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In passive-source seismic exploration, even after seismic instruments complete unified start-up acquisition and hardware synchronization, long-duration continuous records may still contain small residual timing errors, which in turn broaden cross-correlation peaks and degrade event-location results. To address this problem, this study proposes a wavefield-domain residual timing refinement method. The method uses stable noise windows and controlled artificial events in continuous records as constraints, and performs data-window preprocessing, reference cross-correlation function construction, pairwise residual lag estimation, confidence-weighted multi-station joint fusion, and smoothing-constrained fitting of a continuous correction curve to achieve a posterior refinement of residual timing errors after hardware synchronization. Fractional-delay interpolation is then used for waveform correction. Validation using a 60 min continuous record from a local six-station array shows that the proposed method can serve as an effective supplement to hardware synchronization, suppress residual timing errors, and improve the temporal consistency, waveform stackability, and interpretation reliability of passive-source seismic exploration data.

Background A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorithms known as colocalization. However, the most commonly used methods of colocalization have strict limitations, such as requiring overlap in the fluorescent markers and reporting requirements for accurate interpretation of the data, that are often not met. Due to the development of novel super-resolution techniques, which reduce the overlap of the fluorescent signals, a new colocalization method is needed that does not have such strict requirements. Results In order to overcome the limitations of other colocalization algorithms, I developed a new ImageJ/Fiji plugin, Colocalization by cross-correlation (CCC). This method uses cross-correlation over space to identify spatial correlations as a function of distance, removing the overlap requirement and providing more comprehensive results. CCC is compatible with 3D and time-lapse images, and was designed to be easy to use. CCC also generates new images that only show the correlating labeled structures from the input images, a novel feature among the cross-correlating algorithms. Conclusions CCC is a versatile, powerful, and easy to use colocalization and spatial correlation tool that is available through the Fiji update sites. Full and up to date documentation can be found at https://imagej.net/plugins/colocalization-by-cross-correlation . CCC source code is available at https://github.com/andmccall/Colocalization_by_Cross_Correlation .

Several studies have been conducted on droughts, precipitation, and temperature, whereas none have addressed the underlying relationship between nonlinear dynamic properties and patterns of two main hydrological parameters, precipitation and temperature, and meteorological and hydrological droughts. Monthly datasets of Midlands in the UK between 1921 and 2019 were collected for analysis. Subsequent to apply a multifractal approach to attain the nonlinear features of the datasets, the relationship between two hydrological parameters and droughts was investigated through the cross-correlation technique. A similar process was performed to analyze the relationship between multifractal strength variations in time series of precipitation and temperature and droughts. The nonlinear dynamic results indicated that droughts (meteorological and hydrological) were substantially affected by precipitation than temperature. In other words, droughts were more sensitive to precipitation fluctuations than temperature fluctuations. Concerning temperature, meteorological, and hydrological droughts were dependent on the minimum and maximum temperatures (Tmin and Tmax), respectively. The correlation between precipitation and meteorological drought was more long-range persistence than precipitation and hydrological drought. Besides, the correlation between Tmax and droughts was more long-range persistence than Tmin and droughts. Analysis of nonlinear dynamic patterns proved that the multifractal strength of meteorological drought depended on the multifractal strength of precipitation and Tmax, whereas the multifractal strength of hydrological drought depended on the multifractal strength of the Tmin. The correlation between precipitation and drought indices exhibited more multifractal strength than temperature and drought indices. Finally, the pivotal role of maximum temperature on drought events was quite alerting due to global warming intensification.

Conventional tsunami simulations rely on accurate bathymetric data, posing challenges in regions lacking such information. We introduce a novel approach using ambient noise interferometry to derive empirical Green's functions of infragravity waves from noise correlation functions (NCFs) extracted from a 10‐year Deep‐ocean Assessment and Reporting of Tsunamis data set in the Pacific Ocean. Our analysis reveals pronounced propagating behavior in NCFs, indicative of wave dispersion relationships. Long‐period NCFs align with shallow‐water wave dynamics, making them suitable for tsunami simulations. By eliminating the need for precise bathymetry, our method offers a practical solution for data‐sparse regions. A case study of an Alaska tsunami demonstrates our NCFs effectively fit observed pressure data, outperforming conventional Cornell Multi‐Grid Coupled Tsunami Model simulations. The fidelity of our results underscores the potential of ambient noise interferometry‐derived NCFs to enhance tsunami predictions, even in complex environments. Our findings advance tsunami research and have significant implications for disaster preparedness and mitigation. Plain Language Summary Predicting tsunamis accurately is vital for disaster preparedness, but traditional methods require detailed maps of the ocean floor, which aren't always available. In our study, we developed a new approach that doesn't rely on precise bathymetric data. We used ambient noise interferometry to analyze background ocean noise recorded over 10 years by the Deep‐ocean Assessment and Reporting of Tsunamis system in the Pacific Ocean. From this data, we derived empirical Green's functions of infragravity waves (IGWs), which help us understand how tsunami waves propagate. Our analysis revealed that these IGWs, especially for long‐period waves, mimic the behavior of tsunami waves in shallow water. This makes them suitable for simulating tsunamis without needing detailed information about the seafloor. We tested our method on a real tsunami event in Alaska and found that our predictions matched the observed pressure data better than traditional simulations using Cornell Multi‐Grid Coupled Tsunami Model. By eliminating the need for precise bathymetric data, our method offers a practical solution for regions lacking such information. This approach could improve tsunami predictions even in complex coastal environments, enhancing disaster preparedness, and potentially saving lives. Key Points Novel method uses ambient noise to derive empirical Green's functions of infragravity waves, eliminating the need for accurate bathymetric data Long‐period noise correlation functions (NCFs) align with shallow‐water dynamics, making them suitable for tsunami simulations In Alaska case study, NCFs fit observed data better than Cornell Multi‐Grid Coupled Tsunami Model, enhancing tsunami prediction accuracy

Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020–October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008).

Debris flows radiate both seismic and infrasonic waves. According to previous studies, seismic energy is generated by the solid particle collisions with the riverbed and is dominated by the boulder‐rich front, while infrasound is produced by turbulence‐induced waves at the flow surface. To further investigate this complex radiation processes, we present the seismo‐acoustic analysis of a debris‐flow event at Illgraben (Switzerland). Array processing shows that infrasound is preferentially radiated at channel irregularities, acting as predominant acoustic sources because of the intense flow turbulence. The high crosscorrelation observed between the recorded infrasonic and seismic signals suggests that, in addition to the dominant source related to particle impacts, a minor seismic component is produced by the flow waves developing at topographic steps. Plain Language Summary Debris flows represent a major hazard in mountain environments. While flowing, debris flows generate both seismic waves and infrasound (low‐frequency sound). According to previous studies, the seismic waves are produced by the collision between the transported debris and the riverbed, while infrasound is generated by the flow waves developing at its surface. Here we present a combined analysis of the infrasonic and seismic signals generated by a debris‐flow event at Illgraben (Switzerland). The analysis reveals that the infrasound is preferentially produced when the flow overruns the check dams and flow waves develop downstream. These latter also produce seismic waves, which, despite being weaker, add to the dominant seismic component produced by particle collisions. Eventually we show how seismo‐acoustic signals can be used to track the debris flow along the channel. Key Points Infrasound by debris flows is radiated by turbulence‐induced waves at flow surface mostly generated at significant channel irregularities Seismo‐acoustic cross‐correlation suggests that, in addition to particle collisions, turbulent flow radiates seismic waves in debris flows Infrasonic array processing and seismo‐acoustic cross‐correlation analysis can be used to track and detect debris flows

In this paper, the Spectral Amplitude Coding-Optical Code Division Multiple Access (SAC-OCDMA) system performance is investigated both in Free Space Optics (FSO) and Optical Fiber Systems (OFS) scenarios focusing on different types of codes. The codes having low cross correlation such as Random Diagonal (RD) and Khazani-Syed (KS), and zero cross correlation such as Zero Cross Correlation (ZCC) and Multi-Diagonal (MD) are used. In the FSO scenario, moderate turbulence and hazy weather conditions are considered. In the OFS scenario, nonlinear effects, attenuation, and dispersion are taken into consideration. Also, the performance of various codes is evaluated under different rain attenuation conditions. The simulation results show that the SAC-OCDMA codes performance is media-dependent. The ZCC codes give better performance than low cross correlation codes in both FSO and OFS scenarios. The MD code gives the best code performance in both scenarios with the ZCC code following. The RD code provides better performance than the KS code in the OFS scenario. However, in the FSO scenario, the KS code performs better than the RD code under the turbulence effect. Furthermore, the MD code performance under different bit rates is investigated to support tri-play services. Its security performance is also investigated.