Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network

Zhang, Hang; Ma, Chunchi; Pazzi, Veronica; Zou, Yulin

doi:10.3390/app10186621

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…Nevertheless, the large convolution kernel increases computational time, limiting the depth of the neural network. Thus, all convolution/deconvolution layers have three kernels [52]. The batch normalization method is used for training deep neural networks by normalizing the contributions made by each mini-batch.…”

Section: ) Our Proposed Approachmentioning

confidence: 99%

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

et al. 2022

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In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

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Section: ) Our Proposed Approachmentioning

confidence: 99%

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

et al. 2022

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“…In recent years, deep learning technology has continued to evolve, and it has achieved excellent results in image classification, image noise reduction, speech noise reduction, and other fields [15][16][17][18][19][20][21][22] with its self-adaptive feature learning and strong classification ability. Therefore, geophysicists began to use deep learning technology to self-adaptively reduce the noise in the co-frequency band in seismic signals.…”

Section: Introductionmentioning

confidence: 99%

Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block

et al. 2023

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To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic co-band denoising model Atrous Residual Dense Block U-Net (ARDU), which uses a U-shaped convolutional neural network (U-Net) as a basic framework and combines atrous convolution and the residual dense block (RDB). In the ARDU model, atrous convolution is connected with residual dense blocks to form the feature extraction unit of the model encoder. Among them, the residual dense blocks can deepen the network’s depth and enhance the feature extraction ability of the network on the premise of mitigating the gradient-vanishing and gradient-exploding problem. Atrous convolution can enlarge receptive fields, reduce waveform distortion, and protect effective signals without increasing network parameters. To test the denoising performance of the ARDU model, the Stanford Global Seismic dataset was used to construct a training set and a test set and the model was trained and tested on it. The experimental results of the ARDU model for different types of seismic co-band noise showed that this model can effectively remove seismic co-band noise, protect effective signals, improve the SNR of seismic signals, and enhance the quality of seismic signals. To further verify the denoising effect of the model, this model was compared with the wavelet threshold denoising U-Net model and the denoising residual dense block (DnRDB) model, and the results showed that the ARDU model has the best SNR, r (correlation coefficient), and root-mean-square error (RMSE) and the least distortion of the seismic signal waveform.

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“…In the context of hydraulic stimulation monitoring, CNNs have been used to detect microseismic events recorded by both surface arrays (Consolvo & Thornton, 2020) and downhole DAS (Binder & Tura, 2020; Stork et al ., 2020; Huot et al ., 2021). In order to process and denoise microseismic data, convolutional encoder–decoder networks (Zhang et al ., 2020), deep dual‐tasking networks (Zhang et al ., 2021) and unsupervised learning for signal feature extraction (Zhang & van der Baan, 2020) have all been successfully implemented. Machine learning algorithms are also useful for automatically detecting signals recorded by fibre optics in the near surface.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network‐based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data

Liu

Huff

Luo

et al. 2022

Geophysical Prospecting

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This study presents a workflow of using a convolutional neural network to automatically classify microseismic events originating from a more productive oil and gasbearing formation (the Eagle Ford Shale), as compared to events originating in less productive formations (the Austin Chalk and Buda Limestone). These microseismic events occur due to hydraulic stimulation and are recorded by fibre optic-distributed acoustic sensing measurements from a horizontal monitoring well. The convolutional neural network is trained to recognize guided wave energy in distributed acoustic sensing seismograms, since microseismic events originating within or close to a lowvelocity reservoir (such as the Eagle Ford) generate significant guided wave energy. The training of convolutional neural network is conducted using synthetic seismograms overlain with real noise profiles from field data. Field events with guided waves are then classified by the convolutional neural network as occurring within or close to the Eagle Ford, while events without guided waves are classified as occurring far outside the Eagle Ford. Noise attenuation steps (including a bandpass filter, median filter and non-local means filter) are implemented to increase the signal-to-noise ratio of the field data and improve the classification accuracy. The accuracy of the convolutional neural network is measured by comparison with labels of the events determined by human inspection of guided wave presence. We also evaluate the impact of different network architectures and noise attenuation methods on classification accuracy. The accuracy and F1-score of the final classification are both 0.85 when tested on a high signal-to-noise ratio subset of the field data. On the complete dataset including low signal-to-noise ratio events, an accuracy and F1-score of 0.80 are achieved. These results demonstrate the high effectiveness of the trained convolutional neural network on guided wave detection and classification of microseismic events inside and outside the Eagle Ford formation.

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Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network

Cited by 17 publications

References 27 publications

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block

Convolutional neural network‐based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data

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