Quality control of seismic data based on convolutional neural network

2021 ◽  
Vol 57 (3) ◽  
pp. 329-338
Author(s):  
Seoahn Lee ◽  
Dong-Hoon Sheen
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Convolutional Neural Network ◽  
Seismic Data

White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V307-V317 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Naihao Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
White Noise ◽  
Seismic Data ◽  
Prior Information ◽  
Training Procedure ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition

Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.

Footstep detection in urban seismic data with a convolutional neural network

2020 ◽  
Vol 39 (9) ◽  
pp. 654-660 ◽  
Author(s):  
Srikanth Jakkampudi ◽  
Junzhu Shen ◽  
Weichen Li ◽  
Ayush Dev ◽  
Tieyuan Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Earthquake Hazard ◽  
Small Scale ◽  
State University ◽  
Data Set ◽  
Near Surface ◽  
Privacy Concerns ◽  
Seismic Arrays

Seismic data for studying the near surface have historically been extremely sparse in cities, limiting our ability to understand small-scale processes, locate small-scale geohazards, and develop earthquake hazard microzonation at the scale of buildings. In recent years, distributed acoustic sensing (DAS) technology has enabled the use of existing underground telecommunications fibers as dense seismic arrays, requiring little manual labor or energy to maintain. At the Fiber-Optic foR Environmental SEnsEing array under Pennsylvania State University, we detected weak slow-moving signals in pedestrian-only areas of campus. These signals were clear in the 1 to 5 Hz range. We verified that they were caused by footsteps. As part of a broader scheme to remove and obscure these footsteps in the data, we developed a convolutional neural network to detect them automatically. We created a data set of more than 4000 windows of data labeled with or without footsteps for this development process. We describe improvements to the data input and architecture, leading to approximately 84% accuracy on the test data. Performance of the network was better for individual walkers and worse when there were multiple walkers. We believe the privacy concerns of individual walkers are likely to be highest priority. Community buy-in will be required for these technologies to be deployed at a larger scale. Hence, we should continue to proactively develop the tools to ensure city residents are comfortable with all geophysical data that may be acquired.

scholarly journals Quality Control of Cigarettes Packaging using Convolutional Neural Network

2019 ◽  
Vol 462 ◽  
pp. 012002 ◽  
Author(s):  
A Nazar ◽  
M N P Nurwiyadi ◽  
M Syai’in ◽  
A Khumaidi ◽  
R Y Adhitya ◽  
...  
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Convolutional Neural Network

scholarly journals Automated System for Grading Apples using Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 1458-1464
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Automated System ◽  
Test Accuracy ◽  
Fruit Processing ◽  
Fruit Grading ◽  
Increasing Demand

Fruit grading is a process that affect quality control and fruit-processing industries to meet the efficiency of its production and society. However, these industries have suffered from lack of standards in quality control, higher time of grading and low product output because of the use of manual methods. To meet the increasing demand of quality fruit products, fruit-processing industries must consider automating their fruit grading process. Several algorithms have been proposed over the years to achieve this purpose and their works were based on color, shape and inability to handle large dataset which resulted in slow recognition accuracy. To mitigate these flaws, we develop an automated system for grading and classification of apple using Convolutional Neural Network (CNN) used in image recognition and classification. Two models were developed from CNN using ResNet50 as its convolutional base, a process called transfer learning. The first model, the apple checker model (ACM) performs the recognition of the image with two output connections (apple and non-apple) while the apple grader model (AGM) does the classification of the image that has four output classes (spoiled, grade A, grade B & grade C) if the image is an apple. A comparison evaluation of both models were conducted and experimental results show that the ACM achieved a test accuracy of 100% while the AGM obtained recognition rate of 99.89%.The developed system may be employed in food processing industries and related life applications.

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.

scholarly journals Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images

2021 ◽  
pp. 31-43
Author(s):  
Ryosuke Kaneko ◽  
Hiromichi Nagao ◽  
Shin-ichi Ito ◽  
Kazushige Obara ◽  
Hiroshi Tsuruoka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Low Frequency ◽  
Seismic Waveforms ◽  
Seismic Waveform ◽  
Numerous Paper ◽  
Large Earthquakes ◽  
Activation Mapping

AbstractThe installation of dense seismometer arrays in Japan approximately 20 years ago has led to the discovery of deep low-frequency tremors, which are oscillations clearly different from ordinary earthquakes. As such tremors may be related to large earthquakes, it is an important issue in seismology to investigate tremors that occurred before establishing dense seismometer arrays. We use deep learning aiming to detect evidence of tremors from past seismic data of more than 50 years ago, when seismic waveforms were printed on paper. First, we construct a convolutional neural network (CNN) based on the ResNet architecture to extract tremors from seismic waveform images. Experiments applying the CNN to synthetic images generated according to seismograph paper records show that the trained model can correctly determine the presence of tremors in the seismic waveforms. In addition, the gradient-weighted class activation mapping clearly indicates the tremor location on each image. Thus, the proposed CNN has a strong potential for detecting tremors on numerous paper records, which can enable to deepen the understanding of the relations between tremors and earthquakes.

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