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2d-tomography

This repository is a 2D travel-time tomography seismic using MATLAB which I build and my friend Rinta in order to complete my Final Projects. The forward modelling is resolved using Fast Marching Method (FMM) with finite difference approximation and the raytracing is resolved based on John Vidale paper, Finite difference calculation of travel times. This code can use two method in inversion part, Least Square and Pseudo-Inverse where the input data is only needed travel time and the location of station (in UTM, both easting and northing). You can also set some parameters which could affects the tomography result, such as the number of iteration, displaying forward modelling or inverse modelling to track your data, save your model or not and etc.

2d_and_3d_elastic_reverse_time_migration

2D and 3D elastic reverse time migration using wavefield decomposition and vector-based excitation amplitude imaging condition.

3d-iso-fd-fwi-mpich

这是一个开源程序，里面包含了三维各项同性正演模拟和全波形反演[translation: This is an open source program that contains 3D isotropic forward modeling and full waveform inversion.]

3d-machine-learning

A resource repository for 3D machine learning

4d-seismic-neural-inversion

Deep Neural Networks for Map-Based 4D Seismic Pressure-Saturation Inversion

4dproximal

7349-group-project

acousticfwi_lbfgsb

acr-build-helloworld-node

Sample Node.js application for use in demonstrating Azure Container Registry Build.

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

aml-tf-object-detection-deployment

A sample Azure Machine Learning project for creating the Docker container with a pre-trained object detection model and deploying it as API

amp

We propose a deep learning based cloud workload prediction algorithm with Adaptive Pattern Mining (AMP). First, a two-step, multi-models based method is proposed to capture the ”low frequency” and ”high frequency” characteristics of highly-variable workloads simultaneously. Next, we design a DNN based classification algorithm to automatically identify the different workload patterns for a given workload and use the corresponding model to predict the future workload.

analytics-zoo

Distributed Tensorflow, Keras and BigDL on Apache Spark

apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

attentive-regularization

CNNs with learnable kernel locations

autoweb

Final project in CS394R: Reinforcement Learning Theory and Practice at UT Austin

awesome-deeplearning

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome-reservoir-engineering

Curated from repositories that make our lives as reservoir engineers and geoscientists easier or just more awesome

azure-machine-learning-tutorial

Material for Azure Machine Learning tutorial lecture, held within Data Mining course of M.Sc. in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).

azure_machine_learning

This is the code for "Azure Machine Learning" By Siraj Raval on Youtube

b11179_hands-on-gpu-computing-with-python-

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

basics

scripts to demonstrate concepts in signal/image processing, seismic modeling

bayesian-approach-predicting-well-production

Predicting oil production from horizontal wells using Bayesian hierarchical linear-regression

bayesian_seismic_inversion

The class computes the Bayesian Seismic Inversion results . Some modules are missing due to its proprietary natures, updates with an executable file is coming soon.

bruges

Geophysics library with various helpful functions

carla_ray_rlib

Carla-Simulator environment compatible with Ray/Rllib

causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning