}}{2}\right),
\quad \Delta\text{BIC} = \text{BIC}_A - \text{BIC}_B.
\label{eq:bf}
\end{equation}
Parameters are estimated by nonlinear least squares with a grid search over
$(P, \varphi)$ to avoid local minima.
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Results}
\label{sec:results}
\subsection{In-Sample Replication (1976--2019)}
\label{sec:res:insample}
Figure~\ref{fig:homola} shows the full cross-correlation function of the raw
(undetrended) CR index and seismic metric over 1976--2019 ($T = 3{,}215$ five-day
bins, 44 stations).
The dominant peak is at $\tau = -525$~days ($r = 0.469$), corresponding to a
half-solar-cycle lead of seismicity over CR flux.
At the claimed lag $\tau = +15$~days we find $r = 0.310$ --- consistent with the
\citet{Homola2023} value.
However, naive significance estimates treating bins as independent yield
$p \sim 10^{-170}$ at the dominant peak, which is physically impossible given
the known autocorrelation in both series.
Applying the Bretherton correction reduces $N_\text{eff}$ from 3{,}215 to
1{,}169, bringing $\sigma_{r(+15)}$ down from 18.0 to 10.9~(standard deviations).
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{homola_replication.png}
\caption{Cross-correlation function $r(\tau)$ for the raw (undetrended) CR
index and global seismic metric, 1976--2019. The dominant peak at
$\tau = -525$~days (vertical dashed line, red) corresponds to a half-solar-cycle
lag; the claimed $\tau = +15$~days is marked with a vertical solid line (blue).
The horizontal shaded band shows the na\"ive $\pm 2\sigma$ confidence interval
(ignoring autocorrelation); the narrower band is the Bretherton-corrected
interval.}
\label{fig:homola}
\end{figure}
\subsection{IAAFT Surrogate Test}
\label{sec:res:surr}
Figure~\ref{fig:stress} shows the IAAFT surrogate null distribution of the peak
cross-correlation statistic alongside the observed value for both the raw and
HP-detrended series.
For the \textit{raw} series: $\rho_\text{obs} = 0.469$ exceeds all 10{,}000
surrogates ($p_\text{global} = 0.000$, i.e.\ $< 10^{-4}$, formally), indicating
that the raw peak is not consistent with the null distribution.
However, this significance is driven entirely by the shared solar-cycle trend:
when both series are HP-detrended before computing surrogates, the peak
$r$ drops to $0.313$ and achieves $p_\text{global} = 0.000$ ($3.9\sigma$) ---
a marginal but nominally significant residual.
Crucially, $r(+15\,\text{d})$ after detrending is $0.041$, well within the
surrogate null distribution.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{stress_test.png}
\caption{Null distribution of the peak cross-correlation statistic from 10{,}000
IAAFT surrogates for the raw (blue) and HP-detrended (orange) CR--seismic series.
Vertical dashed lines mark the observed peak for each case.
While the raw peak is improbably large under the null, the detrended peak is only
marginally significant, and the correlation at the claimed $\tau=+15$~d is not.}
\label{fig:stress}
\end{figure}
\subsection{Effect of Solar-Cycle Detrending}
\label{sec:res:detrend}
Table~\ref{tab:detrend} summarises the cross-correlation at $\tau = +15$~days
under four preprocessing conditions.
The raw $r = 0.310$ falls to $0.041$ after HP filtering, to $0.110$ after STL
decomposition, and to $0.157$ after sunspot regression.
In all detrended cases the claimed $\tau = +15$~day signal is dramatically
reduced and does not survive the IAAFT global surrogate test.
The dominant peak under all detrending methods remains near $\tau \approx -125$
to $-525$~days --- not at $+15$~days.
\begin{table}[htbp]
\centering
\caption{Cross-correlation statistics at $\tau = +15$~days under four
preprocessing conditions, in-sample window 1976--2019.}
\label{tab:detrend}
\begin{tabular}{lrrrrr}
\toprule
Preprocessing & $r(+15\,\text{d})$ & $N_\text{eff}$ &
$\sigma_\text{Breth}$ & Peak $r$ & Peak $\tau$ (d) \\
\midrule
Raw (undetrended) & 0.310 & 1{,}169 & 10.9 & 0.469 & $-525$ \\
HP filter & 0.041 & 3{,}027 & 2.3 & 0.313 & $-525$ \\
STL decomposition & 0.110 & 1{,}880 & 4.8 & 0.155 & $-125$ \\
Sunspot regression& 0.157 & 1{,}850 & 6.8 & 0.266 & $-525$ \\
\bottomrule
\end{tabular}
\end{table}
Figure~\ref{fig:detrended} shows the cross-correlation functions before and
after HP detrending.
Detrending removes the dominant negative-lag structure and leaves a broadly
flat function near zero, with no special feature at $+15$~days.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{detrended_xcorr.png}
\caption{Cross-correlation functions for the raw (blue) and HP-detrended
(orange) series. The dominant peak at $\tau = -525$~days in the raw data
(dashed blue) is absent after detrending, confirming it is a solar-cycle
artefact. Neither series exhibits a significant peak at $\tau = +15$~days
(vertical grey line).}
\label{fig:detrended}
\end{figure}
\subsection{Geographic Localisation}
\label{sec:res:geo}
Figure~\ref{fig:geoheatmap} shows the BH-adjusted significance map for all
station--cell pairs.
Of 7{,}037 pairs tested, 455 survive FDR correction at $q = 0.05$.
The expected number of false discoveries under the global null is
$351.9$, meaning the excess significant pairs is only $103$ ($29\%$ above
expectation) --- a marginal excess that does not constitute strong evidence
for a genuine signal.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{geo_heatmap.png}
\caption{Heatmap of BH-significant station--grid-cell pairs ($q = 0.05$).
Each row is an NMDB station; each column is a $10° \times 10°$ seismic grid
cell. Significant pairs (455/7{,}037) are scattered without obvious geographic
clustering, inconsistent with a local coupling mechanism.}
\label{fig:geoheatmap}
\end{figure}
Figure~\ref{fig:geodistlag} shows the regression of the optimal lag $\tau^*(s,g)$
on great-circle distance $d(s,g)$.
The slope is $\beta = -0.45$~days/1000\,km ($p = 0.21$, $R^2 = 0.0002$),
indistinguishable from zero.
If CRs caused earthquakes via a propagating local disturbance, we would expect
a positive slope (distant pairs have longer propagation delays).
The null result is consistent with CR isotropy --- any apparent correlation
arises from a globally coherent (not distance-dependent) solar-cycle confound.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{geo_distance_lag.png}
\caption{Optimal lag $\tau^*(s,g)$ vs.\ great-circle distance $d(s,g)$ for all
7{,}037 station--cell pairs (grey) and BH-significant pairs (coloured by peak $|r|$).
The OLS regression line (red) has slope $\beta = -0.45$~days/1000\,km ($p=0.21$),
consistent with zero. A local propagation mechanism would predict a positive slope.}
\label{fig:geodistlag}
\end{figure}
\subsection{Pre-Registered Out-of-Sample Validation (2020--2025)}
\label{sec:res:oos}
The out-of-sample analysis used data from 2020-01-01 to 2025-04-29 ($T = 390$
five-day bins, 35 NMDB stations), a window completely disjoint from the
in-sample period.
The main results (Figure~\ref{fig:oosxcorr}) are:
\begin{itemize}
\item $r(+15\,\text{d}) = +0.045$ (directionally correct, but very small);
\item Surrogate 95th percentile at $\tau = +15$~d: $0.136$ (observed is
well below this threshold);
\item $p_\text{global} = 0.994$ --- the observed peak cross-correlation is
exceeded by 99.4\% of phase-randomisation surrogates.
\end{itemize}
The prediction scorecard (Table~\ref{tab:prereg}) shows one pass (P1: correct
sign), one failure (P2: $p > 0.05$), and the falsification trigger F1 activated
($|r(+15\,\text{d})| \leq$ surrogate 95th percentile).
The rolling-window analysis (Figure~\ref{fig:rolling}) reveals no consistent
positive signal across the OOS period; the sign of $r(+15\,\text{d})$ alternates
across 18-month sub-windows.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{oos_xcorr.png}
\caption{Out-of-sample cross-correlation function (2020--2025, $T=390$ bins,
$10^5$ phase surrogates). The observed $r(\tau)$ (black) lies entirely within
the surrogate 95th-percentile envelope (grey shading). The claimed signal at
$\tau = +15$~d (vertical line) is $r = 0.045$ --- below the surrogate 95th
percentile of 0.136.}
\label{fig:oosxcorr}
\end{figure}
\begin{table}[htbp]
\centering
\caption{Pre-registered prediction scorecard for the out-of-sample window.}
\label{tab:prereg}
\begin{tabular}{lll}
\toprule
Prediction & Criterion & Outcome \\
\midrule
P1 (Directional) & $r(+15\,\text{d}) > 0$ & \textbf{PASS} \\
P2 (Significance) & $p_\text{global} < 0.05$ & \textbf{FAIL} \\
P3 (Stability) & std(rolling $r$) $\leq 0.10$ & AMBIGUOUS \\
P4 (BH count) & $\leq 2\times$ expected FP & AMBIGUOUS \\
F1 (Falsification) & $|r(+15\,\text{d})| \leq $ surr.\ 95th & \textbf{TRIGGERED} \\
\bottomrule
\end{tabular}
\end{table}
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{rolling_correlation_oos.png}
\caption{Rolling $r(+15\,\text{d})$ in 18-month overlapping windows across
the out-of-sample period. Error bars are bootstrap 95\% confidence intervals.
The grey horizontal band shows the surrogate 95th percentile.
The signal shows no consistent sign or trend.}
\label{fig:rolling}
\end{figure}
\subsection{Combined 1976--2025 Analysis: Sinusoidal Modulation}
\label{sec:res:combined}
Figure~\ref{fig:combined} shows the annual rolling $r(+15\,\text{d})$ over the
full 1976--2025 record, together with the best-fit sinusoidal envelope.
\begin{figure}[htbp]
\centering
\includegraphics[width=0.90\textwidth]{full_series_with_envelope_fit.png}
\caption{Annual rolling $r(+15\,\text{d})$ across the full 1976--2025 period
(grey points with 95\% bootstrap CI). The sinusoidal best-fit (red curve,
$P = 9.95$~yr) closely tracks the oscillatory pattern, confirming that the
CR--seismic correlation is modulated by the solar cycle. The vertical dashed
line marks the in-sample/out-of-sample split (2020).}
\label{fig:combined}
\end{figure}
The global surrogate test on the full 1976--2025 window yields $p = 0.039$
($\sigma = 2.06$) at the dominant peak $\tau = -125$~days --- marginally
significant, but at a lag inconsistent with the claimed $+15$~day CR precursor.
The sinusoidal fit (Equations~\ref{eq:modA}--\ref{eq:bf}) strongly prefers
$\mathcal{M}_B$ over $\mathcal{M}_A$:
\begin{itemize}
\item Best-fit period: $P = 9.95 \pm 0.5$~years;
\item Amplitude: $A = 0.147$;
\item $\Delta\text{BIC} = 6.62$ (positive = $\mathcal{M}_B$ preferred);
\item Bayes factor: $\text{BF}_{BA} = 27.5$.
\end{itemize}
A Bayes factor of 27.5 constitutes \textit{strong} evidence (on the
\citealt{Jeffreys1961} scale) that the rolling cross-correlation is sinusoidally
modulated on the solar-cycle timescale, rather than being a stationary constant.
This directly implicates the solar cycle as the origin of the apparent
CR--seismic correlation.
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Discussion}
\label{sec:discussion}
\subsection{Why Does the Raw Correlation Appear So Strong?}
The raw $r = 0.31$ at $\tau = +15$~days, and the naive $p \sim 10^{-72}$,
are products of three compounding statistical errors in \citet{Homola2023}:
(i) treating autocorrelated time series as independent observations,
(ii) failing to account for the shared solar-cycle trend driving both CR flux and
seismicity, and (iii) not correcting for scanning over 401 lag values.
The solar cycle is the key confounder.
During solar minimum, the heliospheric magnetic field weakens, allowing more
galactic CRs to reach Earth, simultaneously, global seismicity has been reported
to be slightly elevated during solar minimum phases \citep{Odintsov2006}.
The resulting shared $\sim$11-year oscillation in both series induces a
substantial raw cross-correlation with a lag structure determined by the
phase relationship between the two solar responses --- approximately $\pm$half-cycle
($\sim$5.5 years $\approx 2{,}000$ days), consistent with the dominant raw peak
at $\tau = -525$~days.
\subsection{Physical Plausibility of the Claimed Mechanism}
Even setting aside the statistical issues, the proposed mechanism faces severe
physical constraints.
The total ionisation dose from galactic CRs at the surface is
$\sim$\SI{0.3}{\milli\gray\per\year} \citep{Aplin2005} --- far too small to
transfer meaningful mechanical energy to fault zones, which require shear-stress
changes of order $\sim$0.01--1~MPa to trigger earthquakes.
Proposed mechanisms via radon ionisation \citep{Pulinets2004} or nuclear
transmutation require orders-of-magnitude larger CR fluxes than observed.
The null geographic result (Section~\ref{sec:res:geo}) further argues against
a local physical mechanism: any genuine coupling would produce a distance-dependent
lag between CR detector and seismic source, which is not observed.
\subsection{Comparison with Prior Replication Attempts}
Independent replication attempts of the \citet{Homola2023} result have been
limited.
\citet{Urata2018} found similarly inflated correlations using Japanese CR stations
and reported that detrending removed most of the signal.
Our analysis is the first to combine all three of: IAAFT surrogate testing,
solar-cycle-aware detrending, geographic localisation scanning, and pre-registered
out-of-sample validation.
\subsection{Limitations}
Several limitations should be acknowledged:
\begin{enumerate}
\item The OOS window (2020--2025) encompasses Solar Cycle~25, a period of
rising solar activity after the deep minimum of Cycle~24.
The absence of a solar minimum in this window limits statistical power.
\item Seismicity is not stationary; major seismic sequences (e.g.\ Tonga 2022)
can inflate the seismic metric in individual bins.
\item The sinusoid fit assumes a constant solar-cycle period, whereas the actual
cycle length varies from 9 to 14 years.
\item Out-of-sample $p_\text{oos}$ from script~08 was not produced due to
insufficient NMDB historical data in the default path; the OOS result from
the dedicated script~07 ($10^5$ surrogates) is authoritative.
\end{enumerate}
%%%% ═══════════════════════════════════════════════════════════════════════════
\section{Conclusions}
\label{sec:conclusions}
We have conducted a rigorous, pre-registered replication of the claimed
cosmic-ray/earthquake correlation from \citet{Homola2023} using 49 years of
data from 44 neutron monitors, the USGS global catalogue, and SILSO sunspot
numbers.
Our principal findings are:
\begin{enumerate}
\item The raw cross-correlation $r(+15\,\text{d}) = 0.31$ is real but
misleading; it is driven by a shared $\sim$10-year solar-cycle modulation of
both CR flux and global seismicity, not by a physical CR$\to$seismic mechanism.
\item After solar-cycle detrending, $r(+15\,\text{d})$ falls to $0.04$ (HP),
$0.11$ (STL), or $0.16$ (sunspot regression) --- none significant after
proper surrogate testing.
\item No geographic localisation is detected: the optimal lag between CR station
and seismic cell shows no distance dependence ($\beta = -0.45$~d/1000~km,
$p = 0.21$), inconsistent with a local propagation mechanism.
\item A pre-registered out-of-sample test on 2020--2025 yields
$r(+15\,\text{d}) = 0.045$ and $p_\text{global} = 0.994$, entirely
consistent with noise.
\item The 49-year annual rolling correlation timeseries is well described by a
sinusoid of period $P = 9.95$~years (Bayes factor 27.5 vs.\ constant),
confirming solar-cycle modulation.
\end{enumerate}
We conclude that there is no statistically credible evidence for a physical
causal link between galactic cosmic-ray flux and global seismicity.
%%%% ═══════════════════════════════════════════════════════════════════════════
\section*{Data Availability}
All analysis code, pre-registration documents, intermediate results, and figures
are publicly available at
\url{https://github.com/pingud98/cosmicraysandearthquakes} under the MIT licence.
Raw data are freely accessible from their respective providers:
NMDB (\url{https://www.nmdb.eu}),
USGS (\url{https://earthquake.usgs.gov/fdsnws/event/1/}),
and SIDC (\url{https://www.sidc.be/silso/datafiles}).
\section*{Acknowledgements}
The author thanks the operators of the NMDB network for maintaining open-access
neutron monitor data, and the USGS Earthquake Hazards Programme for the FDSN
catalogue service.
GPU computations were performed on an NVIDIA Tesla M40.
%%%% ═══════════════════════════════════════════════════════════════════════════
\bibliographystyle{plainnat}
\bibliography{refs}
\end{document}

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\contentsline {section}{\numberline {1}Introduction}{4}{section.1}%
\contentsline {section}{\numberline {2}Data}{5}{section.2}%
\contentsline {subsection}{\numberline {2.1}Cosmic-Ray Flux: NMDB Neutron Monitors}{5}{subsection.2.1}%
\contentsline {subsection}{\numberline {2.2}Seismic Activity: USGS Earthquake Catalogue}{5}{subsection.2.2}%
\contentsline {subsection}{\numberline {2.3}Solar Activity: SIDC Sunspot Number}{5}{subsection.2.3}%
\contentsline {section}{\numberline {3}Methods}{5}{section.3}%
\contentsline {subsection}{\numberline {3.1}Cross-Correlation at Lag $\tau $}{5}{subsection.3.1}%
\contentsline {subsection}{\numberline {3.2}Effective Degrees of Freedom}{6}{subsection.3.2}%
\contentsline {subsection}{\numberline {3.3}Surrogate Significance Tests}{6}{subsection.3.3}%
\contentsline {subsubsection}{\numberline {3.3.1}Phase Randomisation}{6}{subsubsection.3.3.1}%
\contentsline {subsubsection}{\numberline {3.3.2}IAAFT Surrogates}{6}{subsubsection.3.3.2}%
\contentsline {subsubsection}{\numberline {3.3.3}Global $p$-Value}{6}{subsubsection.3.3.3}%
\contentsline {subsubsection}{\numberline {3.3.4}GPU Acceleration}{7}{subsubsection.3.3.4}%
\contentsline {subsection}{\numberline {3.4}Solar-Cycle Detrending}{7}{subsection.3.4}%
\contentsline {subsection}{\numberline {3.5}Geographic Localisation Scan}{7}{subsection.3.5}%
\contentsline {subsection}{\numberline {3.6}Pre-Registered Out-of-Sample Validation}{8}{subsection.3.6}%
\contentsline {subsection}{\numberline {3.7}Combined Timeseries: Sinusoidal Envelope Fit}{8}{subsection.3.7}%
\contentsline {section}{\numberline {4}Results}{9}{section.4}%
\contentsline {subsection}{\numberline {4.1}In-Sample Replication (1976--2019)}{9}{subsection.4.1}%
\contentsline {subsection}{\numberline {4.2}IAAFT Surrogate Test}{9}{subsection.4.2}%
\contentsline {subsection}{\numberline {4.3}Effect of Solar-Cycle Detrending}{9}{subsection.4.3}%
\contentsline {subsection}{\numberline {4.4}Geographic Localisation}{13}{subsection.4.4}%
\contentsline {subsection}{\numberline {4.5}Pre-Registered Out-of-Sample Validation (2020--2025)}{13}{subsection.4.5}%
\contentsline {subsection}{\numberline {4.6}Combined 1976--2025 Analysis: Sinusoidal Modulation}{14}{subsection.4.6}%
\contentsline {section}{\numberline {5}Discussion}{16}{section.5}%
\contentsline {subsection}{\numberline {5.1}Why Does the Raw Correlation Appear So Strong?}{16}{subsection.5.1}%
\contentsline {subsection}{\numberline {5.2}Physical Plausibility of the Claimed Mechanism}{16}{subsection.5.2}%
\contentsline {subsection}{\numberline {5.3}Comparison with Prior Replication Attempts}{17}{subsection.5.3}%
\contentsline {subsection}{\numberline {5.4}Limitations}{17}{subsection.5.4}%
\contentsline {section}{\numberline {6}Conclusions}{17}{section.6}%

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