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Found 6 papers, 2 papers with code
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion
In the field data with two or three well logs, only the methods based on the components proposed in this paper were able to achieve reasonable results.
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest.
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing
The interpolation and reconstruction of missing traces is a crucial step in seismic data processing, moreover it is also a highly ill-posed problem, especially for complex cases such as high-ratio random discrete missing, continuous missing and missing in fault-rich or salt body surveys.
MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation
Recently, training 3D fault segmentation using sparse manual 2D slices is thought to yield promising results, but manual labeling has many false negative labels (abnormal annotations), which is detrimental to training and consequently to detection performance.
Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels
The task of image segmentation requires huge labels, especially 3D seismic data, which has a complex structure and lots of noise.
Prediction and analysis of Coronavirus Disease 2019
According to the current trend, based on the three models, the total number of people expected to be infected is 49852-57447 in Wuhan, 12972-13405 in non-Hubei areas and 80261-85140 in China respectively.
Populations and Evolution
