scholarly journals Signal to noise ratio (SNR) comparison for lockin thermographic data processing methods in CFRP specimen

2010 ◽  
Author(s):  
F.J. Madruga ◽  
P. Albendea ◽  
C. Ibarra-Castanedo ◽  
J.M. López-Higuera
Keyword(s):  
Data Processing ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Processing Methods ◽  
Noise Ratio ◽  
Data Processing Methods

scholarly journals Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

2019 ◽  
Author(s):  
Kukka-Maaria Kohonen ◽  
Pasi Kolari ◽  
Linda M. J. Kooijmans ◽  
Huilin Chen ◽  
Ulli Seibt ◽  
...  
Keyword(s):  
Data Processing ◽  
Eddy Covariance ◽  
Signal To Noise Ratio ◽  
Carbonyl Sulfide ◽  
Lag Time ◽  
Maximum Effect ◽  
Signal To Noise ◽  
Flux Data ◽  
Flux Measurements ◽  
Noise Ratio

Abstract. Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are growing in popularity with the recent development in using COS to estimate gross photosynthesis at the ecosystem scale. Flux data intercomparison would benefit from standardized protocols for COS flux data processing. In this study, we analyze how various data processing steps affect the final flux and provide a method for gap-filling COS fluxes. Different methods for determining the lag time between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 12 % difference in the cumulative COS flux. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the lag time was determined from maximizing the covariance between COS and w. We recommend using a combination of COS and carbon dioxide (CO2) lag times in determining the COS flux, depending on the flux magnitude compared to the detection limit of each averaging period. Different high frequency spectral corrections had a maximum effect of 10 % on COS flux calculations and different detrending methods only 1.2 %. Relative total uncertainty was more than five times higher for low COS fluxes (absolute flux lower than 3 pmol m−2 s−1) than for low CO2 fluxes (lower than 1.5 μmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, and the low signal-to-noise ratio of COS fluxes that is similar to methane, we recommend a combination of CO2 and methane flux processing protocols for COS EC fluxes.

Research on processing methods to improve the signal-to-noise ratio of a magnetoacoustic signal

2020 ◽  
Vol 60 ◽  
pp. 101955
Author(s):  
Shunqi Zhang ◽  
Ren Ma ◽  
Xiaoqing Zhou ◽  
Tao Yin ◽  
Zhipeng Liu
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Processing Methods ◽  
Noise Ratio

scholarly journals An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

2020 ◽  
Author(s):  
Siming He ◽  
Jian Guan ◽  
Xiu Ji ◽  
Hui Wang ◽  
Yi Wang
Keyword(s):  
Data Processing ◽  
Spread Spectrum ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Induced Polarization ◽  
High Resistivity ◽  
Observation System ◽  
Identification Algorithm ◽  
Signal To Noise ◽  
Noise Ratio

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The traditional correlation identification (TCI) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the formula of the TCI algorithm is used for identifying the frequency response of the observation system. Even when the signal to noise ratio (SNR) is −37.5 dB, this ECI algorithm can still be able to keep 3.0 % relative error. Experiments on both synthetic and real SSIP data show that the ECI algorithm can not only suppress the background noise but also better preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.

scholarly journals Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide

2020 ◽  
Vol 13 (7) ◽  
pp. 3957-3975
Author(s):  
Kukka-Maaria Kohonen ◽  
Pasi Kolari ◽  
Linda M. J. Kooijmans ◽  
Huilin Chen ◽  
Ulli Seibt ◽  
...  
Keyword(s):  
Data Processing ◽  
Eddy Covariance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Time Lag ◽  
Carbonyl Sulfide ◽  
Signal To Noise ◽  
Flux Measurements ◽  
Noise Ratio ◽  
Processing Steps

Abstract. Carbonyl sulfide (COS) flux measurements with the eddy covariance (EC) technique are becoming popular for estimating gross primary productivity. To compare COS flux measurements across sites, we need standardized protocols for data processing. In this study, we analyze how various data processing steps affect the calculated COS flux and how they differ from carbon dioxide (CO2) flux processing steps, and we provide a method for gap-filling COS fluxes. Different methods for determining the time lag between COS mixing ratio and the vertical wind velocity (w) resulted in a maximum of 15.9 % difference in the median COS flux over the whole measurement period. Due to limited COS measurement precision, small COS fluxes (below approximately 3 pmol m−2 s−1) could not be detected when the time lag was determined from maximizing the covariance between COS and w. The difference between two high-frequency spectral corrections was 2.7 % in COS flux calculations, whereas omitting the high-frequency spectral correction resulted in a 14.2 % lower median flux, and different detrending methods caused a spread of 6.2 %. Relative total uncertainty was more than 5 times higher for low COS fluxes (lower than ±3 pmol m−2 s−1) than for low CO2 fluxes (lower than ±1.5 µmol m−2 s−1), indicating a low signal-to-noise ratio of COS fluxes. Due to similarities in ecosystem COS and CO2 exchange, we recommend applying storage change flux correction and friction velocity filtering as usual in EC flux processing, but due to the low signal-to-noise ratio of COS fluxes, we recommend using CO2 data for time lag and high-frequency corrections of COS fluxes due to the higher signal-to-noise ratio of CO2 measurements.

scholarly journals Application of a New Wavelet Threshold Method in Unconventional Oil and Gas Reservoir Seismic Data Denoising

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Guxi Wang ◽  
Ling Chen ◽  
Si Guo ◽  
Yu Peng ◽  
Ke Guo
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Three Dimensional ◽  
Threshold Function ◽  
Signal To Noise ◽  
Gas Reservoir ◽  
Seismic Processing ◽  
Noise Ratio

Seismic data processing is an important aspect to improve the signal to noise ratio. The main work of this paper is to combine the characteristics of seismic data, using wavelet transform method, to eliminate and control such random noise, aiming to improve the signal to noise ratio and the technical methods used in large data systems, so that there can be better promotion and application. In recent years, prestack data denoising of all-digital three-dimensional seismic data is the key to data processing. Contrapose the characteristics of all-digital three-dimensional seismic data, and, on the basis of previous studies, a new threshold function is proposed. Comparing between conventional hard threshold and soft threshold, this function not only is easy to compute, but also has excellent mathematical properties and a clear physical meaning. The simulation results proved that this method can well remove the random noise. Using this threshold function in actual seismic processing of unconventional lithologic gas reservoir with low porosity, low permeability, low abundance, and strong heterogeneity, the results show that the denoising method can availably improve seismic processing effects and enhance the signal to noise ratio (SNR).

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