scholarly journals Application of the Segmented Correlation Technology in Seismic Communication with Morse Code

2021 ◽  
Vol 11 (4) ◽  
pp. 1947
Author(s):  
Yuanjie Jiang ◽  
Yuda Chen ◽  
Ruyun Tian ◽  
Longxu Wang ◽  
Shixue Lv ◽  
...  
Keyword(s):  
Seismic Data ◽  
Communication System ◽  
Seismic Waves ◽  
Signal To Noise Ratio ◽  
Signal Extraction ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Target Signal ◽  
Morse Code ◽  
Widespread Adoption

Seismic communication might promise to revolutionize the theory of seismic waves. However, one of the greatest challenges to its widespread adoption is the difficulty of signal extraction because the seismic waves in the vibration environments, such as seas, streets, city centers and subways, are very complex. Here, we employ segmented correlation technology with Morse code (SCTMC), which extracts the target signal by cutting the collected data into a series of segments and makes these segments cross-correlate with the decoded signal to process the collected data. To test the effectiveness of the technology, a seismic communication system composed of vibroseis sources and geophones was built in an environment full of other vibration signals. Most notably, it improves the signal-to-noise ratio (SNR), extending the relay distance and suppressing other vibration signals by using technology to deal with seismic data generated by the system.

scholarly journals Image multiplexing using residue number system coding over MIMO-OFDM communication system

2019 ◽  
Vol 9 (6) ◽  
pp. 4815
Author(s):  
Mohamed Ibrahim Youssif ◽  
Amr ElSayed Emam ◽  
Mohamed Abd ElGhany
Keyword(s):  
Image Quality ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Number System ◽  
Residue Number System ◽  
Signal To Noise ◽  
Residue Number ◽  
Mimo Ofdm ◽  
Noise Ratio

<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>

scholarly journals Study on the Velocity Analysis Method of Low SNR Seismic Data

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang
Keyword(s):  
Seismic Data ◽  
Fresnel Zone ◽  
Signal To Noise Ratio ◽  
Normal Wave ◽  
Curvature Radius ◽  
Velocity Spectrum ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Zero Offset

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

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