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Keywords: training data
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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-2021-3584059
..., it requires the acquisition geometry of field data and the bathymetry of the ocean floor to be known to create training data with the approximate configuration. We demonstrate with a numerical example the ability of our solution to remove ghost reflections and show that it is robust to random noise, even when...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3574963
... to date. To expand our knowledge of these features, we test RetinaNet and MaskRCNN deep learning models as means of detecting shell rings from wide-area LiDAR data using extremely small training datasets. We demonstrate that the use of “negative” training data to identify non-archaeological features 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-2021-3582642
.... For generating ML model with high performance, the features of target seismic data must be reflected in the training data when the training dataset is numerically generated. The characteristic of reflectivity series, one of the important features of target data, was extracted from well log data in previous...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582984
... evaluation artificial intelligence random splitting test training data algorithm applied geoscience resistivity method ocean drilling program Machine Learning Model Evaluation: A Case study for Core Guided Petrophysical Analysis Maitham Alabbad*, Manika Prasad, Ge Jin, Colorado School of Mines...
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
... Conventional seismic processing schemes are both time-consuming and subjective. As an alternative approach, we propose model-driven processing with convolutional neural networks, where a network is trained on synthetically modelled training data in order to learn a given processing scheme. We...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583602
... oil & gas training data interpretation payne chevron technical center summary noise deep section neural network applied geoscience neural network architecture maisha amaru Improved 3D neural network architecture for fault interpretation on field data Enning Wang, Maisha Amaru, Stan...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584041
... synthetic and field examples are provided to demonstrate the current performance of our proposed framework. neural network ground truth primary artificial intelligence training data data distribution reservoir characterization upstream oil & gas field data primary label machine learning...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3592770
... Swell noise can be removed from seismic shot records by using deep neural networks. As with any supervised Machine Learning (ML) problem, swell noise removal requires good quality training data that would allow the generalization of the resulting models. To guarantee good quality training data...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594315
... artificial intelligence reservoir characterization structural geology upstream oil & gas training data neural network classification applied geoscience module prediction exploration geophysicist 10 machine learning deep learning seismic data expanded abstract unlabeled data...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594813
... correctly with enough training data. Data Science Challenges In this dataset, we apply all the above techniques to calculate Finding ideal MLP architecture: the predictions efficiently and effectively. Taylor et al., 2020 show a slightly modified version of this work on a different o Number of hidden...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3580914
... In this work, we propose a weakly supervised learning method which could utilize sparse manual interpretation results as training data for 3D fault detection task. Following a weakly supervised learning setting, we design the masked training data, which are gathered from field seismic volumes...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582218
... The neural networks applied to geophysical inversion has had limited success due to the need for large training data sets and the lack of generalizability to out-of-sample scenarios. The deterministic regularized inversion often requires a good starting model to avoid possible local minima...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427239
... training data proceedings exploration geophysicist 10 dataset probability posterior distribution aleatoric uncertainty international conference artificial intelligence deep learning uncertainty estimation epistemic uncertainty seg international exposition automatic channel detection nam pham...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424584
... Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: 351F Presentation Type: Oral machine learning upstream oil & gas surface wave analysis international conference artificial intelligence training data exploration geophysicist 10 neural network validation sample seg...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3424983
..., October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 9:20 AM Location: Poster Station 3 Presentation Type: Poster neural network iteration 1 artificial intelligence high frequency data initial model machine learning deep learning seismic data training data exploration...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427510
... reservoir characterization upstream oil & gas training set machine learning deep learning data augmentation artificial intelligence classification generator neural network validation set training data seg international exposition augmentation well log data exploration...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427542
... error and the difference between the velocity model used in creating training data set and the true velocity model of the target area. The results show that the ML model for moment tensor inversion is most affected by the error in the peak amplitude. For the picking error, proper results are obtained up...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427602
... In this work, we present a new machine learning workflow for stratigraphic interpretation examining different strategies of picking training data. Generating training data is a time consuming process and different aspects of the training data, such as the dimensionality, spatial extent, amount...
Proceedings Papers

Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427320
.... While they are able to provide accurate reconstructions of seismic data without the need for any training data, they tend to suffer when large gaps are present in the missing data. We observe significant improvements in the reconstructed data when a POCS regularization term is introduced to the DIP. We...

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