scholarly journals Desert seismic signal denoising by 2D compact variational mode decomposition

2019 ◽  
Vol 16 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Yue Li ◽  
Linlin Li ◽  
Chao Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Support Functions ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Effective Signal

Abstract Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.

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.

scholarly journals Seismic Random Noise Attenuation Method Based on Variational Mode Decomposition and Correlation Coefficients

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 280 ◽  
Author(s):  
Yaping Huang ◽  
Hanyong Bao ◽  
Xuemei Qi
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Seismic data is easily affected by random noise during field data acquisition. Therefore, random noise attenuation plays an important role in seismic data processing and interpretation. According to decomposition characteristics of seismic signals by using variational mode decomposition (VMD) and the constraint conditions of correlation coefficients, this paper puts forward a method for random noise attenuation in seismic data, which is called variational mode decomposition correlation coefficients VMDC. Firstly, the original signals were decomposed into intrinsic mode functions (IMFs) with different characteristics by VMD. Then, the correlation coefficients between each IMF and the original signal were calculated. Next, based on the differences among correlation coefficients of effective signals and random noise as well as the original signals, the corresponding treatment was carried out, and the effective signals were reconstructed. Finally, the random noise attenuation was realized. After adding random noise to simple sine signals and the synthetic seismic record, the improved complementary ensemble empirical mode decomposition (ICEEMD) and VMDC were used for testing. The testing results indicate that the proposed VMDC has better random noise attenuation effects. It was also used in real-world seismic data noise attenuation. The results also show that it could effectively improve the signal-to-noise ratio (SNR) of seismic data and could provide high-quality basic data for further interpretation of seismic data.

Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm

2018 ◽  
Vol 22 (7) ◽  
pp. 1519-1530 ◽  
Author(s):  
Hui Li ◽  
Tengfei Bao ◽  
Chongshi Gu ◽  
Bo Chen
Keyword(s):  
Spectrum Analysis ◽  
Damage Identification ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Low Frequency Noise ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure

Extraction of the vibration characteristics of a flood discharge structure under the influence of intensive background noise is one of the main challenges in vibration-based damage identification. A novel algorithm called normalized central frequency difference spectrum is proposed to improve the variational mode decomposition algorithm for high-frequency noise filtering. To eliminate the errors caused by end effect, the waveform matching extension algorithm is used to further improve the variational mode decomposition. However, the vibration signal is still coupled in low-frequency noise. Thereupon, the singular spectrum analysis algorithm is applied to filter the low-frequency noise. In this article, a simulated signal and the measured signals from a dam model are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high denoising precision. In addition, this algorithm can offer clues for damage identification and localization of a flood discharge structure.

An effective data processing workflow for broadband single-sensor single-source land seismic data

2020 ◽  
Vol 39 (6) ◽  
pp. 401-410
Author(s):  
Simon Cordery
Keyword(s):  
Seismic Data ◽  
Amplitude Spectrum ◽  
Low Frequency ◽  
Residual Effects ◽  
Signal Spectrum ◽  
Frequency Noise ◽  
Single Source ◽  
Noise Characteristics ◽  
Single Sensor ◽  
Data Adaptive

Examples of raw and processed broadband single-sensor single-source land seismic data acquired in the Middle East region have been found to be significantly noisy, and very low-frequency signal has been either missing or unrecoverable. In response, an effective and pragmatic processing workflow has been developed that substantially improves the quality of the final processed data, to the extent where we can say that original survey objectives can be met. The new workflow includes early deterministic deconvolution for a number of filtering effects in the recorded signal wavelet, with the aim of flattening the signal wavelet amplitude spectrum over the vibroseis sweep frequencies and zeroing the wavelet phase. This includes the key innovative step of converting the recorded particle motion to that of the vibroseis far-field signal, those respectively being particle acceleration and particle displacement. This significantly boosts low-frequency amplitudes relative to higher frequencies such that it becomes possible to deterministically compensate for earth's absorption using a large gain limit with less concern for overamplifying high-frequency noise. An application of a source designature compensates for the nonflat design of the pilot sweep, further increasing signal amplitudes over the low-frequency ramp-up portion of the sweep. With the flattened signal spectrum, it is possible to better assess trace noise characteristics across the full bandwidth and perform better QC for its removal. Subsequent statistical deconvolution becomes more of a correction for residual effects on the signal wavelet, and the use of trace supergrouping further mitigates the effect of noise on statistical deconvolution and other data-adaptive processes.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

Investigation of Low-Frequency Noise Characteristics in Gated Schottky Diodes

2021 ◽  
Vol 42 (3) ◽  
pp. 442-445
Author(s):  
Dongseok Kwon ◽  
Wonjun Shin ◽  
Jong-Ho Bae ◽  
Suhwan Lim ◽  
Byung-Gook Park ◽  
...  
Keyword(s):  
Schottky Diodes ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Noise Characteristics
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