A data-driven multidimensional signal-noise decomposition approach for GPR data processing

Chen, Chih-Sung; Jeng, Yih

doi:10.1016/j.cageo.2015.09.017

Cited by 14 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 6-month-lead prediction results of the hybrid model gave slightly better forecasts than those of the National Centers for Environmental Prediction (NCEP), and the 12-month-lead prediction had similar predictive power to that of shorter-lead-time predictions 31 . In 2019, Yuan et al proposed an effective neural network model, EEMD-ConvLSTM, which was based on ConvLSTM and EEMD 32 , to predict the North Atlantic oscillation (NAO) index. The experimental results showed that EEMD-ConvLSTM not only had the highest reliability according to the evaluation metrics but could also better capture the variation trends of the NAO index data 33 .…”

mentioning

confidence: 99%

Temporal Convolutional Networks for the Advance Prediction of ENSO

Yan

Wang

et al. 2020

Sci Rep

196

View full text Add to dashboard Cite

el niño-Southern oscillation (enSo), which is one of the main drivers of earth's inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of enSo is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Niño 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Niño 3.4 index and SOI mix many low-and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (eeMD-TCN) hybrid approach, which decomposes the highly variable Niño 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Niño 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984-2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Niño 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Niño 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and eeMD-LStM methods showed that the eeMD-tcn provides the best overall prediction of both the Niño 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. this result means that the tcn approach performs well in the advance prediction of enSo and will be of great guiding significance in studying it. El Niño-Southern Oscillation (ENSO) is a sea surface temperature and air pressure shock that occurs in the equatorial Pacific Ocean 1. It is a sea-air interaction phenomenon at low latitudes, which is manifested by the El Niño-La Niña transition in the ocean and the "southern oscillation" (SO) in the atmosphere. El Niño refers to the warming phenomenon that occurs in the tropical Pacific every 2-7 years, while the cooling phenomenon is called La Niña 2. El Niño and La Niña are closely related to SO, which is the inverse-change phenomenon of the pressure field in the tropical east Pacific and tropical east Indian Ocean. The ENSO is one of the main drivers of Earth's inter-annual climate variability. It often causes a wide range of climate anomalies, triggering a variety of meteorological disasters and causing huge economic property damage in affected areas 3...

show abstract

mentioning

confidence: 99%

Temporal Convolutional Networks for the Advance Prediction of ENSO

Yan

Wang

et al. 2020

Sci Rep

196

View full text Add to dashboard Cite

show abstract

“…The more rigorous the stoppage criterion is set, the more IMFs are obtained. However, too many IMFs are not suggested because it could make the resulting IMFs approaching harmonic functions lacking physical meaning [44]. More details regarding the decomposition method can be found in various publications [40,43,[45][46][47].…”

Section: A Data Decompositionmentioning

confidence: 99%

Prefrontal Brain Electrical Activity and Cognitive Load Analysis Using a Non-linear and Non-Stationary Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

“…As for other methods such as Bonar and Sacchi proposed a diffusion filter (Bonar and Sacchi, 2012), Chen (2017aChen ( , 2017b proposed dictionary learning methods, and Amani et al(2017) proposed a 3D block matching method using the coherency characteristic of the seismic signal to suppress the random noise. The application of generalized regression neural networks to seismic data filtering proved by Djarfour et al (2014) and Chen and Jeng (2015) demonstrates the possibility of applying a datadriven nonlinear filtering scheme in processing ground penetrating radar data.…”

Section: Introductionmentioning

confidence: 98%

Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding

Wen

Lin

2020

Journal of Vibration and Control

View full text Add to dashboard Cite

Unattended object detection systems have seen full applications in military surveillance, object recognition, and intrusion prevention. When applied to actual work scenarios, these systems have problems such as low recognition accuracy, low positioning accuracy, and weak detection effect of distant objects. Obtainment of enough feature information concerning the effective signals is critical to target recognition. This work focuses on interference in seismic signals and the way to store the feature information of effective signals. First, the authors analyzed the frequency and attenuation characteristics of seismic waves of typical target sites, in which the Rayleigh wave is suitable for the detection of the energy of seismic signals produced by human targets and vehicles. As seismic signals are low-frequency waves, the authors researched the performance of the empirical mode decomposition method and the wavelet thresholding method in denoising seismic signals, and an improved empirical mode decomposition-wavelet threshold denoising method is proposed. The test result shows that the improved denoising method can effectively remove noise in seismic signals and preserve the effective signals of the target.

show abstract

A data-driven multidimensional signal-noise decomposition approach for GPR data processing

Cited by 14 publications

References 30 publications

Temporal Convolutional Networks for the Advance Prediction of ENSO

Temporal Convolutional Networks for the Advance Prediction of ENSO

Prefrontal Brain Electrical Activity and Cognitive Load Analysis Using a Non-linear and Non-Stationary Approach

Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding

Contact Info

Product

Resources

About