Frequency Estimation Using Minimum Norm Algorithm on Seismic Data

2018 ◽  
pp. 153-163 ◽  
Author(s):  
Ch. Namitha ◽  
V. Uma Mahesh ◽  
M. Anusha ◽  
S. Koteswara Rao ◽  
T. Vaishnavi Chandra
Keyword(s):  
Seismic Data ◽  
Frequency Estimation ◽  
Minimum Norm

Fast seismic data recovery by combined minimum norm algorithm in DTCW domain

2013 ◽  
Vol 21 (7) ◽  
pp. 1239-1254
Author(s):  
Hongxia Wang ◽  
Bo Chen ◽  
Haoxing Yang
Keyword(s):  
Seismic Data ◽  
Data Recovery ◽  
Minimum Norm

Effective frequency estimation method for sinusoidal interference in seismic data

Geophysics ◽  
2021 ◽  
pp. 1-80
Author(s):  
Yijun Yuan ◽  
Shichang Zhou ◽  
Yun Wang ◽  
Jianjun Gao
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Frequency Estimation ◽  
Amplitude Spectrum ◽  
Estimation Method ◽  
Real Data ◽  
Estimation Algorithm ◽  
Search Technique ◽  
Spectrum Generation ◽  
Frequency Search

The removal of sinusoidal interference is an important step in seismic data processing, especially for data with low signal-to-noise ratios. The intermittent character of sinusoidal interference makes it challenging to identify and attenuate. To address this issue, we propose a method to accurately identify sinusoidal interference and rapidly estimate its frequencies. A spectrum-generation strategy is presented to generate an amplitude spectrum with noticeable sinusoidal interference. An initial estimate of the affected frequencies is found using a frequency-search technique based on the amplitude spectrum. The estimate is then refined by an iterative frequency estimation algorithm, which includes fast frequency estimation and normalized cross-correlation calculation. After modeling the noise using the precise frequency estimation, the sinusoidal interference in seismic data can then be suppressed by adaptively subtracting the estimated noise from the raw seismic data. The effectiveness of the proposed method in identifying sinusoidal interference is verified by testing it on synthetic and field data and by comparing the results with those from existing methods. Synthetic and real data examples indicate that the method is most applicable to land seismic data.

Off-Grid Frequency Estimation with Random Measurements

2017 ◽  
Vol E100.A (11) ◽  
pp. 2493-2497
Author(s):  
Xushan CHEN ◽  
Jibin YANG ◽  
Meng SUN ◽  
Jianfeng LI
Keyword(s):  
Frequency Estimation ◽  
Random Measurements

Formation geological depth image according to refraction and reflection marine seismic data

2017 ◽  
Vol 39 (6) ◽  
pp. 106-121
Author(s):  
A. O. Verpahovskaya ◽  
V. N. Pilipenko ◽  
Е. V. Pylypenko
Keyword(s):  
Seismic Data ◽  
Depth Image ◽  
Marine Seismic

Improvement of Jain’s algorithm for frequency estimation

2020 ◽  
Vol 65 (1) ◽  
pp. 115-122
Author(s):  
Andrea Amalia Minda
Keyword(s):  
Frequency Estimation ◽  
The Real ◽  
Real Frequency

In this paper we propose a procedure to correct Jain's algorithm, which in certain situations fails in correctly estimating the frequency by indicating frequency values that are very far from the real frequency. It happens because the two points considered for the method proposed by Jain are not on the same lobe. Thus, a method is proposed according to which these points are chosen so that the results are improved.

Sign in / Sign up

Export Citation Format

Share Document