scholarly journals Determination of First Arrival Wave Type of Microseismic Signals and Approach to Wave Velocity Correction

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
GuangDong Song ◽  
JiuLong Cheng ◽  
BinXin Hu ◽  
Feng Zhu ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Wave Velocity ◽  
Cross Correlation ◽  
Arrival Time ◽  
Propagation Model ◽  
Time Difference ◽  
Wave Type ◽  
Velocity Correction ◽  
First Arrival ◽  
The Cross

Given the complex environment experienced in working mines, the vibration waves produced by processes such as rock fracture in deep formations usually show interference effects when monitored due to other signals, the so-called “clutter” in the signal, which are interfered with the clutter. At the same time, owing to the influence of system noise, the first arrival time and the arrival time difference values of the signals obtained cannot easily be determined accurately. The propagation model for the microseismic signals experienced and the discrimination method used to determine the first arrival wave type can be established using knowledge of the spatial geometry between the sensors used and the seismic source. Thus, the filtering of the actual from the abnormal wave signals is possible. Using the theory of signal cross-correlation in this work, a correction method for the arrival velocity of the first microseismic signal has been proposed and evaluated. By calculating the cross-correlation coefficient of the same source vibration signal and finding the position that corresponds to the maximum value of the cross-correlation coefficient, the arrival time difference between the signals seen in the two channels is obtained. Thus, the key conclusions can be drawn from the experiments carried out: when the signal-to-noise ratio of the original signal is low, the time difference can still be determined with high accuracy. Further, a wave velocity correction criterion has also been proposed, where the velocity correction of the S wave or the R wave can be realized by combining the spatial coordinate information on the blasting point and an algorithm representing the signal cross-correlation to arrival time difference is used.

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

2014 ◽  
Vol 29 (01) ◽  
pp. 1450236 ◽  
Author(s):  
Guangxi Cao ◽  
Yan Han
Keyword(s):  
Correlation Coefficient ◽  
Stock Markets ◽  
Cross Correlation ◽  
Sunshine Duration ◽  
Statistical Test ◽  
Nonlinear Method ◽  
Weather Variables ◽  
Chinese Stock Markets ◽  
The Cross ◽  
The Relationship

Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole.

scholarly journals Modeling Complex System Correlation Using Detrended Cross-Correlation Coefficient

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
Hong Zhang ◽  
You Gao
Keyword(s):  
Complex System ◽  
Time Series ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Correlation Property ◽  
Traffic Time Series ◽  
Cross Correlations ◽  
The Cross ◽  
Active Research

The understanding of complex systems has become an area of active research for physicists because such systems exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails, and fractality. We here focus on traffic dynamic as an example of a complex system. By applying the detrended cross-correlation coefficient method to traffic time series, we find that the traffic fluctuation time series may exhibit cross-correlation characteristic. Further, we show that two traffic speed time series derived from adjacent sections exhibit much stronger cross-correlations than the two speed series derived from adjacent lanes. Similarly, we also demonstrate that the cross-correlation property between the traffic volume variables from two adjacent sections is stronger than the cross-correlation property between the volume variables of adjacent lanes.

scholarly journals Применение анализа совместных рекуррентностей к оценке фазовой синхронизации физиологических сигналов

2021 ◽  
Vol 91 (12) ◽  
pp. 2045
Author(s):  
O.E. Дик ◽  
A.Л. Глазов
Keyword(s):  
Atrial Fibrillation ◽  
Correlation Coefficient ◽  
Cognitive Functions ◽  
Cross Correlation ◽  
Phase Synchronization ◽  
Phase Trajectories ◽  
Brain Responses ◽  
The Cross ◽  
Theta Range

Based on the analysis of joint recurrences, differences in phase synchronization between rhythmic photostimulation and brain responses were revealed in individuals with atrial fibrillation of paroxysmal and persistent types. As a measure of phase synchronization between two signals, the cross-correlation coefficient between the probabilities of recurrences of the corresponding phase trajectories is considered. With a lengthening of the lifetime of atrial fibrillation and an increase in the degree of decline in cognitive functions, the value of this coefficient increases for brain responses to theta-range frequencies.

Estimating Membrane Voltage Correlations From Extracellular Spike Trains

2003 ◽  
Vol 89 (4) ◽  
pp. 2271-2278 ◽  
Author(s):  
Jessy D. Dorn ◽  
Dario L. Ringach
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Spike Trains ◽  
Membrane Voltage ◽  
Upper And Lower Bounds ◽  
Nominal Data ◽  
Order Of Magnitude ◽  
The Mean ◽  
The Cross ◽  
Measure Of Association

The cross-correlation coefficient between neural spike trains is a commonly used tool in the study of neural interactions. Two well-known complications that arise in its interpretation are 1) modulations in the correlation coefficient may result solely from changes in the mean firing rate of the cells and 2) the mean firing rates of the neurons impose upper and lower bounds on the correlation coefficient whose absolute values differ by an order of magnitude or more. Here, we propose a model-based approach to the interpretation of spike train correlations that circumvents these problems. The basic idea of our proposal is to estimate the cross-correlation coefficient between the membrane voltages of two cells from their extracellular spike trains and use the resulting value as the degree of correlation (or association) of neural activity. This is done in the context of a model that assumes the membrane voltages of the cells have a joint normal distribution and spikes are generated by a simple thresholding operation. We show that, under these assumptions, the estimation of the correlation coefficient between the membrane voltages reduces to the calculation of a tetrachoric correlation coefficient (a measure of association in nominal data introduced by Karl Pearson) on a contingency table calculated from the spike data. Simulations of conductance-based leaky integrate-and-fire neurons indicate that, despite its simplicity, the technique yields very good estimates of the intracellular membrane voltage correlation from the extracellular spike trains in biologically realistic models.

scholarly journals A qualitative study of the seismo-ionospheric precursors prior to the 6 April 2009 earthquake in L'Aquila, Italy

2010 ◽  
Vol 10 (1) ◽  
pp. 133-137 ◽  
Author(s):  
G. S. Tsolis ◽  
T. D. Xenos
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Seismic Event ◽  
Correlation Method ◽  
L’Aquila Earthquake ◽  
Earthquake Preparation ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method ◽  
The Cross

Abstract. In this paper we use the Cross Correlation analysis method in conjunction with the Empirical Mode Decomposition to analyze foF2 signals collected from Rome, Athens and San Vito ionospheric stations, in order to verify the existence of seismo-ionospheric precursors prior to M=6.3 L'Aquila earthquake in Italy. The adaptive nature of EMD allows for removing the geophysical noise from the foF2 signals, and then to calculate the correlation coefficient between them. According to the cross correlation coefficient theory, we expect the stations which located inside the earthquake preparation area, as evaluated using Dobrovolsky equation, to capture the ionospheric disturbances generated by the seismic event. On the other hand the stations outside of this area are expected to remain unaffected. The results of our study are in accordance with the theoretical model, evidencing ionospheric modification prior to L'Aquila earthquake in a certain area around the epicenter. However, it was found that the selection of stations at the limits of the theoretically estimated earthquake preparation area is not the best choice when the cross correlation method is applied, since the modification of the ionosphere over these stations may not be enough for the ionospheric precursors to appear. Our experimental results also show that when a seismic event constitutes the main shock after a series of pre-seismic activity, precursors may appear as early as 22 days prior to the event.

Self-Adaptive Stability-Enhancing Technology With Tip Air Injection in an Axial Flow Compressor

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Jichao Li
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Axial Flow ◽  
Stability Control ◽  
Air Injection ◽  
Axial Flow Compressor ◽  
Comparison Results ◽  
The Cross ◽  
Flow Compressor ◽  
Self Adaptive

Self-adaptive stability control with discrete tip air injection and online detection of prestall inception is experimentally studied in a low-speed axial flow compressor. The control strategy is to sense the cross-correlation coefficient of the wall static pressure patterns and to feed back the signal to an annular array of eight separately proportional injecting valves. The real-time detecting algorithm based on cross-correlation theory is proposed and experimentally conducted using the axisymmetric arrangement of time-resolved sensors. Subsequently, the sensitivity of the cross-correlation coefficient to the discrete tip air injection is investigated. Thus, the control law is formed on the basis of the cross-correlation as a function of the injected momentum ratios. The steady injection and the on–off pulsating injection are simultaneously selected for comparison. Results show that the proposed self-adaptive stability control using digital signal processing (DSP) controller can save energy when the compressor is stable. This control also provides protection when needed. With nearly the same stall margin improvement (SMI) as the steady injection (maximum SMI is 44.2%), the energy of the injected air is roughly a quarter of the steady injection. Unlike the on–off pulsating jet, the new actuating scheme can reduce the unsteady force impinging onto the compressor blades caused by the pulsating jets in addition to achieve the much larger stability range extension.

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