Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V143-V155 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Fangyu Li ◽  
Naihao Liu
Keyword(s):  
Image Segmentation ◽  
Network Architecture ◽  
Training Data ◽  
Segmentation Method ◽  
Fracture Detection ◽  
Time Index ◽  
Network Hierarchy ◽  
First Arrival ◽  
Microseismic Events ◽  
Trained Network

Microseismic imaging plays an important role in hydraulic fracture detection, and the first-arrival picking of microseismic events is the bedrock of microseismic imaging. Manual picking is the most reliable and also the most time-consuming method for the detection of the first arrival of microseismic events. Accurate and efficient first-arrival picking in a real noisy environment is a challenge for most of the automatic first-arrival picking methods. We have developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method. We first form the training data by randomly selecting part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment the selected traces into two parts according to the time index of manual picking and assign each part a label accordingly. Then, we build an encoder-decoder convolutional neural network architecture and use the training data and training label as the input. Next, we obtain the trained network hierarchy by learning the segmented training data and labels. Finally, we predict the first arrivals of microseismic events by applying the trained network hierarchy to the rest of the microseismic traces. The synthetic and field data examples demonstrate that our method successfully identifies the first arrivals. The predicted first-arrival result obtained by using our method is superior to the result obtained by using the traditional method of short-term average and long-term average.

scholarly journals Land Cover Classification using Very High Spatial Resolution Remote Sensing Data and Deep Learning

2020 ◽  
Vol 57 (1-2) ◽  
pp. 71-77
Author(s):  
R. Ķēniņš
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Human Error ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Training Data ◽  
Surface Model

AbstractThe paper describes the process of training a convolutional neural network to segment land into its labelled land cover types such as grass, water, forest and buildings. This segmentation can promote automated updating of topographical maps since doing this manually is a time-consuming process, which is prone to human error. The aim of the study is to evaluate the application of U-net convolutional neural network for land cover classification using countrywide aerial data. U-net neural network architecture has initially been developed for use in biomedical image segmentation and it is one of the most widely used CNN architectures for image segmentation. Training data have been prepared using colour infrared images of Ventspils town and its digital surface model (DSM). Forest, buildings, water, roads and other land plots have been selected as classes, into which the image has been segmented. As a result, images have been segmented with an overall accuracy of 82.9 % with especially high average accuracy for the forest and water classes.

scholarly journals Capsule networks for segmentation of small intravascular ultrasound image datasets

2021 ◽  
Author(s):  
Lennart Bargsten ◽  
Silas Raschka ◽  
Alexander Schlaefer
Keyword(s):  
Image Segmentation ◽  
Intravascular Ultrasound ◽  
Network Architecture ◽  
Training Data ◽  
Ultrasound Images ◽  
Single Shot ◽  
Feature Maps ◽  
Clinical Workflow ◽  
Ivus Image ◽  
Image Datasets

Abstract Purpose Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks. Methods We systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters. Results Contrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance. Conclusion Capsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.

Semi-Supervised Salt Segmentation Using Mean Teacher

2022 ◽  
pp. 1-39
Author(s):  
Zhicheng Geng ◽  
Zhanxuan Hu ◽  
Xinming Wu ◽  
Luming Liang ◽  
Sergey Fomel
Keyword(s):  
Network Architecture ◽  
Input Data ◽  
Moving Average ◽  
Training Data ◽  
Segmentation Method ◽  
Student Model ◽  
The Mean ◽  
Seismic Images ◽  
Subsurface Modeling ◽  
Extract Information

Detecting subsurface salt structures from seismic images is important for seismic structural analysis and subsurface modeling. Recently, deep learning has been successfully applied in solving salt segmentation problems. However, most of the studies focus on supervised salt segmentation and require numerous accurately labeled data, which is usually laborious and time-consuming to collect, especially for the geophysics community. In this paper, we propose a semi-supervised framework for salt segmentation, which requires only a small amount of labeled data. In our method, adopting the mean teacher method, we train two models sharing the same network architecture. The student model is optimized using a combination of supervised loss and unsupervised consistency loss, whereas the teacher model is the exponential moving average (EMA) of the student model. We introduce the unsupervised consistency loss to better extract information from unlabeled data by constraining the network to give consistent predictions for the input data and its perturbed version. We train and test our novel semi-supervised method on both synthetic and real datasets. Results demonstrate that our proposed semi-supervised salt segmentation method outperforms the supervised baseline when there is a lack of labeled training data.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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