Geophysics
16 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Geophysics
Most implemented papers
Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks
Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention.
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
70 years of machine learning in geoscience in review
I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet
We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training.
Data-driven modeling of time-domain induced polarization
We present a novel approach for data-driven modeling of the time-domain induced polarization (IP) phenomenon using variational autoencoders (VAE).
Viskositas: Viscosity Prediction of Multicomponent Chemical Systems
Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics.
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling
Thus, our proposed framework called VI-Non-IID inclines to have better noise characterization and generalization capabilities, which brings better performance on seismic field NA.
Attenuation of Seismic Random Noise With Unknown Distribution: A Gaussianization Framework
The Gaussianization submodule with learnable parameters maps seismic data with the noise of unknown distribution to the one corrupted by Gaussian noise.