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

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.

Download Full-text

Frequency-space prediction filtering for acoustic clutter and random noise attenuation in ultrasound imaging

10.1117/12.2216566 ◽

2016 ◽

Author(s):

Junseob Shin ◽

Lianjie Huang

Keyword(s):

Ultrasound Imaging ◽

Random Noise ◽

Noise Attenuation ◽

Random Noise Attenuation ◽

Frequency Space

Download Full-text

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

Geophysics ◽

10.1190/geo2014-0011.1 ◽

2015 ◽

Vol 80 (1) ◽

pp. V13-V21 ◽

Cited By ~ 16

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.

Download Full-text

Random noise attenuation by f-x empirical-mode decomposition predictive filtering

Geophysics ◽

10.1190/geo2013-0080.1 ◽

2014 ◽

Vol 79 (3) ◽

pp. V81-V91 ◽

Cited By ~ 172

Author(s):

Yangkang Chen ◽

Jitao Ma

Keyword(s):

Empirical Mode Decomposition ◽

Seismic Data ◽

Random Noise ◽

Prediction Errors ◽

Noise Attenuation ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Random Noise Attenuation ◽

Mode Decomposition ◽

Predictive Filtering

Random noise attenuation always played an important role in seismic data processing. One of the most widely used methods for suppressing random noise was [Formula: see text] predictive filtering. When the subsurface structure becomes complex, this method suffered from higher prediction errors owing to the large number of different dip components that need to be predicted. We developed a novel denoising method termed [Formula: see text] empirical-mode decomposition (EMD) predictive filtering. This new scheme solved the problem that makes [Formula: see text] EMD ineffective with complex seismic data. Also, by making the prediction more precise, the new scheme removed the limitation of conventional [Formula: see text] predictive filtering when dealing with multidip seismic profiles. In this new method, we first applied EMD to each frequency slice in the [Formula: see text] domain and obtained several intrinsic mode functions (IMFs). Then, an autoregressive model was applied to the sum of the first few IMFs, which contained the high-dip-angle components, to predict the useful steeper events. Finally, the predicted events were added to the sum of the remaining IMFs. This process improved the prediction precision by using an EMD-based dip filter to reduce the dip components before [Formula: see text] predictive filtering. Synthetic and real data sets demonstrated the performance of our proposed method in preserving more useful energy.

Download Full-text