Deep learning for 3D seismic compressive-sensing technique: A novel approach

2019 ◽  
Vol 38 (9) ◽  
pp. 698-705
Author(s):  
Ping Lu ◽  
Yuan Xiao ◽  
Yanyan Zhang ◽  
Nikolaos Mitsakos
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Field Data ◽  
Original Data ◽  
Generative Adversarial Networks ◽  
Sampled Data ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Adversarial Networks ◽  
Novel Approach

A deep-learning-based compressive-sensing technique for reconstruction of missing seismic traces is introduced. The agility of the proposed approach lies in its ability to perfectly resolve the optimization limitation of conventional algorithms that solve inversion problems. It demonstrates how deep generative adversarial networks, equipped with an appropriate loss function that essentially leverages the distribution of the entire survey, can serve as an alternative approach for tackling compressive-sensing problems with high precision and in a computationally efficient manner. The method can be applied on both prestack and poststack seismic data, allowing for superior imaging quality with well-preconditioned and well-sampled field data, during the processing stage. To validate the robustness of the proposed approach on field data, the extent to which amplitudes and phase variations in original data are faithfully preserved is established, while subsurface consistency is also achieved. Several applications to acquisition and processing, such as decreasing bin size, increasing offset and azimuth sampling, or increasing the fold, can directly and immediately benefit from adopting the proposed technique. Furthermore, interpolation based on generative adversarial networks has been found to produce better-sampled data sets, with stronger regularization and attenuated aliasing phenomenon, while providing greater fidelity on steep-dip events and amplitude-variation-with-offset analysis with migration.

Seismic compressive sensing by generative inpainting network: Toward an optimized acquisition survey

2019 ◽  
Vol 38 (12) ◽  
pp. 923-933 ◽  
Author(s):  
Xiaoyang Rebecca Li ◽  
Nikolaos Mitsakos ◽  
Ping Lu ◽  
Yuan Xiao ◽  
Xing Zhao
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Computational Cost ◽  
Seismic Survey ◽  
Generative Adversarial Networks ◽  
Promising Alternative ◽  
Seismic Surveys ◽  
Adversarial Networks ◽  
Seismic Acquisition ◽  
Seismic Images

The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition. Conventional recovery usually suffers from undesired artifacts, such as oversmoothing, and high computational cost. Generative adversarial networks (GANs) offer promising alternative approaches that can improve quality and reveal finer details. An appropriately designed Wasserstein GAN trained on several historical surveys and capable of learning the statistical properties of the seismic wavelet's architecture is proposed. The efficiency and precision of this model at compressive sensing are validated in three steps. First, the existence of a sparse representation with different compression rates for seismic surveys is studied. Then, nonuniform samplings are studied using the proposed methodology. Finally, a recommendation is proposed for a nonuniform seismic survey grid based on the evaluation of reconstructed seismic images and metrics. The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition without a loss in imaging quality. Along these lines, a compressive sensing design of a nonuniform grid over an asset in the Gulf of Mexico, versus a traditional seismic survey grid that collects data uniformly every few feet, is suggested, leveraging the proposed method.

scholarly journals Data Augmentation using Adversarial Networks for Tea Diseases Detection

2020 ◽  
Vol 20 (1) ◽  
pp. 29
Author(s):  
R. Sandra Yuwana ◽  
Fani Fauziah ◽  
Ana Heryana ◽  
Dikdik Krisnandi ◽  
R. Budiarianto Suryo Kusumo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Original Data ◽  
The Other ◽  
Generative Adversarial Networks ◽  
Learning Technology ◽  
Limited Data ◽  
Adversarial Networks

Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming.  On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented.  By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception.  The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%. 

scholarly journals Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection

2021 ◽  
Vol 11 (7) ◽  
pp. 3086
Author(s):  
Ricardo Silva Peres ◽  
Miguel Azevedo ◽  
Sara Oleiro Araújo ◽  
Magno Guedes ◽  
Fábio Miranda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Manufacturing Sector ◽  
Data Availability ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Learning Approaches ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Structural Adhesive

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

scholarly journals Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Karim Armanious ◽  
Tobias Hepp ◽  
Thomas Küstner ◽  
Helmut Dittmann ◽  
Konstantin Nikolaou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attenuation Correction ◽  
Fdg Pet ◽  
Whole Body ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks ◽  
18F Fdg

scholarly journals A deep learning approach to capture the essence of Candida albicans morphologies

2021 ◽  
Author(s):  
Van Bettauer ◽  
Anna CBP Costa ◽  
Raha Parvizi Omran ◽  
Samira Massahi ◽  
Eftyhios Kirbizakis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Developmental Trajectories ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks ◽  
Latent Space ◽  
Contrast Microscopy ◽  
Opportunistic Human Pathogen ◽  
Community Meeting

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

Motion Imitation for Monocular Videos

2021 ◽  
pp. 118-135
Author(s):  
Priyanka Nandal
Keyword(s):  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Intermediate Representation ◽  
Simple Method ◽  
Adversarial Networks ◽  
Target Subject

This work represents a simple method for motion transfer (i.e., given a source video of a subject [person] performing some movements or in motion, that movement/motion is transferred to amateur target in different motion). The pose is used as an intermediate representation to perform this translation. To transfer the motion of the source subject to the target subject, the pose is extracted from the source subject, and then the target subject is generated by applying the learned pose to-appearance mapping. To perform this translation, the video is considered as a set of images consisting of all the frames. Generative adversarial networks (GANs) are used to transfer the motion from source subject to the target subject. GANs are an evolving field of deep learning.

scholarly journals Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition

2020 ◽  
Vol 34 (03) ◽  
pp. 2645-2652 ◽  
Author(s):  
Yaman Kumar ◽  
Dhruva Sahrawat ◽  
Shubham Maheshwari ◽  
Debanjan Mahata ◽  
Amanda Stent ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Classification Problem ◽  
Visual Speech ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Visual Speech Recognition ◽  
Training Samples ◽  
English Training

Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data leads to error-prone systems with very low accuracies in predicting unseen classes. To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. We also show that our models are language agnostic and therefore capable of seamlessly generating, using English training data, videos for a new language (Hindi). To the best of our knowledge, this is the first work to show empirical evidence of the use of GANs for generating training samples of unseen classes in the domain of VSR, hence facilitating zero-shot learning. We make the added videos for new classes publicly available along with our code1.

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