Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave velocity (VP), S-wave velocity (VS), and density (ρ) of the earth’s subsurface. Generally, the sei...

Prestack and poststack inversion using a physics-guided convolutional neural network

Velocity analysis can be a time-consuming task when performed manually. Methods have been proposed to automate the process of velocity analysis, which, however, typically requires significa...

Automatic velocity analysis using convolutional neural network and transfer learning

The lack of low-frequency information and a good initial model can seriously affect the success of full-waveform inversion (FWI), due to the inherent cycle skipping problem. Computational l...

Extrapolated full-waveform inversion with deep learningEFWI-CNN

Recent advances in machine learning and its applications in various sectors are generating a new wave of experiments and solutions to solve geophysical problems in the oil and gas industry...

Applications of supervised deep learning for seismic interpretation and inversion

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through ...

Wei Xiong

Papers

Seismic fault detection with convolutional neural network