Continuous wavelet transformation of seismic data for feature extraction

被引:13
作者
Ali, Amjad [1 ]
Chen Sheng-Chang [1 ]
Shah, Munawar [2 ]
机构
[1] Zhejiang Univ, Sch Earth Sci, Hangzhou, Zhejiang, Peoples R China
[2] Inst Space Technol, Islamabad, Pakistan
来源
SN APPLIED SCIENCES | 2020年 / 2卷 / 11期
关键词
Gassmann's equation; P-wave velocity; Synthetic seismic trace; Acoustic impedance inversion; Continuous wavelet transformation;
D O I
10.1007/s42452-020-03618-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Continuous wavelet transformation (CWT) as a new mathematical tool has provided deep insights for the identification of localized anomalous zone in the time series data set. In this study, a three-layer geological model is investigated by CWT to locate seismic reflections temporally and spatially. This model consists of three layers, where the third layers of the anticline structure are assumed to act as a pure sandstone hydrocarbon reservoir with 10% porosity. The equation of Gassmann has been implemented for the pore fluid substitution in the reservoir. Synthetic seismic data are generated for the three-layer geological model. Due to the presence of noise, it is always difficult to interpret seismic data. But, CWT has the ability of noise reduction, improving the visualization of a data set and locating the anomalies in terms of scalogram and 3D CWT coefficients. Synthetic seismic data of the geological structure are transformed by CWT. The successful transformation of P-wave velocity, synthetic seismic data and acoustic impedance inversion provided evidence to distinguish different interfaces accurately. CWT has successfully located seismic reflections by localizing high-energy spectrum within the cone of influence. Three high-energy spectrums have been identified at 0.8 s, 1 s and 1.07 s, and it exactly matches the seismic data and three-layer geological model.
引用
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页数:12
相关论文
共 30 条
[11]   AN ADAPTIVE OPTIMAL-KERNEL TIME-FREQUENCY REPRESENTATION [J].
JONES, DL ;
BARANIUK, RG .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1995, 43 (10) :2361-2371
[12]   Characteristics of solar diurnal variations: A case study based on records from the ground magnetic station at Vassouras, Brazil [J].
Klausner, V. ;
Papa, A. R. R. ;
Mendes, O. ;
Domingues, M. O. ;
Frick, P. .
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2013, 92 :124-136
[13]  
Mallat S. G., 1987, THEORY MULTIRESOLUTI
[14]  
Palupi I. R., 2018, EAGE HAGI 1 AS PAC M, P1
[15]  
Partyka G., 1999, LEAD EDGE, V18, P353, DOI [DOI 10.1190/1.1438295, 10.1190/1.1438295]
[16]  
Peyton L., 1998, LEADING EDGE, V17, P1294, DOI DOI 10.1190/1.1438127
[17]   Wavelets and signal processing [J].
Rioul, Olivier ;
Vetterli, Martin .
IEEE SIGNAL PROCESSING MAGAZINE, 1991, 8 (04) :14-38
[18]   Time-space characterization of droughts in the Sao Francisco river catchment using the Standard Precipitation Index and continuous wavelet transform [J].
Santos, Marcus Suassuna ;
Figueiredo Costa, Veber Afonso ;
Fernandes, Wilson dos Santos ;
de Paes, Rafael Pedrollo .
RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2019, 24
[19]  
Sun S., 2002, 72nd Annual International Meeting, SEG, Expanded Abstracts, P457, DOI 10.1190/1.1817281
[20]   COMPUTER-BASED HIGHLY SENSITIVE ELECTRON-WAVE INTERFEROMETRY [J].
TAKEDA, M ;
RU, QS .
APPLIED OPTICS, 1985, 24 (18) :3068-3071