scholarly journals The relation between seismicity and water level changes in the Koyna–Warna region, India

2012 ◽  
Vol 12 (3) ◽  
pp. 813-817 ◽  
Author(s):  
J. Pavan Kumar ◽  
D. V. Ramana ◽  
R. K. Chadha ◽  
C. Singh ◽  
M. Shekar
Keyword(s):  
Water Level ◽  
Cross Correlation ◽  
Time Lag ◽  
Hydraulic Diffusivity ◽  
Water Level Changes ◽  
Daily Water ◽  
The Cross

Abstract. The analysis of the cross-correlation between the seismicity and the daily water level changes in both the Koyna and Warna (India) reservoirs is studied. The time lag between both datasets is calculated and is applied to estimate the hydraulic diffusivity. The range of the hydraulic diffusivity estimated between 0.1 m2 s−1 and 10 m2 s−1.

scholarly journals A study of the cross-correlation and time lag in black hole X-ray binary XTE J1859+226

2017 ◽  
Vol 362 (7) ◽  
Author(s):  
Songpeng Pei ◽  
Guoqiang Ding ◽  
Zhibing Li ◽  
Yajuan Lei ◽  
Rai Yuen ◽  
...  
Keyword(s):  
Black Hole ◽  
Cross Correlation ◽  
Time Lag ◽  
X Ray ◽  
The Cross

scholarly journals EVOLUTION OF THE CROSS-CORRELATION AND TIME LAG OF 4U 1735-44 ALONG THE BRANCHES

2013 ◽  
Vol 146 (3) ◽  
pp. 60 ◽  
Author(s):  
Ya-Juan Lei ◽  
Hao-Tong Zhang ◽  
Cheng-Min Zhang ◽  
Jin-Lu Qu ◽  
Hai-Long Yuan ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Time Lag ◽  
The Cross

Analysis of the cross-correlation between water level and seismicity at Açu reservoir (Brazil)

2015 ◽  
Vol 658 ◽  
pp. 151-158 ◽  
Author(s):  
Luciano Telesca ◽  
Aderson F. do Nascimento ◽  
Francisco H.R. Bezerra ◽  
Joaquim M. Ferreira
Keyword(s):  
Water Level ◽  
Cross Correlation ◽  
The Cross

scholarly journals Autocorrelation and cross-correlation flow analysis along the confluence of the Kupa and Sava Rivers

2021 ◽  
Vol 36 (5) ◽  
pp. 67-77
Author(s):  
Marta Caren ◽  
Krešimir Pavlić
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Time Lag ◽  
Flow Analysis ◽  
Meteorological Station ◽  
Cross Correlation Function ◽  
Maximum Value ◽  
Time Period ◽  
Cross Correlation Analysis ◽  
The Cross

In this paper, an autocorrelation and cross-correlation analysis of the flow of the Kupa and Sava rivers was performed. The analysis was performed at hydrological stations close to the confluence of these two rivers near the city of Sisak, based on data of mean daily flows and daily precipitation. The analysed time period is from 2008 to 2017, with the series being divided into two parts of five years each, from 2008 to 2012 and 2013 to 2017. Daily flow data were measured at the hydrological stations Farkašić on the Kupa River and Crnac on the Sava River, and data on precipitation at the main meteorological station and the automatic meteorological station Sisak. The maximum value of the cross-correlation function between the hydrological stations at the Kupa and Sava rivers is very high, but at a time lag of zero days. The value of the cross-correlation function remains high, up to 0.6 and up to a 4 day lag. The cross-correlation function between precipitation and hydrological data has a very low maximum value.

scholarly journals Ecology of Icerya purchasi Maskell, 1879 (Homoptera: Margarodidae) on clementine trees at Sidi Moussa (Central Mitidja, Algeria)

2020 ◽  
Vol 10 (5) ◽  
pp. 296-301
Author(s):  
L. Boukhobza ◽  
R. Belguendouz ◽  
M. Biche
Keyword(s):  
Cross Correlation ◽  
Temporal Dynamics ◽  
Statistical Tests ◽  
Integrated Control ◽  
Time Lag ◽  
Pairwise Comparison ◽  
Point Of View ◽  
Correlation Test ◽  
Larval Stages ◽  
The Cross

In order to establish better communication between applied entomology and fundamental ecology to consider an integrated control against the Australian cochineal Icerya purshasi Maskell, 1879 (Homoptera: Margarodidae) the most formidable pest for citrus fruits, a study on the Spatio-temporal dynamics of the parasite were followed for two years in a clementine orchard in Western Mitidja in Algeria. Ten-day samples of leaves and twigs were carried out from 2017 to 2018. The level of I. purchasi infestation is very high throughout the study period when the minimum threshold exceeds 400 individuals, with 3 intense periods of infestations: spring, summer and fall during the two years. The population of young larval stages is the largest during the two years of study with 13,323 individuals (62.79%) and 13,968 individuals (54.39%) in 2017 and 2018 respectively against 7896 individuals (37.21%) in 2017 and 11,715 individuals (33.50%) in 2018 for adults. Tukey's pairwise comparison test on the companion plan shows that the 2018 one is the most important from an overall effective point of view (ANOVA p≤1%, Tukey's test p≤1%). The Cross-Correlation Test shows the presence of a time lag (p=0.0371, p≤5%) and the maximum overall abundance was reported around mid-July for both campaigns. Statistical tests show that females show the same fertility during the two campaigns (ANOVA, p≤5%). peak fertility in 2017 was reported in mid-June, while peak fertility was reported in mid-July for the 2018 campaign. The Cross-Correlation Test shows a very significant time lag from one month to another (p=0.0064, p≤1%).

scholarly journals A novel method for quantifying periodicity and time delay in dynamic neural networks using unstable subaction potential threshold depolarizations

2020 ◽  
Vol 123 (3) ◽  
pp. 1236-1246
Author(s):  
Julian Sorensen ◽  
Nick J. Spencer
Keyword(s):  
Neural Networks ◽  
Correlation Function ◽  
Time Delay ◽  
Action Potentials ◽  
Cross Correlation ◽  
Time Lag ◽  
Cross Correlation Function ◽  
Electrical Signals ◽  
The Cross

Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multisite recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straightforward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveforms are unstable (e.g., fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability. NEW & NOTEWORTHY The determination of frequency(s) at which two sources are synchronized, and relative time delay between them, is a fundamental problem for a wide a range of signal-processing applications. In this methodology paper, we present novel procedures for periodicity estimation for single time series and joint periodicity and time delay estimation for two time series. The methods use properties of the cross-correlation function rather than the cross-correlation function explicitly.

Polarity Change Extraction of GPR Data for Under-road Cavity Detection: Application on Sudeoksa Testbed Data

2019 ◽  
Vol 24 (3) ◽  
pp. 419-431
Author(s):  
Jongha Hwang ◽  
Donggeon Kim ◽  
Xiangyue Li ◽  
Dong-Joo Min
Keyword(s):  
Phase Shift ◽  
Power Spectrum ◽  
Cross Correlation ◽  
Survey Methods ◽  
Time Lag ◽  
Background Signal ◽  
Data Set ◽  
Air Ground Interface ◽  
The Cross ◽  
Polarity Change

Ground penetrating radar (GPR) is one of the most widely used geophysical survey methods to locate cavities under roads due to its speedy exploration and high-resolution imaging. To locate underground cavities using GPR, we need to distinguish between cavity-induced reflections and other reflections, which can be achieved by examining the polarity change in reflections compared to the polarity of the transmitted signal. The polarity change can be measured from the phase shift between the target and first reflections. To estimate the phase shift in reflections, the method of computing the power spectrum difference between the original trace and background signal was proposed, but the method has a limitation for shallow reflectors. As an alternative method to avoid this limitation, we propose using only one component of the power spectrum difference, the cross-correlation between the target reflection and background signal. The cross-correlation has its maximum peak at a time lag between the target and first reflection (from the air-ground interface). Additionally, the phase at that time lag represents a phase shift between the two reflections. We compare our cross-correlation-based method with the conventional method of computing the whole power spectrum difference and investigate the feasibility of our method for distinguishing cavity-induced reflections using a 2D field data set acquired in a testbed in Sudeoksa, Korea.

scholarly journals Analysis of the cross-correlation between seismicity and water level in the Aswan area (Egypt) from 1982 to 2010

2012 ◽  
Vol 12 (7) ◽  
pp. 2203-2207 ◽  
Author(s):  
L. Telesca ◽  
R. ElShafey Fat ElBary ◽  
A. El-Ela Amin Mohamed ◽  
M. ElGabry
Keyword(s):  
Water Level ◽  
Cross Correlation ◽  
Time Lag ◽  
The Other ◽  
Time Lags ◽  
Surrounding Area ◽  
Pressure Diffusion ◽  
Triggering Mechanisms ◽  
Deep Seismicity ◽  
Aswan High Dam

Abstract. In this study the correlation between the monthly fluctuations of the water level of the Aswan High Dam and monthly number of earthquakes from 1982 to 2010, which occurred in the surrounding area, was investigated. Our findings reveal that significant correlation is present during the period 1982–1993 between water level and shallow seismicity (depth less than 15 km). The deep seismicity (depth larger than 15 km) is significantly correlated with the water level between January and April 1989. The time lag of the significant maximal cross-correlation varies from 2–8~months for the shallow seismicity, while it is around 7–8 months for the deep seismicity. These values of the time lags could be in favour of the presence of two distinct triggering mechanisms: one due to pore pressure diffusion and the other due to fracture compaction (undrained response).

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