Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network

Lan, Tianwei; Zeng, Zhaofa; Han, Liguo; Zeng, Jingwen

doi:10.3390/app13010655

Cited by 5 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet transform [25,26] is a powerful signal processing tool and a mathematical method widely used in various fields such as signal processing and seismic exploration. It decomposes and analyzes signals at different scales and positions using wavelet func- Then, the signal enters the differential amplification circuit, which divides the signal into two paths with different DC biases, opposite directions, and no phase deviation.…”

Section: Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

Liu,

Zhang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO2) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO2 is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO2 concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.

show abstract

Section: Wavelet Transformmentioning

confidence: 99%

Section: Wavelet Transformmentioning

confidence: 99%

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

Liu,

Zhang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Currently, common methods for identifying effective microseismic signals include parametric analysis, waveform analysis, wavelet transform, pattern recognition, etc. But most of these methods require manual processing, which is subject to the unstable classification efficiency and affected by the prior experience of processors [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Shen,

Wang,

Zeng

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

show abstract

Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression

Liu

Chen

2023

Acta Geophys.

View full text Add to dashboard Cite

Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network

Cited by 5 publications

References 19 publications

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression

Contact Info

Product

Resources

About