Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. V13-V21 ◽  
Author(s):  
Yang Liu ◽  
Ning Liu ◽  
Cai Liu
Keyword(s):  
Dimensional Space ◽  
Random Noise ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Frequency Space ◽  
Adaptive Prediction ◽  
Time Space ◽  
Prediction Filter

Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We developed a 2D [Formula: see text] adaptive prediction filter (APF) and further extended this to a 3D [Formula: see text] version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of patching, a popular method for handling nonstationarity, we obtained smoothly nonstationary APF coefficients by solving a global regularized least-squares problem. We used shaping regularization to control the smoothness of the coefficients of APF. Three-dimensional space-noncausal [Formula: see text] APF uses neighboring traces around the target traces in the 3D seismic cube to predict noise-free signal, so it provided more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequency-space deconvolution, time-space prediction filter, and frequency-space RNA, we tested the feasibility of our method in reducing seismic random noise on three synthetic data sets. Results of applying the proposed method to seismic field data demonstrated that nonstationary [Formula: see text] APF was effective in practice.

Lateral prediction for noise attenuation by t-x and f-x techniques

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1887-1896 ◽  
Author(s):  
Ray Abma ◽  
Jon Claerbout
Keyword(s):  
Random Noise ◽  
High Amplitude ◽  
Noise Attenuation ◽  
Space Domain ◽  
Frequency Space ◽  
Amplitude Noise ◽  
Time Space ◽  
Domain Prediction ◽  
Prediction Techniques ◽  
Prediction Filter

Attenuating random noise with a prediction filter in the time‐space domain generally produces results similar to those of predictions done in the frequency‐space domain. However, in the presence of moderate‐ to high‐amplitude noise, time‐space or t-x prediction passes less random noise than does frequency‐space, or f-x prediction. The f-x prediction may also produce false events in the presence of parallel events where t-x prediction does not. These advantages of t-x prediction are the result of its ability to control the length of the prediction filter in time. An f-x prediction produces an effective t-x domain filter that is as long in time as the input data. Gulunay’s f-x domain prediction tends to bias the predictions toward the traces nearest the output trace, allowing somewhat more noise to be passed, but this bias may be overcome by modifying the system of equations used to calculate the filter. The 3-D extension to the 2-D t-x and f-x prediction techniques allows improved noise attenuation because more samples are used in the predictions, and the requirement that events be strictly linear is relaxed.

Random noise attenuation using f-x regularized nonstationary autoregression

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V61-V69 ◽  
Author(s):  
Guochang Liu ◽  
Xiaohong Chen ◽  
Jing Du ◽  
Kailong Wu
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Geological Structure ◽  
Least Square ◽  
Noise Attenuation ◽  
Spatial Direction ◽  
Random Noise Attenuation ◽  
Time Space ◽  
Prediction Technique ◽  
Domain Prediction

We have developed a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space ([Formula: see text]) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using [Formula: see text] RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional [Formula: see text] domain prediction technique. The conventional [Formula: see text] domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic data. The new method does not require windowing strategies in spatial direction. We implement the algorithm by an iterated scheme using the conjugate-gradient method. We constrain the coefficients of nonstationary autoregression (NA) to be smooth along space and frequency in the [Formula: see text] domain. The shaping regularization in least-square inversion controls the smoothness of the coefficients of [Formula: see text] RNA. There are two key parameters in the proposed method: filter length and radius of shaping operator. Tests on synthetic and field data examples showed that, compared with [Formula: see text] domain and time-space domain prediction methods, [Formula: see text] RNA can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.

Inversion-based data-driven time-space domain random noise attenuation method

2017 ◽  
Vol 14 (4) ◽  
pp. 543-550 ◽  
Author(s):  
Yu-Min Zhao ◽  
Guo-Fa Li ◽  
Wei Wang ◽  
Zhen-Xiao Zhou ◽  
Bo-Wen Tang ◽  
...  
Keyword(s):  
Random Noise ◽  
Data Driven ◽  
Noise Attenuation ◽  
Space Domain ◽  
Random Noise Attenuation ◽  
Time Space

Streaming orthogonal prediction filter in the t-x domain for random noise attenuation

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. F41-F48 ◽  
Author(s):  
Yang Liu ◽  
Bingxiu Li
Keyword(s):  
Random Noise ◽  
Synthetic Data ◽  
Complex Surface ◽  
Seismic Signal ◽  
Seismic Exploration ◽  
Signal Recovery ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Surface Prediction ◽  
Many Sources

In seismic exploration, there are many sources of random noise, for example, scattering from a complex surface. Prediction filters (PFs) have been widely used for random noise attenuation, but these typically assume that the seismic signal is stationary. Seismic signals are fundamentally nonstationary. Stationary PFs fail in the presence of nonstationary events, even if the data are cut into overlapping windows (“patching”). We have developed an adaptive PF method based on streaming and orthogonalization for random noise attenuation in the [Formula: see text]-[Formula: see text] domain. Instead of using patching or regularization, the streaming orthogonal PF (SOPF) takes full advantage of the streaming method, which generates the signal value as each new noisy data value arrives. The streaming signal-and-noise orthogonalization further improves the signal recovery ability of the SOPF. The streaming characteristic makes the proposed method faster than iterative approaches. In comparison with [Formula: see text]-[Formula: see text] deconvolution and [Formula: see text]-[Formula: see text] regularized nonstationary autoregression, we tested the feasibility of the proposed method in attenuating random noise on two synthetic data sets. Field-data examples confirm that the [Formula: see text]-[Formula: see text] SOPF has a reasonable denoising ability in practice.

Multicomponent f-x seismic random noise attenuation via vector autoregressive operators

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V91-V99 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio Sacchi
Keyword(s):  
Temporal Frequency ◽  
Random Noise ◽  
Real Data ◽  
Var Model ◽  
Model Parameters ◽  
Prediction Errors ◽  
Noise Attenuation ◽  
Vector Autoregressive ◽  
Random Noise Attenuation ◽  
Frequency Space

We propose an extension of the traditional frequency-space ([Formula: see text]) random noise attenuation method to three-component seismic records. For this purpose, we develop a three-component vector autoregressive (VAR) model in the [Formula: see text] domain that is applied to the multicomponent spatial samples of each individual temporal frequency. VAR model parameters are estimated using the least-squares minimization of forward and backward prediction errors. VAR modeling effectively identifies the potential coherencies between various components of a multicomponent signal. We use the squared coherence spectrum of VAR models as an indicator to determine these coherencies. Synthetic and real data examples are provided to show the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document