Improved CEEMDAN–wavelet transform de-noising method and its application in well logging noise reduction

Zhang, Jingxia; Guo, Yinghai; Shen, Yu‐Lin; Zhao, Difei; Li, Mi

doi:10.1088/1742-2140/aaa076

Cited by 36 publications

(14 citation statements)

References 32 publications

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“…The method used here has evolved from the original Empirical Mode Decomposition (EMD) method proposed by Huang et al (1998) using Hilbert-Huang transform to overcome some initial problems like mode splitting. The method has been found effective in separating the nonlinear trends in many climate time series (Antico et al, 2014;Franzke, 2009Franzke, , 2010Franzke & Woollings, 2011;Guan et al, 2018;Martinez-Lopez et al, 2018;Qian et al, 2011;Rottler et al, 2020;Wu et al, 2011;Zhang et al, 2018). By applying CEMD, the secular trend of all the observed and model simulated time series is obtained as the residual, after removing the oscillatory components from the time series, and difference in magnitudes between the two time period represents the climate change signal under global warming.…”

Section: The Nonlinear Trend and Estimation Secular Climate Changementioning

confidence: 99%

Large Sensitivity of Simulated Indian Summer Monsoon Rainfall (ISMR) to Global Warming: Implications of ISMR Projections

et al. 2021

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With the water and food security of one-sixth of the world's population strongly tied to it, the future of the seasonal mean Indian Summer Monsoon Rainfall (ISMR) is of serious concern for policymakers and farmers alike. Unmistakable evidence of the increasing trend of global mean temperature (T g) attributed to anthropogenic greenhouse forcing with high confidence (Houghton et al., 2001; IPCC, 2007, 2014) together with a similar increasing trend of annual mean temperature over the Indian monsoon region (Kothawale & Rupa Kumar, 2005; Ross et al., 2018) leading to enhanced moisture content over the region and in landocean temperature contrast (Sutton et al., 2007), a forced increasing trend of ISMR is reasonable to expect at longer time scales. While an increasing trend of ISMR may be expected due primarily to about 7% increase in the precipitable water per K increase in mean temperature (Mears et al., 2007) also referred to as the Clausius-Clapeyron (CC) scaling, climate models simulate only 1%-3% increase in global mean precipitation/K (Held & Soden, 2006; Wentz et al., 2007). Although the moisture content in the atmosphere increases according to the CC scaling with increase of temperature, regional precipitation like the ISMR is also influenced by dynamics in addition to thermodynamic

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Section: The Nonlinear Trend and Estimation Secular Climate Changementioning

confidence: 99%

Large Sensitivity of Simulated Indian Summer Monsoon Rainfall (ISMR) to Global Warming: Implications of ISMR Projections

et al. 2021

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“…In order to evaluate the denoising performance of the method quantitatively, the root mean square error (RMSE), SNRout, and correlation coe cient (R) are adopted in this article [32]:…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Zhang

Tian

Banerjee

et al. 2019

Mathematical Problems in Engineering

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Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

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“…A new noiseassisted data analysis approach that can resolve the mode mixing disadvantage of the original EMD, which was first introduced by (Huang et al 1998;Wu and Huang 2004). ICEEMDAN uses the observed signal for IMF sifting to solve EEMD and its variant problems has been studied in the literature (Zhang et al 2018;Xu and Ren 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

Tayyab

Dong

Sibtain

et al. 2021

Preprint

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In this study, 03 ensemble and decomposition methods (DMs) i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) were coupled artificial intelligence and machine learning based method AI-ML, i.e., multilayer perceptron (MLP), support vector regression (SVR) to develop 06 fundamental hybrid models to predict streamflow with one-month lead time. Developed models in this study were categorized into runoff models (RMs) and rainfall-runoff models (RRMs). Results indicated that (i) among standalone models (SMs), support vector regression (SVR) performs better than multilayer perceptron (MLP), (ii) decomposition methods (DMs) have ability to improve the accuracy rate of the standalone models (SMs) and, (iii) rainfall runoff models (RRMs) have shown great accuracy throughout the investigation as compared to runoff models (RMs). To compare model performances flow-hydrographs (FHG) were generated, 05 performance evaluation criteria (PEC) were used to quantify the model precision. Two step verification method i.e., extreme value analysis (EVA) and least value analysis (LVA) approaches were proposed to verify the performances. Among all developed hybrid models (HMs), i.e., EMD- (MLP, SVR), EEMD- (MLP, SVR) and ICEEMDAN- (MLP, SVR), rainfall-runoff ICEEMDAN-(SVR) model was selected as optimal model with MAE (59.56), RMSE (91.82), R (0.97) MAPE (8.75), and NSEC (0.97) for Mangla watershed, Pakistan.

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Improved CEEMDAN–wavelet transform de-noising method and its application in well logging noise reduction

Cited by 36 publications

References 32 publications

Large Sensitivity of Simulated Indian Summer Monsoon Rainfall (ISMR) to Global Warming: Implications of ISMR Projections

Large Sensitivity of Simulated Indian Summer Monsoon Rainfall (ISMR) to Global Warming: Implications of ISMR Projections

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Optimum Application of Hybrid Data Driven Models With Two Step Verification Method at Mangla Watershed, Pakistan

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