Magnitude Time Series Research Articles

Identifying the Occurrence Time of the Destructive Kahramanmaraş-Gazientep Earthquake of Magnitude M7.8 in Turkey on 6 February 2023

Here, we employ natural time analysis of seismicity together with non-extensive statistical mechanics aiming at shortening the occurrence time window of the Kahramanmaraş-Gazientep M7.8 earthquake. The results obtained are in the positive direction pointing to the fact that after 3 February 2023 at 11:05:58 UTC, a strong earthquake was imminent. Natural time analysis also reveals a minimum fluctuation of the order parameter of seismicity almost three and a half months before the M7.8 earthquake, pointing to the initiation of seismic electrical activity. Moreover, before this earthquake occurrence, the detrended fluctuation analysis of the earthquake magnitude time-series reveals random behavior. Finally, when applying earthquake nowcasting, we find average earthquake potential score values which are compatible with those previously observed before strong (M≥7.1) earthquakes. The results obtained may improve our understanding of the physics of crustal phenomena that lead to strong earthquakes.

Thermal pattern of Tatun volcanic system by satellite-observed temperatures and its correlation with earthquake magnitudes

The land surface temperature (LST) of volcanoes detected from satellite sensors reflects the thermal status of heat sources in the subsurface. Volcanic earthquakes occur as magma and volcanic fluids transport to the surface from depth. Thus, both LST and earthquake magnitude are key parameters to the study of active volcanoes. Here we investigate the volcanic status of Tatun Volcanic Group (TVG) based on LST and seismic observations. The Earth-observing satellites onboard thermal sensor derived land surface temperature, and the seismic records retrieved volcanic earthquake magnitude are used to delineate the past and current pattern of volcanic activity plus the future trend of the TVG. The spatiotemporal distribution of LST and volcanic earthquake magnitude in TVG are analyzed. The high-similarity trends of the 4-decade LST time series and 3-decade earthquake magnitude time series are inspected. The retrieved surface thermal pattern shows the non-steady-state nature of the subsurface thermal sources at this volcanic complex. The LST trend exhibits a rather positive correlation with the energy released from volcanic earthquakes and consequently, the presumption on the connection between LSTs and earthquakes is validated.

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers.

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.

Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression

IntroductionFunctional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Laminar fMRI studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined if the feasibility of laminar fMRI at 3T could be improved by use of NORDIC denoising and phase regression. Methods5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3–8 sessions on 3–4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block design finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in temporal signal-to-noise ratio (tSNR) and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression. Results and conclusionNORDIC denoising resulted in tSNR values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support an improved feasibility of laminar fMRI at 3T.

Chromospheric activity and photospheric variation of α Ori during the great dimming event in 2020

Aims. The so-called great dimming event of α Ori in late 2019 and early 2020 sparked our interest in the behaviour of chromospheric activity during this period. α Ori was already part of the long-term monitoring program of our TIGRE telescope to study the stellar activity of giant stars, and therefore regular measurements of α Ori have been taken since 2013. Methods. In the context of this study, we determined the TIGRE S -index values and, using a set of calibration stars, converted these to the Mount Wilson S -index scale, which allows us to combine our TIGRE activity measurements with the SMWO values taken during the landmark Mount Wilson program some decades earlier and to compare that extended time series with the visual and V magnitude photometric data from the AAVSO database. In addition, we determined the absolute and normalised excess flux of the Ca II H&K lines. To understand the activity in absolute terms, we also assessed the changes in effective temperature using the TiO bands covered by our TIGRE spectra. Results. We find a clear drop in effective temperature by about 80 K between November 2019 and February 2020, which coincides with the minimum of visual brightness. In addition, the effective (luminous) photospheric area of α Ori also shrank. This might be related to a temporary synchronisation of several large convective cells in cooling and sinking down. During the same period, the S-index increased significantly, yet this is a mere contrast effect, because the normalised excess flux of the Ca II H&K lines did not change significantly. However, the latter dropped immediately after this episode. Comparing the combined SMWO values and visual magnitude time series, we find a similar increase in the S -index during another noticeable decrease in the visual magnitude of α Ori which took place in 1984 and 1985. These two episodes of dimming therefore seem to share a common nature. To probe the dynamics of the upper photosphere, we further analysed the closely neighbouring lines of V I and Fe I at 6251.82 and 6251.56 Å respectively. Remarkably, their core distance varies, and once converted to radial velocity, shows a relation with the great dimming event, as well as with the consecutive, weaker dimming episode in the observing season of 2020 and 2021. This type of variation could be caused by rising and sinking cool plumes as a temporary spill-over of convection on α Ori. Conclusions. As the effective temperature of α Ori is variable, the S -index, computed relative to a near-ultraviolet (NUV) continuum, is only of restricted use for any monitoring study of the chromospheric activity of α Ori. It is therefore important to consider the effective temperature variability and derive the normalised Ca II H&K flux to study the chromospheric long-term changes in absolute terms. In fact, the Ca II H&K normalised excess flux time series shows that the chromospheric emission of α Ori did not change significantly between November 2019 and February 2020, but then beyond the great dimming minimum it does vary. Hence, this delay of the chromospheric reaction suggests that the cause for the great dimming is located in the photosphere. An investigation of the long-term spectroscopic and photometric time series of α Ori suggests that the great dimming in 2019 and 2020 does not appear to be a unique phenomenon, but rather that such dimmings do occur more frequently, which motivates further monitoring of α Ori with facilities such as TIGRE.

Investigation of long memory in concrete fracture through acoustic emission time series analysis under monotonic and fatigue loading

The significant dependence of current events on past events is known as a long memory phenomenon in stochastic processes. The fracture process in concrete is interesting due to its complexity arising from long-range interactions of multi-scale cracks which may manifest in the long memory. We quantify the long memory effect in the concrete fracture process using the Hurst exponent for acoustic emission measurements. The magnitude and inter-event time series are studied for three different beam sizes under monotonic and fatigue loading. The long memory is strongly observed under fatigue loading. Local variations in the Hurst exponent reveal the fundamental difference between monotonic and fatigue loading, which can be used for damage detection in concrete.

Natural Time Analysis of Global Seismicity

Natural time analysis enables the introduction of an order parameter for seismicity, which is just the variance of natural time χ, κ1=⟨χ2⟩−⟨χ⟩2. During the last years, there has been significant progress in the natural time analysis of seismicity. Milestones in this progress are the identification of clearly distiguishable minima of the fluctuations of the order parameter κ1 of seismicity both in the regional and global scale, the emergence of an interrelation between the time correlations of the earthquake (EQ) magnitude time series and these minima, and the introduction by Turcotte, Rundle and coworkers of EQ nowcasting. Here, we apply all these recent advances in the global seismicity by employing the Global Centroid Moment Tensor (GCMT) catalog. We show that the combination of the above three milestones may provide useful precursory information for the time of occurrence and epicenter location of strong EQs with M≥8.5 in GCMT. This can be achieved with high statistical significance (p-values of the order of 10−5), while the epicentral areas lie within a region covering only 4% of that investigated.

Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine

AbstractSpring phytoplankton blooms in the Gulf of Maine (GoM) are sensitive to climate‐related local and remote forcing. Nutrient supply through the slope water intrusion has been viewed as critical in regulating the GoM spring blooms, with an assumption that nitrogen is the primary limiting nutrient. In recent years, this paradigm has been challenged, with silicate being recognized as another potential limiting nutrient, but the source of silicate and its associated water mass remain difficult to be determined. In this study, a time series of spring bloom magnitude was constructed using a self‐organizing map algorithm, and then correlated with the fluctuation of water composition in the deep Northeast Channel. The results reveal the importance of silicate supply from previously less‐recognized deep Scotian Shelf Water inflow. This study offers a new hypothesis for spring bloom regulation, providing a better understanding of mechanisms controlling the spring bloom magnitude in the GoM.

The role of parameters in Bayesian Online Changepoint Detection: detecting early warning of mount Merapi eruptions

Indonesia is a country that is surrounded by active volcanoes, which may erupt at any time; therefore, an online early warning system of volcanic eruption is crucial. In this paper, an online early warning system is constructed based on the changepoints detection on earthquake magnitude time series. This online early warning system is built using a Bayesian Online Changepoint Detection (BOCPD) method. One of the method's advantages is that one can customize the parameters (initial hyper-parameters and hazard-rate parameter) of BOCPD to follow a chosen constraint. These parameters determine the time and number of changepoints. An algorithm, called Appropriate Parameters of Bayesian Online Changepoint Detection for Early Warning (APBOCPD-EW), is proposed to get the parameters that lead the detection to the early warning points before eruption. We apply the algorithm for online early warning of mount Merapi eruptions. The results show that the proposed method produces parameters that give good estimation time for early warnings of mount Merapi's eruptions.

Nonlinear dynamic analysis of the fault activities induced by groundwater level variations

This study explored the possible repercussions of water-table reduction on the activity of faults in California, USA. The statistical period was regarded between 1972 and 2020. The cross-correlation and dynamic analysis were performed on the constructed time-series of the number and magnitude of the earthquakes. The cross-correlation results indicated a correlation between faults activity and water-table fluctuations. The dynamic analysis results of the monthly earthquake numbers indicated that declining groundwater levels produced irregularities and the conversion of periodicity in the time-series. Moreover, chaos and sensitivity to the initial conditions were diminished, while the randomness in time-series developed (m increased from 6 to 7). Additionally, the correlation and long-range persistency in time-series were reduced (H(2) decreased from 0.6493 to 0.6304). Furthermore, the number of earthquakes in a month and multifractality were limited (for ΔH from 1.8557 to 1.1701 and Δα from 2.0726 to 1.3743). The dynamic analysis results of the earthquake magnitude time-series suggested that the periodicity and predictability were reduced, while irregularities and randomness in time-series were extended. Moreover, long-memory, correlation, and multifractal characteristics in time-series were diminished (for H(2) from 0.9506 to 0.6310, ΔH from 0.1277 to 0.0536, and Δα from 0.27 to 0.1665). Besides, the possibilities of large and small earthquakes were balanced, while the probability of an earthquake of a greater magnitude shifted. The cross-correlation results were consistent with the dynamic analysis findings and confirmed water-table reduction correlation with faults’ seismic activity. In summary, seismicity pattern (rates and magnitude) was altered due to a reduction in water-table.

Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

Long-Term Correlations in São Francisco River Flow: The Influence of Sobradinho Dam

Abstract In this work we study the influence of the Sobradinho dam construction on daily streamflow of São Francisco River, Brasil, by analyzing long-range correlations in magnitude and sign time series obtained from streamflow anomalies, using the Detrended Fluctuation Analysis (DFA) method. The magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties. The streamflow data recorded during the period 1929-2009, were divided in the periods pre-construction (1929 to 1972) and post-construction (1980 to 2009) of Sobradinho dam and analyzed for small scales (less than 1 year) and for large scales (more than 1 year). In post-construction of Sobradinho dam, DFA-exponents of magnitude series increased at small scales (0.895 to 1.013) and at large scales (0.371 to 0.619) indicating that the memory associated with nonlinear components becames stronger. For sign series, the DFA-exponent increased at small scales (0.596 to 0.692) indicating stronger persistence of flow increments direction, and decreased at large scales (0.381 to 0.259) indicating stronger anti-persistence (positive increments are more likely to be followed by negative increments and vice versa). These results provide new evidence on the hydrological changes in the São Francisco River caused by human activities.

Investigation of the temporal correlations between earthquake magnitudes before the Mexico M8.2 earthquake on 7 September 2017

By employing Detrended Fluctuation Analysis (DFA), which has been established as a standard method to investigate long range correlations in nonstationary time series, we study the temporal correlations between the magnitudes of the earthquakes that occurred before the recent deadly Mexico M8.2 earthquake on 7 September 2017 in Chiapas region. Our aim here is to shed light on the origin of the following precursory phenomenon found in our previous publication (Sarlis et al., 2018): Upon considering the analysis of seismicity in the new time domain termed natural time, it was shown that the entropy change of seismicity under time reversal exhibited an important minimum almost 3 months before this major earthquake. Here the application of DFA to earthquake magnitude time series reveals that this minimum of the entropy change of seismicity is preceded as well as followed by characteristic changes of temporal correlations between earthquake magnitudes, which are quantified by the DFA exponent α. In particular, before this minimum the long range correlations breakdown to an almost random behavior possibly turning to anticorrelation (α≤0.5) while after the minimum (and before the major M8.2 earthquake) long range correlations develop with an exponent α between 0.6 and 0.7.

A remarkable change of the entropy of seismicity in natural time under time reversal before the super-giant M9 Tohoku earthquake on 11 March 2011

Here, we analyze in natural time all earthquakes of magnitude (M) 3.5 or larger in Japan from 1 January 1984 until the occurrence of the super-giant M9 Tohoku earthquake on 11 March 2011. We find that two and a half months before this M9 earthquake a pronounced minimum of the entropy change of seismicity under time reversal is observed. Remarkably the exponent α resulting from the detrended fluctuation analysis of the earthquake magnitude time-series exhibits a simultaneous minimum with an unusual low value () indicating an evident anticorrelated behavior. The validity of these findings is supported by the most studied non-conservative self-organized criticality model for earthquakes since it exhibts a non-zero change of the entropy upon time reversal, which reveals a breaking of the time symmetry, thus reflecting the predictability in this model.

Differences in nonlinear heart dynamics during rest and exercise and for different training

Objective: In this work we want to analyze differences in nonlinear properties between rest and exercise and also to study the permanent effects of physical exercise on heart rate dynamics. Approach: It has been shown that physical exercise alters heart dynamics by increasing heart rate and decreasing variability, modifying spectral power and linear correlations, etc. We hypothesize that physical exercise should also reduce nonlinearity in the heartbeat time series. To quantify nonlinearity in the heartbeat time series, we use an index of nonlinearity recently proposed by Bernaola et al based on correlations of the magnitude time series. Main results: Our results confirm our initial hypothesis of loss of nonlinearity during physical exercise. Moreover, regarding the permanent effects of physical exercise on heart rate dynamics, we also obtain that aerobic physical training tends to increase nonlinearity in heart dynamics during rest. Significance: It is well-known that heart dynamics are controlled by complex interactions between the sympathetic and parasympathetic branches of the autonomic nervous system. Moreover, these two branches act in a competing way, resulting in a clear parasympathetic withdrawal and sympathetic activation during physical exercise. We associate these interactions during physical exercise with a drastic loss of nonlinear properties in the heartbeat time series, revealing the importance of nonlinearity measures in the study of complex systems.

Micro-scale, mid-scale, and macro-scale in global seismicity identified by empirical mode decomposition and their multifractal characteristics

The magnitude time-series of the global seismicity is analyzed by the empirical mode decomposition giving rise to 14 intrinsic mode functions (IMF) and a trend. Using Hurst analysis one can identify three different sums of these IMFs and the trend which exhibit distinct multifractal behaviour and correspond to micro-, mid- and macro-scales. Their multifractal detrended fluctuation analysis reveals that the micro-scale time-series exhibits anticorrelated behaviour in contrast to the mid-scale one which is long-range correlated. Concerning the mid-scale one, in the range of 30 to 300 consecutive events the maximum entropy method power spectra indicates that it exhibits an 1/fα behaviour with α close to 1/3 which is compatible with the long-range correlations identified by detrended fluctuation analysis during periods of stationary seismicity. The results have been also verified to hold regionally for the earthquakes in Japan and shed light on the significance of the mid-scale of 30 to 300 events in the natural time analysis of global (and regional) seismicity. It is shown that when using the mid-scale time-series only, we can obtain results similar to those obtained by the natural time analysis of global seismicity when focusing on the prediction of earthquakes with M ≥ 8.4.

Diagnosing Conditions Associated with Large Intensity Forecast Errors in the Hurricane Weather Research and Forecasting (HWRF) Model

Abstract Understanding and forecasting tropical cyclone (TC) intensity change continues to be a paramount challenge for the research and operational communities, partly because of inherent systematic biases contained in model guidance, which can be difficult to diagnose. The purpose of this paper is to present a method to identify such systematic biases by comparing forecasts characterized by large intensity errors with analog forecasts that exhibit small intensity errors. The methodology is applied to the 2015 version of the Hurricane Weather Research and Forecasting (HWRF) Model retrospective forecasts in the North Atlantic (NATL) and eastern North Pacific (EPAC) basins during 2011–14. Forecasts with large 24-h intensity errors are defined to be in the top 15% of all cases in the distribution that underforecast intensity. These forecasts are compared to analog forecasts taken from the bottom 50% of the error distribution. Analog forecasts are identified by finding the case that has 0–24-h intensity and wind shear magnitude time series that are similar to the large intensity error forecasts. Composite differences of the large and small intensity error forecasts reveal that the EPAC large error forecasts have weaker reflectivity and vertical motion near the TC inner core from 3 h onward. Results over the NATL are less clear, with the significant differences between the large and small error forecasts occurring radially outward from the TC core. Though applied to TCs, this analog methodology could be useful for diagnosing systematic model biases in other applications.

Multiscale multifractal detrended fluctuation analysis of earthquake magnitude series of Southern California

The multifractal characteristics of magnitude time series of earthquakes that occurred in Southern California from 1990 to 2010 are studied in this work. A method for the scale division of the magnitude of these earthquakes based on empirical mode decomposition (EMD) and multifractal analysis is proposed. This method gains a new insight into measuring multifractal properties of the magnitude time series at multiple scales, and it reveals further information about the dynamic seismic behavior. By using EMD, a time series can be decomposed into mode time series that represent different time–frequency components. We find that time–frequency components show long-range correlation with different Hurst exponents by using R∕S analysis. Based on the different fractal structures of components, we consider three different scale series: Micro-, Mid- and Macro-scale subsequences, which are superposed and reconstructed by the components. The multifractal properties of the three scale subsequences are analyzed by using multifractal detrended fluctuation analysis (MF-DFA). The results show that the three different scale subsequences have various shapes of multifractal spectra and corresponding distinct properties. The Micro-scale subsequence singularity spectrum shows left-skewed, indicating a relative dominance of the lower Hurst exponent; the Mid-scale subsequence has a right-skewed singularity spectrum; the Macro-scale subsequence exhibits the most significant persistence and shows the strongest multifractality.

Temporal analysis of croatian seismogenic zones to improve earthquake magnitude prediction

The prediction of earthquakes is a task of utmost difficulty that has been addressed in many different ways. However, an initial definition of the area of interest is needed, with adequate catalogs. In this work, different seismogenic zones proposals in the Republic of Croatia are studied, in terms of predictability. Such zones have been characterized with widely used seismicity parameters. Later, studies based on training and test sets properties as well as the quality of the data involved are carried out. The studies presented in this work analyze the prediction performance across the earthquake magnitude time series of the target seismogenic zones. Results show that specific prediction techniques could be used in some zones to improve earthquake magnitude prediction.

Structural and functional quantitative susceptibility mapping from standard fMRI studies.

Standard functional MRI (fMRI), which includes resting-state or paradigm-driven designs, is widely used in studies of brain function, aging, and disease. These fMRI studies typically use two-dimensional gradient echo-planar imaging, which inherently contains phase data that enables quantitative susceptibility mapping (QSM). This work focuses on the dual value of QSM within fMRI studies, by providing both a localized analysis of functional changes in activated tissue, and iron-sensitive structural maps in deep grey matter (DGM). Using a visual paradigm fMRI study on healthy volunteers at clinical (1.5 T) and high field strength (4.7 T), we perform functional analysis of magnitude and QSM time series, and at the same time harness structural QSM of iron-rich DGM, including globus pallidus, putamen, caudate head, substantia nigra, and red nucleus. The effects of fMRI spatial resolution and time series variation on structural DGM QSM are investigated. Our results indicate that structural DGM QSM is feasible within existing fMRI studies, provided that the voxel dimensions are equal to or less than 3 mm, with higher resolutions preferred. The mean DGM QSM values were about 40 to 220 ppb, while the interquartile ranges of the DGM QSM time series varied from about 3 to 9 ppb, depending on structure and resolution. In contrast, the peak voxel functional QSM (fQSM) changes in activated visual cortex ranged from about -10 to -30 ppb, and functional clusters were consistently smaller on QSM than magnitude fMRI. Mean-level DGM QSM of the time series was successfully extracted in all cases, while fQSM results were more prone to residual background fields and showed less functional change compared with standard magnitude fMRI. Under the conditions prescribed, standard fMRI studies may be used for robust mean-level DGM QSM, enabling study of DGM iron accumulation, in addition to functional analysis. Copyright © 2016 John Wiley & Sons, Ltd.