... generative adversarial network imaging diffraction seg international exposition exploration geophysicist 10 dataset angle domain Synthesizing seismic diffractions using a generative adversarial network Ricard Durall*1,2 , Valentin Tschannen1 , Franz-Josef Pfreundt1, Janis Keuper1,3, 1Fraunhofer ITWM...

...Wasserstein cycle-consistent generative adversarial network for improved seismic impedance inversion: Example on 3D SEAM model Ao Cai*, Haibin Di, Zhun Li, Hiren Maniar, Aria Abubakar, Schlumberger, Houston, TX Summary The artificial neural network (ANN), particularly the feedforward neural network...

... network sandstone image reservoir characterization upstream oil & gas machine learning deep learning rock image digital rock image reconstruction objective seg international exposition rock sample isotropic property exploration geophysicist 10 axis gan generative adversarial network...

... ABSTRACT As one of machine learning techniques, deep learning has recently achieved the state-of-the-art performances in many areas, such as computer vision, natural language processing, to name a few. A generative model called Generative Adversarial Network (GAN) was invented in 2014...

... beyond phase recognition and wave classification. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 1 Presentation Type: Poster generative adversarial network seg international exposition annual meeting...

... inversion using generative adversarial networks as a geological prior : First EAGE/PESGB Workshop Machine Learning . Richardson , A. , 2018 , Seismic full-waveform inversion using deep learning tools and techniques: arXiv:1801.07232 . Robinson , E. A. , 1967 , Predictive...

... ABSTRACT We propose an algorithm for seismic data interpolation using generative adversarial networks (GANs). The method works by extracting feature vectors of the training data by self-learning and does not require any processing to create the training labels. The algorithm does not make any...

... dataset. In this paper, we propose a method for seismic data interpolation by using the conditional generative adversarial network in time and frequency domain (TF-CGAN). This network consists of two parts, a generation network and a discrimination network. Seismic data and the FFT-transformed data...

... resolution. Firstly, we introduce the generative adversarial network to seismic data processing and use it to enhance the resolution of seismic data. For a 3D field seismic dataset, we use one typical method, the adaptive bandwidth extension in the continuous wavelet transform domain, to enhance...

... mechanism in realistic settings. In this study, we investigate the feasibility of using a supervised CNN and a semi-supervised generative adversarial network (GAN) for 3D facies classification from seismic data and well logs, to overcome these challenges. We assess the performance with varying data...

... ABSTRACT This work proposes an application of deep-learning technique to noise removal for onshore seismic data. Two 24-layer Deep Neuron Networks are built using the architecture Generative Adversarial Network, with 0.3 billion parameters in total. The processing time can be reduced from...

... a generative adversarial network (GAN) to process seismic migrated images in order to potentially obtain different kinds of outputs depending on the application target at training stage. We demonstrate the promising features of this tool through a couple of synthetic examples. In the first example, the GAN...

... learning hoop neural network machine learning deep neural network architecture seg international exposition imaging generative adversarial network artificial intelligence operator algorithm reconstruction deep architecture architecture downward continuation approach inverse problem upstream...

... ABSTRACT Generative adversarial networks (GANs) are a class of machine learning techniques that involve two networks trained simultaneously to generate a desired outcome. These schemes have had success in many traditional image processing tasks, such as style transfer and super-resolution...