TY  - CONF
N2  - Noise is the unwanted energy in a seismic trace opposed to the signals corresponding to reflected energy from the subsurface features. Since it can overlap with the main signals' energy and conceal the geological information, noise attenuation is one of the most important steps in seismic data processing. The most common method is frequency filtering. However, due to its limitations on separating the noise from signals, this method usually results in hurting the signal. Hence, it is important to develop an alternative method that can attenuate the noise without affecting the signal. Filters based on time-frequency analysis of the data can have a better separation of the noise from signal as they maintain the time localization of events while presenting their frequency content simultaneously. One of the recent approaches to time-frequency analysis of signals is the Empirical Wavelet Transform (EWT) which provides adaptive wavelet filter bank for signal analysis. In this paper, a filter is designed based on EWT for random noise attenuation and is applied on both synthetic and real data. To evaluate the proposed filter performance, its results are compared with the filters based on Short Time Fourier Transform and Wavelet transform. As the EWT filter separate different seismic features using the adaptive basis wavelets, it can attenuate the noise while preserving the signals with higher precision. © 2019, International Petroleum Technology Conference
N1  - cited By 1; Conference of International Petroleum Technology Conference 2019, IPTC 2019 ; Conference Date: 26 March 2019 Through 28 March 2019; Conference Code:146421
KW  - Data handling; Gasoline; Seismology; Separation; Signal analysis; Signal to noise ratio
KW  -  Geological information; Noise attenuation; Seismic data processing; Short time Fourier transforms; Signal-to-noise ratio enhancement; Subsurface features; Synthetic and real data; Time frequency analysis
KW  -  Wavelet transforms
ID  - scholars11880
TI  - Signal to noise ratio enhancement using empirical wavelet transform
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088405929&doi=10.2523%2fiptc-19569-ms&partnerID=40&md5=1af58ea0c874789943eebb7c1aa67480
A1  - Lee, W.Y.
A1  - Hamidi, R.
A1  - Ghosh, D.
A1  - Musa, M.H.
Y1  - 2019///
SN  - 9781613996195
PB  - International Petroleum Technology Conference (IPTC)
ER  -