Seismic Stratigraphic Interpretation Based on Deep Active Learning
Author:
Affiliation:
1. Department of Automation, Tsinghua University, Beijing, China
2. Machine Intelligent Sensing and Advanced Computing Lab, College of Electrical and Mechanical Engineering, Beijing University of Chemical Technology, Beijing, China
Funder
National Key Research and Development Program of China
Natural Science Foundation of China
NSFC Youth Science Foundation Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10159420.pdf?arnumber=10159420
Reference33 articles.
1. 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube
2. Generating Seismic Horizon Using Multiple Seismic Attributes
3. Data-driven identification of stratigraphic units in 3D seismic data using hierarchical density-based clustering
4. Extracting horizons and sequence boundaries from 3D seismic images
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1. An Active Learning Framework for Microseismic Event Detection;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision;Electronics;2023-12-30
4. Spatial Pattern Learning: Dip Structure Constraint Multi-View Convolutional Neural Network for Pre-Stacked Seismic Inversion;IEEE Transactions on Geoscience and Remote Sensing;2023
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