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convolutional neural networks

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Journal Article
Journal: Geophysics
Published: 10 June 2020
Geophysics (2020) 85 (4): O47–O58.
..., imbalanced facies class distribution, and lack of rigorous performance evaluation metrics. To overcome these challenges, we have developed a supervised convolutional neural network (CNN) and a semisupervised generative adversarial network (GAN) for 3D seismic facies classification in situations...
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Journal Article
Journal: Geophysics
Published: 11 November 2021
Geophysics (2021) K1–K13.
... on high-order derivatives and are therefore susceptible to noise in the data. Convolutional neural networks (CNNs) are a subset of machine-learning methods that are well-suited to image processing tasks, and they have been shown to be effective at interpreting other geophysical data, such as seismic...
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Journal Article
Journal: Geophysics
Published: 20 October 2021
Geophysics (2021) 86 (6): R959–R971.
... by minimizing the data residuals, is a computationally expensive task. To alleviate this problem and improve imaging quality, we have developed an LSRTM approach using convolutional neural networks (CNNs), which is referred to as CNN-LSRTM. Specifically, the LSRTM problem can be implemented via a gradient-like...
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Journal Article
Journal: Geophysics
Published: 09 September 2021
Geophysics (2021) 86 (6): KS109–KS121.
... and picked. We have used a novel artificial neural network framework to directly map seismic data, without any event picking or detection, to their potential source locations. We train two convolutional neural networks (CNNs) on labeled synthetic acoustic data containing simulated microseismic events...
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Journal Article
Published: 14 April 2021
Seismological Research Letters (2021) 92 (5): 2961–2971.
... an improved solution, ArrayConvNet—a convolutional neural network that uses continuous array data from a seismic network to seamlessly detect and localize events, without the intermediate steps of phase detection, association, travel‐time calculation, and inversion. When testing this methodology with events...
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Journal Article
Journal: Geophysics
Published: 19 March 2021
Geophysics (2021) 86 (3): M41–M48.
... different dropout ratios in convolutional neural networks. The aleatoric uncertainty is irreducible because it relates to the stochastic dependency within the input observations. As the number of Monte Carlo realizations increases, the epistemic uncertainty asymptotically converges and the model standard...
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Journal Article
Journal: Geophysics
Published: 18 February 2021
Geophysics (2021) 86 (2): V131–V142.
... a convolution neural network (CNN), which captures priors based on the particular structure of the CNN, but it does not need any training data set. The ill-posed inverse problem in seismic interpolation is thus solved using the CNN structure as a prior, and the learned network weights are the parameters...
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Journal Article
Journal: Geophysics
Published: 21 January 2021
Geophysics (2021) 86 (1): MR27–MR37.
...Rongang Cui; Danping Cao; Qiang Liu; Zhaolin Zhu; Yan Jia ABSTRACT Predicting elastic parameters based on digital rock images is an interesting application of a convolutional neural network (CNN), which can improve the efficiency of prediction. Predicting elastic parameters by a conventional CNN...
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Journal Article
Journal: Geophysics
Published: 21 January 2021
Geophysics (2021) 86 (1): R129–R146.
...Mattia Aleardi; Alessandro Salusti ABSTRACT We have developed a prestack inversion algorithm that combines a discrete cosine transform (DCT) reparameterization of data and model spaces with a convolutional neural network (CNN). The CNN is trained to predict the mapping between the discrete cosine...
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Journal Article
Journal: Geophysics
Published: 13 October 2020
Geophysics (2020) 85 (6): V425–V441.
... convolutional neural network (CNN) for seismic image registration. The concept of optical flow is widely applied to the problem of image registration using variational methods. Recent developments in the field of computer vision have shown that optical flow estimation can be formulated as a supervised machine...
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Journal Article
Journal: Interpretation
Published: 12 October 2020
Interpretation (2020) 8 (4): T941–T952.
... convolutional neural network (CNN) based on the short-time Fourier transform to address swell noises. In the numerical experiments, we quantitatively evaluate the denoising performances of the time- and frequency-domain CNNs, compare the impacts of network structures on attenuating swell noises, and study how...
Journal Article
Journal: PALAIOS
Published: 12 October 2020
PALAIOS (2020) 35 (9): 391–402.
... that can be applied to recognize and categorize fossil specimens. Our results demonstrate that, given adequate images and training, convolutional neural network models can correctly identify fusulinids with high levels of accuracy. Continued efforts in digitization of biological and paleontological...
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Journal Article
Journal: Geophysics
Published: 17 August 2020
Geophysics (2020) 85 (5): N41–N55.
...Vishal Das; Tapan Mukerji ABSTRACT We have built convolutional neural networks (CNNs) to obtain petrophysical properties in the depth domain from prestack seismic data in the time domain. We compare two workflows — end-to-end and cascaded CNNs. An end-to-end CNN, referred to as PetroNet, directly...
Journal Article
Journal: Geophysics
Published: 13 June 2020
Geophysics (2020) 85 (4): WA227–WA240.
... classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep...
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Journal Article
Journal: Geophysics
Published: 30 April 2020
Geophysics (2020) 85 (4): WA77–WA86.
... a semisupervised workflow for efficient seismic stratigraphy interpretation by using the state-of-the-art deep convolutional neural networks (CNNs). Specifically, the workflow consists of two components: (1) seismic feature self-learning (SFSL) and (2) stratigraphy model building (SMB), each of which is formulated...
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Journal Article
Journal: Geophysics
Published: 16 January 2020
Geophysics (2020) 85 (4): WA27–WA39.
... proposed to automate fault and horizon interpretation, each of which today still requires significant human effort. We improve automatic structural interpretation in seismic images by using convolutional neural networks (CNNs) that recently have shown excellent performances in detecting and extracting...
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Journal Article
Journal: Interpretation
Published: 07 August 2019
Interpretation (2019) 7 (3): SE269–SE280.
... methods in suppressing random noise. However, when the subsurface structure becomes complex, this method suffers from higher prediction errors owing to the large number of different dip components that need to be predicted. Here, we used a denoising convolutional neural network (DnCNN) algorithm...
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Journal Article
Journal: Interpretation
Published: 28 May 2019
Interpretation (2019) 7 (3): SF27–SF40.
... of accuracy in different fields. Although deep convolutional neural networks (CNNs) (a kind of deep-learning technique) have reached or surpassed human-level performance in image recognition tasks, little has been done to transport this new image classification technology to geoscientific problems. We have...
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Journal Article
Published: 16 January 2019
Seismological Research Letters (2019) 90 (2A): 481–490.
... of northeastern British Columbia , Can. J. Explor. Geophys. 30 , no.  1 , 39 – 50 . Krizhevsky A. Sutskever I. , and Hinton G. E. 2012 . Imagenet classification with deep convolutional neural networks , Advances in Neural Information Processing Systems , Lake Tahoe, Nevada , 3–6...
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Journal Article
Published: 09 January 2019
Seismological Research Letters (2019) 90 (2A): 503–509.
... associations, and in problems in which earthquake rates are high and many false arrivals are present, many standard techniques may fail to resolve the problem accurately. As an alternative approach, in this work we apply convolutional neural networks (CNNs) to the problem of associations; we train CNNs to read...
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