data-inversion's starred repositories
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
100-Days-Of-ML-Code
100 Days of ML Coding
datascience
Curated list of Python resources for data science.
awesome-open-geoscience
Curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome
seismic_deep_learning
A couple of python scripts to interpret geological structures from geophysical images using deep learning
segyio-notebooks
Notebooks with examples and demos of segyio
WaveProp_in_MATLAB
Single-file implementations of 2D and 3D acoustic and elastic wave propagation in time domain using finite-differences(FDTD). Simple formulation and implementation
Signal_Tools
Signal Processing toolbox, including DFT, IDFT, Wavelet, τp transform, HHT. Besides, this repository aslo has other useful functions, such as 1D/2D Convolution, Cross-Correlation, Filtering and Denosing.
CNN_based_impedance_inversion
Convolutional neural network for seismic impedance inversion
dispinversion
Surface Wave Dispersion Inversion Code
geo-style-keras
Neural style transfer applied to elastic subsurface models [SEG19]. Visualized and explained.
SEAM_I_2D_Elastic
SEAM I open sourced 2D line for creating synthetic prestack seismic
avoinversion
Adjoint-state based AVO Inversion Method
AWE-Reverve-Time-Migration
RTM in matlab language
wheeler_hale_2015
An implementation of Wheeler and Hale's 2015 method for aligning well logs using dynamic warping
ComputaGeophys
This repo is for my class notes in Computational Geophysics. The main page is https://yufengwa.github.io/ComputaGeophys/
Geoph_426_526
Notebooks for GEOPH 426 & 526 - Geophysical Signal Processing - University of Alberta
WaveProp_in_MATLAB
Single-file codes in MATLAB for acoustic and elastic wave propagation in time domain using finite-differences(FDTD). As simple formulation and implementation as possible.
GPGN_436_536
Jupyter notebooks for courses GPGN 436/536
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)