Attenuation of random noise using denoising convolutional neural networks

2019 ◽  
Vol 7 (3) ◽  
pp. SE269-SE280
Author(s):  
Xu Si ◽  
Yijun Yuan ◽  
Tinghua Si ◽  
Shiwen Gao
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Random Noise ◽  
Training Data ◽  
Prediction Errors ◽  
Network Learning ◽  
Deep Network ◽  
Residual Image ◽  
And Training

Random noise often contaminates seismic data and reduces its signal-to-noise ratio. Therefore, the removal of random noise has been an essential step in seismic data processing. The [Formula: see text]-[Formula: see text] predictive filtering method is one of the most widely used methods in suppressing random noise. However, when the subsurface structure becomes complex, this method suffers from higher prediction errors owing to the large number of different dip components that need to be predicted. Here, we used a denoising convolutional neural network (DnCNN) algorithm to attenuate random noise in seismic data. This method does not assume the linearity and stationarity of the signal in the conventional [Formula: see text]-[Formula: see text] domain prediction technique, and it involves creating a set of training data that are obtained by data processing, feeding the neural network with the training data obtained, and deep network learning and training. During deep network learning and training, the activation function and batch normalization are used to solve the gradient vanishing and gradient explosion problems, and the residual learning technique is used to improve the calculation precision, respectively. After finishing deep network learning and training, the network will have the ability to separate the residual image from the seismic data with noise. Then, clean images can be obtained by subtracting the residual image from the raw data with noise. Tests on the synthetic and real data demonstrate that the DnCNN algorithm is very effective for random noise attenuation in seismic data.

SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U87-U98
Author(s):  
Jing Zheng ◽  
Jerry M. Harris ◽  
Dongzhuo Li ◽  
Badr Al-Rumaih
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Arrival Time ◽  
Time Lapse ◽  
S Phase ◽  
Training Data ◽  
Phase Detector ◽  
Phase Detection ◽  
Seismic Data Processing

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

scholarly journals METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Mykola Kulyk ◽  
Sergiy Dmitriev ◽  
Oleksandr Yakushenko ◽  
Oleksandr Popov
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Training Data ◽  
Turbine Engines ◽  
Data Sets ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Wide Range ◽  
And Training

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

scholarly journals Generative Adversarial Network Performance in Low-Dimensional Settings

2021 ◽  
Vol 126 ◽  
Author(s):  
Felix Jimenez ◽  
Amanda Koepke ◽  
Mary Gregg ◽  
Michael Frey
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Training Data ◽  
High Dimensional ◽  
Generative Adversarial Network ◽  
Low Dimensions ◽  
Target Distribution ◽  
Adversarial Network ◽  
Low Dimensional ◽  
And Training

A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to createexamples that faithfully reproduce a target distribution. GANs have recently had particular success in applications involvinghigh-dimensional distributions in areas such as image processing. Little work has been reported for low dimensions, where properties of GANs may be better identified and understood. We studied GAN performance in simulated low-dimensional settings, allowing us totransparently assess effects of target distribution complexity and training data sample size on GAN performance in a simpleexperiment. This experiment revealed two important forms of GAN error, tail underfilling and bridge bias, where the latter is analogousto the tunneling observed in high-dimensional GANs.

scholarly journals Light Actuation Based On Facial Mood Recognition

2020 ◽  
Vol 9 (05) ◽  
pp. 25052-25056
Author(s):  
Abhi Kadam ◽  
Anupama Mhatre ◽  
Sayali Redasani ◽  
Amit Nerurkar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Facial Emotion ◽  
Specific Data ◽  
Affective Meaning ◽  
Deep Learning Model ◽  
And Training

Current lighting technologies extend the options for changing the appearance of rooms and closed spaces, as such creating ambiences with an affective meaning. Using intelligence, these ambiences may instantly be adapted to the needs of the room’s occupant(s), possibly improving their well-being. In this paper, we set actuate lighting in our surrounding using mood detection. We analyze the mood of the person by Facial Emotion Recognition using deep learning model such as Convolutional Neural Network (CNN). On recognizing this emotion, we will actuate lighting in our surrounding in accordance with the mood. Based on implementation results, the system needs to be developed further by adding more specific data class and training data.

scholarly journals Convolutional Neural Network-Based Discriminator for Outlier Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fahad Alharbi ◽  
Khalil El Hindi ◽  
Saad Al Ahmadi ◽  
Hussien Alsalamn
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Systematic Approach ◽  
Data Augmentation ◽  
Random Noise ◽  
Training Data ◽  
Human Errors ◽  
Machine Learning Methods ◽  
Benchmark Datasets ◽  
Trusted Data

Noise in training data increases the tendency of many machine learning methods to overfit the training data, which undermines the performance. Outliers occur in big data as a result of various factors, including human errors. In this work, we present a novel discriminator model for the identification of outliers in the training data. We propose a systematic approach for creating training datasets to train the discriminator based on a small number of genuine instances (trusted data). The noise discriminator is a convolutional neural network (CNN). We evaluate the discriminator’s performance using several benchmark datasets and with different noise ratios. We inserted random noise in each dataset and trained discriminators to clean them. Different discriminators were trained using different numbers of genuine instances with and without data augmentation. We compare the performance of the proposed noise-discriminator method with seven other methods proposed in the literature using several benchmark datasets. Our empirical results indicate that the proposed method is very competitive to the other methods. It actually outperforms them for pair noise.

Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. B403-B417 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Danping Cao ◽  
Yihuai Lou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Time Window ◽  
Training Data ◽  
Fault Surface ◽  
Unique Object ◽  
Segmentation Methods ◽  
Convolution Filters ◽  
Waveform Pattern

The seismic horizon is a critical input for the structure and stratigraphy modeling of reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for seismic data in which the lateral continuity of reflections is interrupted by faults and unconformities. The process of seismic horizon interpretation can be viewed as segmenting the seismic traces into different parts and each part is a unique object. Thus, we have considered the horizon interpretation as an object detection problem. We use the encoder-decoder convolutional neural network (CNN) to detect the “objects” contained in the seismic traces. The boundary of the objects is regarded as the horizons. The training data are the seismic traces located on a user-defined coarse grid. We give a unique training label to the time window of seismic traces bounded by two manually picked horizons. To efficiently learn the waveform pattern that is bounded by two adjacent horizons, we use variable sizes for the convolution filters, which is different than current CNN-based image segmentation methods. Two field data examples demonstrate that our method is capable of producing accurate horizons across the fault surface and near the unconformity which is beyond the current capability of horizon picking method.

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