A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time–Frequency Decomposition Domain

2021 ◽  
Author(s):  
Ning Wu ◽  
Yue Li ◽  
Jie Yan ◽  
Haitao Ma
Keyword(s):  
Seismic Data ◽  
Low Rank ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Enhancement Method

Nonstationary local time-frequency transform

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. V245-V254
Author(s):  
Yangkang Chen
Keyword(s):  
Local Time ◽  
Seismic Data ◽  
Low Frequency ◽  
Data Sets ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Data Registration ◽  
Frequency Decomposition ◽  
Temporal Smoothing ◽  
The Time Domain

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

Probing the subsurface karst features using time-frequency decomposition

2016 ◽  
Vol 4 (4) ◽  
pp. T533-T542 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Single Frequency ◽  
Frequency Interval ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Inherent Frequency ◽  
Resolution Mapping ◽  
Resource Exploration

The high-resolution mapping of karst features is of great importance to hydrocarbon discovery and recovery in the resource exploration field. Currently, however, there are few effective methods specifically tailored for such a task. The 3D seismic data can reveal the existence of karsts to some extent, but a precise characterization cannot be obtained. I have developed an effective framework for accurately probing the subsurface karst features using a well-developed time-frequency decomposition algorithm. More specifically, I have introduced a frequency interval analysis approach for obtaining the best karsts detection result using an optimal frequency interval. A high-resolution time-frequency transform was preferred in the proposed framework to capture the inherent frequency components hidden behind the amplitude map. Although the single-frequency slice could not provide a reliable karst depiction result, the summation over the selected frequency interval could obtain a high-resolution and high-fidelity delineation of subsurface karsts. I used a publicly available 3D field seismic data set as an example to indicate the performance of the proposed method.

Application of sparse time-frequency decomposition to seismic data

2014 ◽  
Vol 11 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Xiong-Wen Wang ◽  
Hua-Zhong Wang
Keyword(s):  
Seismic Data ◽  
Time Frequency ◽  
Frequency Decomposition

Seismic data analysis by adaptive sparse time-frequency decomposition

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. V207-V217 ◽  
Author(s):  
Hamid Sattari ◽  
Ali Gholami ◽  
Hamid R. Siahkoohi
Keyword(s):  
Data Analysis ◽  
Seismic Data ◽  
Mixed Norm ◽  
Gas Reservoir ◽  
Efficient Tool ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Amplitude Versus Offset ◽  
The Difference ◽  
Amplitude Versus

The variation of frequency content of a seismic trace with time carries information about the properties of the subsurface reflectivity sequence. Time-frequency (TF) analysis is a significant tool to extract such information for seismostratigraphic interpretation purposes. However, several TF transforms have been reported in the literature; higher resolution and sensitivity to local changes of the signal have always mattered. We have developed an adaptive high-resolution TF transform that is performed in two sequential steps: First, the window length is adaptively determined for each sample of the signal such that it leads to maximum compactness of energy in the resulting TF plane. Second, the generated nonstationary windows are used to inversely decompose the signal under study via a convex constrained sparse optimization, where a mixed norm of the TF coefficients is minimized subject to invertibility of the transform. Later on, the optimized transform is used as an efficient tool for seismic data analysis such as thin-bed characterization and thin-bedded gas reservoir detection. In the case of gas reservoir detection, based on amplitude versus offset analysis in the TF domain, a simple new method called the difference section was evaluated. The results of various numerical examples from synthetic and field data revealed a remarkable performance of the proposed method compared with the state-of-the-art TF transforms.

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.

A union of spectrum and cepstrum: Recipe for thin-bed delineation

2019 ◽  
Vol 38 (4) ◽  
pp. 298-305
Author(s):  
Prashant Kumar Mishra ◽  
Sanjai Kumar Singh ◽  
Pradip Kumar Chaudhuri
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Dominant Frequency ◽  
Resolution Limit ◽  
Bandwidth Extension ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Cepstral Analysis ◽  
Resultant Data

The resolution limit of seismic data is an intricate issue that depends not only on frequency and data quality (signal-to-noise ratio) but also on the tools and technology used to analyze seismic response. In this context, the subject of thin-bed delineation is extremely significant for coal-laminated (causing large acoustic impedance contrasts) clastic sequences of the Western Onshore Basin, India. Most of the clastic reservoirs in the area are of subseismic resolution (below 10 m in thickness) due to the low dominant frequency available in seismic data (19–35 Hz). This is where improving seismic resolution is essential for a detailed structural and stratigraphic interpretation. We have implemented a modified workflow with which, by using state-of-the-art techniques of time-frequency decomposition and cepstral analysis, significant seismic bandwidth extension has been achieved. This in turn yields improved vertical resolution of the seismic data with better geologic interpretability. Our approach is named the “syn-cepstral method” after its two integral constituents — synchrosqueezing transform and cepstral analysis. Applying the syn-cepstral method produces better well-to-seismic ties and resolves additional events in comparison to the original seismic data. The validity of syn-cepstral methodology has been demonstrated by 1D and 2D modeling studies followed by application to a 3D seismic data set from the Western Onshore Basin of India. The improvement in thin-bed delineation arising from the increased bandwidth of the resultant data has been validated by well-to-seismic ties and amplitude map interpretation. Thus, while thin clastic reservoir beds in the logs show no discernible presence in the original seismic data, upon application of the syn-cepstral method, the resultant seismic data show improved interpretability of these units.

Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V117-V124 ◽  
Author(s):  
Mohammad Amir Nazari Siahsar ◽  
Saman Gholtashi ◽  
Amin Roshandel Kahoo ◽  
Hosein Marvi ◽  
Alireza Ahmadifard
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Real Data ◽  
Seismic Signal ◽  
Oil Field ◽  
Low Rank ◽  
Classical Formula ◽  
Sparse Decomposition ◽  
Time Frequency ◽  
Frequency Representation

Attenuation of random noise is a major concern in seismic data processing. This kind of noise is usually characterized by random oscillation in seismic data over the entire time and frequency. We introduced and evaluated a low-rank and sparse decomposition-based method for seismic random noise attenuation. The proposed method, which is a trace by trace algorithm, starts by transforming the seismic signal into a new sparse subspace using the synchrosqueezing transform. Then, the sparse time-frequency representation (TFR) matrix is decomposed into two parts: (a) a low-rank component and (b) a sparse component using bilateral random projection. Although seismic data are not exactly low-rank in the sparse TFR domain, they can be assumed as being of semi-low-rank or approximately low-rank type. Hence, we can recover the denoised seismic signal by minimizing the mixed [Formula: see text] norms’ objective function by considering the intrinsically semilow-rank property of the seismic data and sparsity feature of random noise in the sparse TFR domain. The proposed method was tested on synthetic and real data. In the synthetic case, the data were contaminated by random noise. Denoising was carried out by means of the [Formula: see text] classical singular spectrum analysis (SSA) and [Formula: see text] deconvolution method for comparison. The [Formula: see text] deconvolution and the classical [Formula: see text] SSA method failed to properly reduce the noise and to recover the desired signal. We have also tested the proposed method on a prestack real data set from an oil field in the southwest of Iran. Through synthetic and real tests, the proposed method is determined to be an effective, amplitude preserving, and robust tool that gives superior results over classical [Formula: see text] SSA as conventional algorithm for denoising seismic data.

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