Seismic wavelet estimation

2016 ◽  
pp. 94-111
Keyword(s):  
Wavelet Estimation ◽  
Seismic Wavelet

Robust wavelet estimation and blind deconvolution of noisy surface seismics

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. V37-V46 ◽  
Author(s):  
Mirko van der Baan ◽  
Dinh-Tuan Pham
Keyword(s):  
Blind Deconvolution ◽  
General Purpose ◽  
Sufficient Accuracy ◽  
Wiener Filtering ◽  
Challenging Problem ◽  
Limited Data ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Principal Frequency ◽  
Band Limited

Robust blind deconvolution is a challenging problem, particularly if the bandwidth of the seismic wavelet is narrow to very narrow; that is, if the wavelet bandwidth is similar to its principal frequency. The main problem is to estimate the phase of the wavelet with sufficient accuracy. The mutual information rate is a general-purpose criterion to measure whiteness using statistics of all orders. We modified this criterion to measure robustly the amplitude and phase spectrum of the wavelet in the presence of noise. No minimum phase assumptions were made. After wavelet estimation, we obtained an optimal deconvolution output using Wiener filtering. The new procedure performs well, even for very band-limited data; and it produces frequency-dependent phase estimates.

Wavelet estimation for a multidimensional acoustic or elastic earth

Geophysics ◽  
1990 ◽  
Vol 55 (7) ◽  
pp. 902-913 ◽  
Author(s):  
Arthur B. Weglein ◽  
Bruce G. Secrest
Keyword(s):  
Estimation Method ◽  
Normal Derivative ◽  
Source Spectrum ◽  
Earth Model ◽  
Far Field ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Array Pattern ◽  
The Earth ◽  
Source Array

A new and general wave theoretical wavelet estimation method is derived. Knowing the seismic wavelet is important both for processing seismic data and for modeling the seismic response. To obtain the wavelet, both statistical (e.g., Wiener‐Levinson) and deterministic (matching surface seismic to well‐log data) methods are generally used. In the marine case, a far‐field signature is often obtained with a deep‐towed hydrophone. The statistical methods do not allow obtaining the phase of the wavelet, whereas the deterministic method obviously requires data from a well. The deep‐towed hydrophone requires that the water be deep enough for the hydrophone to be in the far field and in addition that the reflections from the water bottom and structure do not corrupt the measured wavelet. None of the methods address the source array pattern, which is important for amplitude‐versus‐offset (AVO) studies. This paper presents a method of calculating the total wavelet, including the phase and source‐array pattern. When the source locations are specified, the method predicts the source spectrum. When the source is completely unknown (discrete and/or continuously distributed) the method predicts the wavefield due to this source. The method is in principle exact and yet no information about the properties of the earth is required. In addition, the theory allows either an acoustic wavelet (marine) or an elastic wavelet (land), so the wavelet is consistent with the earth model to be used in processing the data. To accomplish this, the method requires a new data collection procedure. It requires that the field and its normal derivative be measured on a surface. The procedure allows the multidimensional earth properties to be arbitrary and acts like a filter to eliminate the scattered energy from the wavelet calculation. The elastic wavelet estimation theory applied in this method may allow a true land wavelet to be obtained. Along with the derivation of the procedure, we present analytic and synthetic examples.

Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4241-4247 ◽  
Author(s):  
Hong Tao Sun ◽  
Yong Shou Dai ◽  
Fang Wang ◽  
Xing Peng
Keyword(s):  
Genetic Algorithm ◽  
Signal To Noise Ratio ◽  
Linear Optimization ◽  
Optimization Methods ◽  
High Signal ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Convergence Performance ◽  
High Order Statistics ◽  
Chaos Genetic Algorithm

Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.

An examination of the exponential decay method of mixed‐phase wavelet estimation

Geophysics ◽  
1984 ◽  
Vol 49 (12) ◽  
pp. 2094-2099 ◽  
Author(s):  
Gregory D. Lazear
Keyword(s):  
Exponential Decay ◽  
Mixed Phase ◽  
Estimation Methods ◽  
Minimum Phase ◽  
Signal To Noise ◽  
Wavelet Estimation ◽  
Data Window ◽  
Seismic Wavelet ◽  
Processing Theory ◽  
Data Windows

Signal processing theory states that an isolated wavelet which is causal and mixed phase may be converted to minimum phase by applying an exponential decay of amplitude with time. The exponential decay might therefore be a useful preprocessing step for seismic wavelet estimation since many estimation methods require that the wavelet in the data be minimum phase. This is the basis of a method proposed by Taner and Coburn (1980). The wavelets in a seismic trace, however, are generally not isolated, but instead are convolved with a densely populated reflection coefficient series causing severe wavelet overlap. Wavelet estimation is generally done using a window of data from the seismic trace which excludes refractions, surface waves, and data with poor signal‐to‐noise ratios. Due to the wavelet overlap, the window generally truncates wavelets at the window edges. When exponential decay is applied to the window, these truncated wavelets dominate the wavelet estimation methods. When no wavelet truncation occurs, the exponential decay converts each wavelet to minimum phase, and complete wavelets dominate the data window. If the reflection series is uncorrelated, then the autocorrelations of these data windows, when averaged over many traces, give an average autocorrelation which equals that of the decayed wavelet. This autocorrelation gives the correct minimum‐phase estimated wavelet. When truncation of wavelets does occur, the autocorrelation of the decayed data window does not equal that of the decayed wavelet, and an erroneous wavelet is estimated. Therefore, the exponential decay method is only useful for seismic wavelet estimation when data windows may be chosen such that no wavelets are truncated at the window onset.

TIME-VARYING SEISMIC WAVELET ESTIMATION FROM NONSTATIONARY SEISMIC DATA

2017 ◽  
Vol 60 (2) ◽  
pp. 191-202
Author(s):  
FENG Wei ◽  
HU Tian-Yue ◽  
YAO Feng-Chang ◽  
ZHANG Yan ◽  
Cui Yong-Fu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Time Varying ◽  
Wavelet Estimation ◽  
Seismic Wavelet

The impact of random noise on seismic wavelet estimation

2008 ◽  
Vol 27 (2) ◽  
pp. 226-230 ◽  
Author(s):  
Michael K. Broadhead
Keyword(s):  
Random Noise ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
The Impact
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