Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

2019 ◽  
Vol 219 (2) ◽  
pp. 1281-1299 ◽  
Author(s):  
X T Dong ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Order Statistic ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Low Frequency ◽  
High Order ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
High Order Statistic ◽  
Adaptive Noise

SUMMARY The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.

Complex seismic trace analysis

Geophysics ◽  
1979 ◽  
Vol 44 (6) ◽  
pp. 1041-1063 ◽  
Author(s):  
M. T. Taner ◽  
F. Koehler ◽  
R. E. Sheriff
Keyword(s):  
Instantaneous Frequency ◽  
Seismic Data ◽  
Trace Analysis ◽  
Color Patterns ◽  
Phase Information ◽  
Real Component ◽  
The Real ◽  
Geological Interpretation ◽  
Envelope Amplitude ◽  
Spatial Changes

The conventional seismic trace can be viewed as the real component of a complex trace which can be uniquely calculated under usual conditions. The complex trace permits the unique separation of envelope amplitude and phase information and the calculation of instantaneous frequency. These and other quantities can be displayed in a color‐encoded manner which helps an interpreter see their interrelationship and spatial changes. The significance of color patterns and their geological interpretation is illustrated by examples of seismic data from three areas.

Non-stationary predictive filtering for seismic random noise suppression - A tutorial

Geophysics ◽  
2021 ◽  
pp. 1-79 ◽  
Author(s):  
Hang Wang ◽  
Wei Chen ◽  
Weilin Huang ◽  
Shaohuan Zu ◽  
Xingye Liu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Space Dimension ◽  
Noise Suppression ◽  
Random Noise ◽  
Spatial Dimension ◽  
Seismic Data Processing ◽  
Filtering Method ◽  
Time Space ◽  
Stationary Version ◽  
Predictive Filtering

Predictive filtering in the frequency domain is one of the most widely used denoising algorithms in the seismic data processing workflow. Predictive filtering is based on the assumption of linear/planar events in the time-space domain. In traditional predictive filtering method, the predictive filter is fixed across the spatial dimension, which cannot deal with the spatial variation of seismic data well. To handle the curving events, the predictive filter is either applied in local windows or extended to a non-stationary version. The regularized non-stationary autoregression (RNAR) method can be treated as a non-stationary extension of the traditional predictive filtering, where the predictive filter coefficients are variable in different space locations. The highly under-determined inverse problem is solved by shaping regularization with a smoothness constraint in space. We further extend the RNAR method to a more general case, where we can apply more constraints to the filter coefficients according to the features of seismic data. First, apart from the smoothness in space, we also apply a smoothing constraint in frequency, considering the coherency of the coefficients in the frequency dimension. Secondly, we apply a frequency dependent smoothing radius along the space dimension to better take advantage of the non-stationarity of seismic data in the frequency axis, and to better deal with noise. The proposed method is validated via several synthetic and field data examples.

DIRECT SEISMIC DETECTION OF HYDROCARBONS

1975 ◽  
Vol 15 (1) ◽  
pp. 81
Author(s):  
W. Pailthorpe ◽  
J. Wardell
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Lower Boundary ◽  
The United States ◽  
Bright Spot ◽  
Limiting Factor ◽  
Fluid Interface ◽  
Seismic Section ◽  
Wide Range ◽  
Very High

During the past two years, much publicity has been given to the direct indication of hydrocarbon accumulations by "Bright Spot" reflections: the very high amplitude reflections from a shale to gas-sand or gas-sand to water-sand interface. It was soon generally realised, however, that this phenomenon was of limited occurrence, being mostly restricted to young, shallow, sand and shale sequences such as the United States Gulf Coast. A more widely detectable indication of hydrocarbons was found to be the reflection from a fluid interface, such as the gas to water interface, within the reservoir. This reflection is characterised by its flatness, being a fluid interface, and is often called the "Flat Spot".Model studies show that the flat spots have a wide range of amplitudes, from very high for shallow gas to water contacts, to very low for deep oil to water contacts. However, many of the weaker flat spots on good recent marine seismic data have an adequate signal to random noise ratio for detection, and the problem is to separate and distinguish them from the other stronger reflections close by. In this respect the unique flatness of the fluid contact reflection can be exploited by dip discriminant processes, such as velocity filtering, to separate it from the generally dipping reflectors at its boundaries. A limiting factor in the detection of the deeper flat spots is the frequency bandwidth of the seismic data. Since the separation between the flat spot reflection and the upper and lower boundary reflections of the reservoir is often small, relatively high frequency data are needed to resolve these separate reflections. Correct display of the seismic data can be critical to flat spot detection, and some degree of vertical exaggeration of the seismic section is often required to increase apparent dips, and thus make the flat spots more noticeable.The flat spot is generally a smaller target than the structural features that conventional seismic surveys are designed to find and map, and so a denser than normal grid of seismic lines is required adequately to map most flat spots.

Simultaneous multifrequency inversion of full-waveform seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (2) ◽  
pp. R1-R14 ◽  
Author(s):  
Wenyi Hu ◽  
Aria Abubakar ◽  
Tarek M. Habashy
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Regularization Parameter ◽  
Synthetic Data ◽  
Frequency Data ◽  
Perfectly Matched Layers ◽  
Inversion Algorithm ◽  
Inversion Process ◽  
High Convergence Rate ◽  
Velocity Image

We present a simultaneous multifrequency inversion approach for seismic data interpretation. This algorithm inverts all frequency data components simultaneously. A data-weighting scheme balances the contributions from different frequency data components so the inversion process does not become dominated by high-frequency data components, which produce a velocity image with many artifacts. A Gauss-Newton minimization approach achieves a high convergence rate and an accurate reconstructed velocity image. By introducing a modified adjoint formulation, we can calculate the Jacobian matrix efficiently, allowing the material properties in the perfectly matched layers (PMLs) to be updated automatically during the inversion process. This feature ensures the correct behavior of the inversion and implies that the algorithm is appropriate for realistic applications where a priori information of the background medium is unavailable. Two different regularization schemes, an [Formula: see text]-norm and a weighted [Formula: see text]-norm function, are used in this algorithm for smooth profiles and profiles with sharp boundaries, respectively. The regularization parameter is determined automatically and adaptively by the so-called multiplicative regularization technique. To test the algorithm, we implement the inversion to reconstruct the Marmousi velocity model using synthetic data generated by the finite-difference time-domain code. These numerical simulation results indicate that this inversion algorithm is robust in terms of starting model and noise suppression. Under some circumstances, it is more robust than a traditional sequential inversion approach.

Improvement of S/N Ratio of Seismic Data by Multi-Channel Wiener Filter Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1501-1505
Author(s):  
Xue Hao ◽  
Na Li ◽  
Lin Ren
Keyword(s):  
Noise Reduction ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Wiener Filter

Noise reduction or cancellation is important for getting clear and useful signals. This paper deals with the implementation of the multi-channel wiener filter algorithm for noise suppression of seismic data. Known the velocity of reflection event, utilizes the resemblance of reflection signal in each seismic trace, the multi-channel wiener filter algorithm is effective in enhance reflection event and suppress the random noise. This algorithm is used to CDP gathers and the simulation shows the method is effective.

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