Multicomponent Time-Frequency Noise Attenuation of Microseismic Data

Abstract The attenuation of low-frequency broadband noise in a light, small form-factor is an intractable challenge. In this paper, a new technology is presented which employs the highly efficient visco-thermal loss mechanism of a micro-perforated plate (MPP) and successfully lowers its frequency response by combining it with decorated membrane resonators (DMR). Absorption comes from the membranes but primarily from the MPP, as the motion of the two membranes causes a pressure differential across the MPP creating airflow through the perforations. This combination of DMR and MPP has led to the Segmented Membrane Sound Absorber (SeMSA) design, which is extremely effective at low-frequency broadband sound absorption and which can achieve this at deep sub-wavelength thicknesses. The technology is compared to other absorbers to be found in the literature and the SeMSA outperforms them all in either the 20–1000 Hz or 20–1200 Hz range for depths of up to 120 mm. This was verified through analytical, finite element and experimental analyses.

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White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network

Geophysics ◽

10.1190/geo2018-0635.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. V307-V317 ◽

Cited By ~ 2

Author(s):

Hao Wu ◽

Bo Zhang ◽

Tengfei Lin ◽

Fangyu Li ◽

Naihao Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

White Noise ◽

Seismic Data ◽

Prior Information ◽

Training Procedure ◽

Noise Attenuation ◽

Variational Mode Decomposition ◽

Time Frequency ◽

Mode Decomposition

Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.

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Threshold Noise Reduction Research of Improved EEMD Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.226-228.237 ◽

2012 ◽

Vol 226-228 ◽

pp. 237-240 ◽

Cited By ~ 1

Author(s):

Mei Jun Zhang ◽

Hao Chen ◽

Chuang Wang ◽

Qing Cao

Keyword(s):

Noise Reduction ◽

Signal To Noise Ratio ◽

Frequency Noise ◽

Signal To Noise ◽

Time Frequency ◽

Noise Ratio ◽

Reduction Effect ◽

Eemd Method ◽

Simulation Signal ◽

Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

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