Joint time-variant spectral analysis — Part 1: Forward modeling the effects of thin-bed layering

2018 ◽  
Vol 6 (4) ◽  
pp. T967-T983
Author(s):  
Ramses G. Meza ◽  
J. Antonio Sierra ◽  
John P. Castagna ◽  
Umberto Barbato
Keyword(s):  
Seismic Data ◽  
Seismic Isolation ◽  
Forward Modeling ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Phase Component ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Specific Frequency ◽  
Net To Gross

Using time-frequency and time-phase analysis we found that for an isolated thin bed in a binary-impedance setting, there is no observable sensitivity in preferential illumination as layered net-to-gross (NTG) changes within the isolated thin bed, regardless of the way the internal layering is distributed — either uniformly or semirandomly. The NTG signature is observed on the amplitude (magnitude) responses, rather than any specific frequency or phase component. On the other hand, external mutual thin-bed interference can significantly change the preferred phase component for each participating target. This phenomenon is largely driven by the embedded seismic wavelet that determines the nominal seismic response of an isolated thin layer and what phase component would preferentially illuminate it. For vertical separations between mutually interfering and elastically comparable thin beds in which mutual constructive interference is achieved, the target bed will be preferentially illuminated at a phase component that is very close to that of a total seismic isolation, whereas the occurrence of mutual destructive interference will cause a significant departure on the phase preferential illumination from that of an isolated seismic thin bed. All these observations can provide an avenue to yield more robust stratigraphic interpretations of seismic data and enhance the confidence on subsurface description.

Value of nonstationary wavelet spectral balancing in mapping a faulted fluvial system, Bohai Gulf, China

2015 ◽  
Vol 3 (3) ◽  
pp. SS1-SS13 ◽  
Author(s):  
Huailai Zhou ◽  
Yuanjun Wang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Spectral Bandwidth ◽  
Deconvolution Method ◽  
Fluvial System ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Enhanced Resolution ◽  
Coherence Energy ◽  
Text Filtering

Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired over Bohai Gulf, China. We mapped these improvements spatially using a suite of 3D volumetric coherence, energy, curvature, and frequency attributes. The resulting images displayed improved lateral resolution of channel edges and fault edges with few, if any artifacts associated with amplification of random noise.

Time-variant wavelet extraction with a local-attribute-based time-frequency decomposition for seismic inversion

2017 ◽  
Vol 5 (1) ◽  
pp. SC9-SC16 ◽  
Author(s):  
Rui Zhang ◽  
Sergey Fomel
Keyword(s):  
Frequency Spectrum ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Signal ◽  
Seismic Inversion ◽  
Kernel Matrix ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Frequency Decomposition ◽  
Noise Contamination

Seismic impedance inversion has been widely used to estimate subsurface properties. Conventional inversion assumes that seismic data are the convolution result of seismic wavelet and reflectivity, implying that seismic data are stationary when a constant wavelet is considered. However, seismic data are nonstationary because of noise contamination and attenuation during wave propagation, which means that the frequency spectrum of the seismic signal changes from shallow to deep formations. We have developed a time-variant wavelet extraction method by using a local-attribute-based spectral decomposition technique. Time-variant wavelets are generated according to the local frequency spectrum, which can be used to construct a time-variant wavelet kernel matrix. By using this time-variant kernel matrix, we can obtain a better correlation between synthetic and extracted seismograms than by using constant wavelet on a field data example. Using this example, we have also compared the time-variant and constant wavelets for inverting the field data to estimate subsurface acoustic impedance. Our results showed improved resolution and a better fit to well-log-measured impedance by using the time-variant wavelets.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

Suppression of strong random noise in seismic data by using time-frequency peak filtering

2013 ◽  
Vol 56 (7) ◽  
pp. 1200-1208 ◽  
Author(s):  
Yue Li ◽  
BaoJun Yang ◽  
HongBo Lin ◽  
HaiTao Ma ◽  
PengFei Nie
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Time Frequency ◽  
Frequency Peak

scholarly journals Stationary Wave Interference and Its Relation to Tropical Convection and Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1369-1389 ◽  
Author(s):  
Michael Goss ◽  
Steven B. Feldstein ◽  
Sukyoung Lee
Keyword(s):  
Sea Ice ◽  
Stationary Wave ◽  
Warm Pool ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Boreal Winter ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Sea Ice Concentration ◽  
Stratospheric Polar Vortex

Abstract The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V307-V317 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Naihao Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
White Noise ◽  
Seismic Data ◽  
Prior Information ◽  
Training Procedure ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition

Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.

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