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Keywords: loss function
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Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584030
.... The network is trained on random input points in space and a variance of the Helmholtz equation for the scattered wavefield is used as the loss function to update the network parameters. In spite of the methods flexibility, like handling irregular surfaces and complex media, and its potential for velocity...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583695
... injected volume n, n + i 4D seismic survey. A comprehensive analysis of the geophys- and n + m, (dnpre, dnp+rei and dnp+rem). The loss function of the ical data and geology is usually required to increase the con- network is de ned as dence of the prediction (Lindeberg et al., 2001; Hoversten et al., 2003...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583662
... in the deblending task also intensifies the overfitting problem. We propose a self-supervised learning method for seismic data deblending and a flexible deblending algorithm for speed-accuracy tradeoff. Using a novel blind-trace network and blending loss function, self-supervised training is deployed directly...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583872
... multiple Gulf of Mexico datasets to train and test the 2D neural network. Further, to preserve the timing of events, RMO curvatures, frequencies, and amplitudes, we propose a composite loss function based on the short-time Fourier transform. Experimental results show that the designed loss helps...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584720
... learning artificial intelligence applied geoscience automatic stratigraphic correlation neural network stratigraphic correlation correlation segnet geophysical prospecting application segnet model loss function training data stratigraphic interpretation frequency signal reservoir...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594593
... at every location. To address the challenge, we proposed a semi-supervised learning approach with a hybrid optimizer, i.e., Genetic-evolutionary adaptive moment estimation (G-ADAM) for acoustic impedance estimation from seismic data. In this approach, reflectivity and seismic loss functions are used...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594524
..., which made industry-scale application possible in terms of efficiency. This study improves the picking algorithm in three ways: (1) it is more stable by using a new loss function which combines the conventional wavenumber differences and the wavenumber slope differences; (2) it is more efficient because...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582611
... is used as the prior dis- implemented to construct the loss function. We can estimate the data uncertainty from the variance in the logit space and tribution due to its interpretability. For example, a Bernoulli the mode uncertainty using entropy. distribution with a mean of 0.8 can be interpreted...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583313
.... models that are trained to apply the needed signal Generally, neural networks learn to map inputs to outputs processing to the data. One of the main challenges related with a specified loss function that match specific to the use of CNN s for seismic processing is the notion of predictive modeling...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3414896
... classification hydraulic fracturing classification convolutional neural network prediction loss function architecture exploration geophysicist 10 cnn seg international exposition activation function reservoir characterization deep learning seismic data microseismic event noise gradient...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3421468
... exposition imaginary part exploration geophysicist 10 loss function erence real part Machine learned Green s functions that approximately satisfy the wave equation Tariq Alkhalifah, Chao Song, KAUST, Umair bin Waheed, KFUPM SUMMARY inversion (Pratt, 1999). Such solutions are obtained by inverting...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3423159
... are known to suffer from instability and increased computational cost compared to the isotropic case. Here, we employ the emerging paradigm of physics-informed neural networks to solve the anisotropic qP-wave eikonal equation. By minimizing a loss function formed by imposing the validity of the eikonal...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424997
... Type: Oral reservoir characterization upstream oil & gas colombo neural network inversion workflow machine learning deep learning sandoval-curiel artificial intelligence objective function joint inversion procedure operator exploration geophysicist 10 loss function dl inversion...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427818
... optimization problem variance geophysics upstream oil & gas sacchi exploration geophysicist 10 noise gaussian mixture artificial intelligence seismic reconstruction outlier algorithm reservoir characterization subpopulation loss function high-amplitude noise generalized loss...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-W13-05
... inversion fcn dataset regular fcn inverse fcn loss function CycleFCN: A Physics-Informed Data-driven Seismic Waveform Inversion Method Peng Jin, Shihang Feng, Youzuo Lin, Brendt Wohlberg, and David Moulton, Los Alamos National Laboratory; Erol Cromwell and Xingyuan Chen, Paci c Northwest National...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3428223
...+, we de ne the nor- data is corrupted by additive Gaussian space-time white noise: malization operator P on functions y : D × T R by y = G (u) + , N(0, I). 1 (P y)(x,t) = Z (x) (y(x,t Z (x) = (y(x,t )) dt . A general additive Gaussian noise T Another loss function that has been widely...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3425457
... reservoir characterization upstream oil & gas source location exploration geophysicist 10 train the network passive seismic event amplitude peak frequency input data data segment university seg international exposition loss function Predict passive seismic events with a convolutional...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3425406
... exposition wave propagation artificial intelligence training data informed neural network dimitri voytan loss function boundary condition informed neural network partial differential equation differential equation Wave Propagation with Physics Informed Neural Networks Dimitri Voytan and Mrinal K...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3426516
... concatenation expanded abstract seg international exposition convolutional neural network exploration geophysicist 10 loss function interpretation fault detection probability image unet model synthetic data Seismic fault detection based on 3D Unet++ model Dun Yang, Yufei Cai, Guangmin Hu, Xingmiao...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3426412
... seismic images with different resolutions and noise levels to serve as training data sets. To improve the perception quality, we design a novel loss function that combines the l 1 loss and multi-scale structural similarity loss. Extensive experimental results on both synthetic and field seismic images...

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