Skip Nav Destination
Close Modal
Update search
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number
NARROW
Format
Subjects
Article Type
Date
Availability
1-20 of 556
Keywords: deep learning
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
1
Sort by
Proceedings Papers
Complete detection of small earthquakes uncovers intricate relation between injection and seismicity
Yangkang Chen, Alexandros Savvaidis, Omar M. Saad, Daniel Siervo, Dino Huang, Yunfeng Chen, Iason Grigoratos, Sergey Fomel, Caroline Breton
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4081978
..., analyzing numerous small-magnitude events (Ml< 1.0) is prohibitively unaffordable. We propose to apply an advanced deep learning method, the earthquake compact convolutional transformer (EQCCT), to relieve the workload of analyzing thousands of small earthquakes per month in West Texas. The EQCCT method...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4091022
... geoscience & energy 10 small fracture geological subdiscipline prediction exploration geophysicist sedimentary rock deep learning rock type mudstone fracture real drilling data neural network micro-fracture prediction technology american association reservoir characterization pre-stack...
Proceedings Papers
Enhancing seismic data quality: A machine learning approach to denoising and signal damage reduction
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4094881
... & energy 10 enhancing seismic data quality coherent signal deep learning machine learning blind-trace network noise record signal damage reduction american association streamer data Enhancing Seismic Data Quality: A Machine Learning Approach to Denoising and Signal Damage Reduction Mark Roberts...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4076489
... As one of the primary approaches for property estimation in subsurface interpretation and characterization, stochastic seismic inversion provides a measurement of uncertainty but is a complicated process and requires heavy computation. To accelerate the task with deep learning, we propose...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4081304
... models while conditioning to well facies data. geology reservoir characterization geologist artificial intelligence sedimentary rock rock type deep learning machine learning geomodel generative adversarial network generative facies facies model applied geoscience & energy 10 ddpm...
Proceedings Papers
Yangkang Chen, Omar M. Saad, Alexandros Savvaidis, Fangxue Zhang, Yunfeng Chen, Dino Huang, Huijian Li, Farzaneh Aziz Zanjani
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4081476
... for microseismic events. However, determining the P-wave first-motion polarity can be challenging and subjective for smaller magnitude events. Here, we propose a deep-learning method (EQpolarity) for determining the P-wave first-motion polarity using the vertical-component seismic waveforms. We apply the deep...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4081840
... Five-dimensional (5D) seismic data reconstruction is a crucial step to improve seismic imaging. We introduce a deep complex-valued neural network for constructing an unsupervised frequencyspace domain deep learning framework to reconstruct each frequency component of 5D seismic data. We apply...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4083103
... common as a way to improve resolution. With the help of these stacks, seismic data at different offset angles can be processed, revealing more details about underlying structures and improving the imaging of intricate geological features. Delineating faults through deep learning becomes an important step...
Proceedings Papers
Wen Pan, Harry Rynja, Ramakrishna Dandu, Zaifeng Liu, Shuzhen Ye, Antonio De Lilla, Jay Chen, Jeremy Vila
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4086530
... learning geologist artificial intelligence deep learning reservoir characterization convolutional neural network economic geology automatic migrated gather processing seismic gather american association exploration geophysicist prediction amplitude avc petroleum geology geological...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4087062
... RTT module is based on grid-warping and image-warping, which is faster and easier to integrate with deep learning. Besides, raw data after RTT and its frequency division results, and embedding of time and velocity constitute the input data of the network. Benefiting from semi-supervised learning...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4087480
... 5D interpolation aims to recover the missing seismic traces in a 5D data volume, using all physical dimensions of seismic acquisition. Here, we develop a highly effective workflow for reconstructing highly incomplete data using deep learning (DL). The proposed method requires an ingenious data...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4087561
... With the increasing application of deep learning in geoscience, In this paper, a new method of fault-karst reservoir identification based on ResU-Next model is proposed on the basis of previous research. Traditional learning and prediction of karst caves and faults are conducted separately...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4087583
... Recent advances in deep learning and computer vision have resulted in giant leaps in automating some of the cumbersome oil and gas exploration and production operations. Deep convolutional neural networks have been widely used for seismic interpretation tasks including detection, classification...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4088064
... of this approach is its ability to compress and denoise the raw SP log and predictor features into a latent representation and then to efficiently predict the baseline-corrected SP log without manual interpretation. We train our deep learning model against manually-corrected SP logs, and test with unseen wells...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4089186
... learning rely on extensive synthetic data for training, with the high economic costs and resource investment limiting their widespread application. To further improve the efficiency of dispersion inversion and reduce algorithm development costs, we have developed a lightweight, deep learning-based...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4100627
... at the same time, so as to realize the first break picking stably. Finally, we use field data demonstrate the effectiveness of the proposed first break picking method. geologist artificial intelligence correction geology template deep learning reservoir characterization nltd neural network...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4100633
... estimations but with limited coverage and azimuth for epicenter location. To bridge the capacities of surface and borehole arrays, a deep learning method is proposed to directly locate microseismic events in 3D using recordings from both surface and borehole sensors based on an elastic medium assumption...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4100715
... Conventional marine seismic surveys lack near offset information, resulting in lower-quality imaging in the shallow subsurface. We present a deep learning workflow to reconstruct near offsets, which utilizes field data collected with source-over-cable acquisition geometry as training data. First...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4100860
... is evaluated through testing on both synthetic and real datasets, notably including seismic angle gathers from the Volve dataset. geologist reservoir characterization misaligned seismic gather deep learning machine learning prediction artificial intelligence economic geology exploration...
Proceedings Papers
Publisher: Society of Exploration Geophysicists
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4101284
... Deep learning (DL) methods have demonstrated promising advancements in seismic demultiple, addressing issues of traditional workflows. However, a key challenge is the limited flexibility of DL solutions in the demultiple process. Once a DL model has been trained, it produces one demultiple...
1