CROSS‐CORRELATION FILTERING

Geophysics ◽  
1954 ◽  
Vol 19 (4) ◽  
pp. 660-683 ◽  
Author(s):  
Hal J. Jones ◽  
John A. Morrison

Correlation analysis techniques may be applied to seismic data already subjected to standard recording and analysis procedure in an effort to extract additional information, or to raw data as an alternative filtering method. These techniques involve determination of certain parameters which provide a quantitative measure of the correlation between two sets of data. Among the most useful of these parameters are the auto‐ and cross‐correlation coefficients and functions long used by statisticians in time series analysis and recently applied to filtering and prediction problems in the field of communications. This paper discusses some applications of correlation analysis in interpretation of seismograms. The use of cross‐correlation analysis to identify weak reflections masked by high noise is illustrated for several problems. Equivalence of correlation analysis procedures to filtering operations is stressed. Special analog computing equipment facilitating computation of correlation coefficients and power spectra directly from oscillograms or graphs is described. A brief discussion of modern optimum filter theory is presented.

2019 ◽  
Vol 880 (1) ◽  
pp. 41 ◽  
Author(s):  
Beili Ying ◽  
Alessandro Bemporad ◽  
Silvio Giordano ◽  
Paolo Pagano ◽  
Li Feng ◽  
...  

2016 ◽  
Vol 15 (02) ◽  
pp. 1650012 ◽  
Author(s):  
Guangxi Cao ◽  
Cuiting He ◽  
Wei Xu

This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and [Formula: see text]-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.


2017 ◽  
Vol 32 (3) ◽  
pp. 39-49
Author(s):  
L. A. Elshin

In article methodical approaches to modeling, on the basis of tools of the cross-correlation analysis, Purchasing Managers' Index of the region are considered (on the example of the Volga Federal District). Within the constructed system of regional indexes the concept of determination of level of their influence on parameters of development of separate industrial sectors of economy of regional economic systems is approved.


2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga

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