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.

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.

scholarly journals GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer Learning

2021 ◽  
pp. 1-32
Author(s):  
Mohammad Mahdi Behzadi ◽  
Horea T. Ilies
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Knowledge Transfer ◽  
Boundary Conditions ◽  
Transfer Learning ◽  
Computational Cost ◽  
Generative Adversarial Networks ◽  
Learning Methods ◽  
Adversarial Networks ◽  
Difficult Time

Abstract Many machine learning methods have been recently developed to circumvent the high computational cost of the gradient-based topology optimization. These methods typically require extensive and costly datasets for training, have a difficult time generalizing to unseen boundary and loading conditions and to new domains, and do not take into consideration topological constraints of the predictions, which produces predictions with inconsistent topologies. We present a deep learning method based on generative adversarial networks for generative design exploration. The proposed method combines the generative power of conditional GANs with the knowledge transfer capabilities of transfer learning methods to predict optimal topologies for unseen boundary conditions. We also show that the knowledge transfer capabilities embedded in the design of the proposed algorithm significantly reduces the size of the training dataset compared to the traditional deep learning neural or adversarial networks. Moreover, we formulate a topological loss function based on the bottleneck distance obtained from the persistent diagram of the structures and demonstrate a significant improvement in the topological connectivity of the predicted structures. We use numerous examples to explore the efficiency and accuracy of the proposed approach for both seen and unseen boundary conditions in 2D.

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

Towards High-Performance Deep Learning Models in Tool Wear Classification with Generative Adversarial Networks

2021 ◽  
pp. 117484
Author(s):  
Dirk Alexander Molitor ◽  
Christian Kubik ◽  
Marco Becker ◽  
Ruben Helmut Hetfleisch ◽  
Fan Lyu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
High Performance ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Adversarial Networks ◽  
Tool Wear Classification

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.

Deep Learning in Cybersecurity

2020 ◽  
Vol 10 (2) ◽  
pp. 82-105
Author(s):  
Yadigar N. Imamverdiyev ◽  
Fargana J. Abdullayeva
Keyword(s):  
Deep Learning ◽  
Statistical Analysis ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Comprehensive Overview ◽  
Adversarial Networks

In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existing cyberattack detection methods based on DL is categorized. Methods covering attacks to deep learning based on generative adversarial networks (GAN) are investigated. The datasets used for the evaluation of the efficiency proposed by researchers for cyberattack detection methods are discussed. The statistical analysis of papers published on cybersecurity with the application of DL over the years is conducted. Existing commercial cybersecurity solutions developed on deep learning are described.

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