Relative Characteristic Classes

1957 ◽  
Vol 79 (3) ◽  
pp. 517 ◽  
Author(s):  
Michel A. Kervaire
Keyword(s):  
Characteristic Classes ◽  
Relative Characteristic

On the Buchstaber Subring in MSp∗

1998 ◽  
Vol 5 (5) ◽  
pp. 401-414
Author(s):  
M. Bakuradze
Keyword(s):  
Tensor Product ◽  
Characteristic Classes ◽  
Generalized Quaternion ◽  
Symplectic Cobordisms

Abstract A formula is given to calculate the last n number of symplectic characteristic classes of the tensor product of the vector Spin(3)- and Sp(n)-bundles through its first 2n number of characteristic classes and through characteristic classes of Sp(n)-bundle. An application of this formula is given in symplectic cobordisms and in rings of symplectic cobordisms of generalized quaternion groups.

On killing characteristic classes by cobordism

1979 ◽  
Vol 29 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Stavros Papastavridis
Keyword(s):  
Characteristic Classes

scholarly journals Improving Accuracy in Studying the Interactions of Seismic Waves with Bottom Sediments

2021 ◽  
Vol 9 (2) ◽  
pp. 229
Author(s):  
Georgy Mitrofanov ◽  
Nikita Goreyavchev ◽  
Roman Kushnarev
Keyword(s):  
Bottom Sediments ◽  
Seismic Waves ◽  
Wide Frequency Range ◽  
Quality Of Data ◽  
High Quality ◽  
Relative Characteristic ◽  
Seabed Evolution ◽  
Marine Seismic ◽  
Very High

The emerging tasks of determining the features of bottom sediments, including the evolution of the seabed, require a significant improvement in the quality of data and methods for their processing. Marine seismic data has traditionally been perceived to be of high quality compared to land data. However, high quality is always a relative characteristic and is determined by the problem being solved. In a detailed study of complex processes, the interaction of waves with bottom sediments, as well as the processes of seabed evolution over short time intervals (not millions of years), we need very high accuracy of observations. If we also need significant volumes of research covering large areas, then a significant revision of questions about the quality of observations and methods of processing is required to improve the quality of data. The article provides an example of data obtained during high-precision marine surveys and containing a wide frequency range from hundreds of hertz to kilohertz. It is shown that these data, visually having a very high quality, have variations in wavelets at all analyzed frequencies. The corresponding variations reach tens of percent. The use of the method of factor decomposition in the spectral domain made it possible to significantly improve the quality of the data, reducing the variability of wavelets by several times.

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