FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation
Delineating faults from seismic images is a key step for seismic structural interpretation,
reservoir characterization, and well placement. In conventional methods, faults are …
reservoir characterization, and well placement. In conventional methods, faults are …
Building realistic structure models to train convolutional neural networks for seismic structural interpretation
Seismic structural interpretation involves highlighting and extracting faults and horizons that
are apparent as geometric features in a seismic image. Although seismic image processing …
are apparent as geometric features in a seismic image. Although seismic image processing …
FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network
We simultaneously estimate fault probabilities, strikes, and dips directly from a seismic
image by using a single convolutional neural network (CNN). In this method, we assume a …
image by using a single convolutional neural network (CNN). In this method, we assume a …
UNet++ 和迁移学习相结合的复杂断裂识别方法研究
芦凤明， 孟瑞刚， 张军华， 王静， 李健， 王作乾， 刘璐… - 地球物理学进展, 2022 - dsjyj.com.cn
随着计算机和人工智能技术发展, 卷积神经网络(Convolutional Neural Networks, CNN)
在断层识别中的应用越来越广泛. 但是常规CNN 网络难以获得大量实际的断层样本 …
在断层识别中的应用越来越广泛. 但是常规CNN 网络难以获得大量实际的断层样本 …
Self-adaptive denoising net: Self-supervised learning for seismic migration artifacts and random noise attenuation
Seismic noise attenuation is essential for seismic interpretation and reservoir
characterization. Recently, many researchers have applied convolutional neural network …
characterization. Recently, many researchers have applied convolutional neural network …
Fault detection on seismic structural images using a nested residual U-Net
Automatic identification of faults on seismic structural images is a challenging yet crucial task
in quantitative seismic interpretation. Human picking or attribute-based fault detection …
in quantitative seismic interpretation. Human picking or attribute-based fault detection …
Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand
Unsupervised machine learning using an unsupervised vector quantization neural network
(UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as …
(UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as …
Automatic fault detection on seismic images using a multiscale attention convolutional neural network
High-fidelity fault detection on seismic images is one of the most important and challenging
topics in the field of automatic seismic interpretation. Conventional hand-picking-based and …
topics in the field of automatic seismic interpretation. Conventional hand-picking-based and …
Seismic fault detection using convolutional neural networks with focal loss
Fault detection is a fundamental and important research topic in automatic seismic
interpretation since the geometry of faults usually reveals the accumulation and migration of …
interpretation since the geometry of faults usually reveals the accumulation and migration of …
Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network
The seismic horizon is a critical input for the structure and stratigraphy modeling of
reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for …
reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for …