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Found 13 papers, 7 papers with code
Recursions Are All You Need: Towards Efficient Deep Unfolding Networks
Secondly, we randomize the number of recursions during training to decrease the overall training time.
Meta-Optimization of Deep CNN for Image Denoising Using LSTM
The recent application of deep learning (DL) to various tasks has seen the performance of classical techniques surpassed by their DL-based counterparts.
Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation
We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally.
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
We formulate the problem as a very fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task.
Semi-supervised Sequence Modeling for Elastic Impedance Inversion
The proposed workflow uses well-log data to guide the inversion.
Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network
In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data.
Semi-supervised Learning for Acoustic Impedance Inversion
Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for acoustic impedance.
Image and Video Processing Signal Processing Geophysics
Structure Label Prediction Using Similarity-Based Retrieval and Weakly-Supervised Label Mapping
By having an interpreter select a very small number of exemplar images for every class of subsurface structures, we use a novel similarity-based retrieval technique to extract thousands of images that contain similar subsurface structures from the seismic volume.
Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks
Recent advances in machine learning have shown promising results for recurrent neural networks (RNN) in modeling complex sequential data such as videos and speech signals.
Multiresolution Analysis and Learning for Computational Seismic Interpretation
Moreover, directional multiresolution attributes, such as the curvelet transform, are more effective than the non-directional attributes in distinguishing different subsurface structures in large seismic datasets, and can greatly help the interpretation process.
Image and Video Processing Geophysics
A Machine Learning Benchmark for Facies Classification
In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i. e., seismic volume labeling.
Content-adaptive non-parametric texture similarity measure
In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values.
Image and Video Processing
