Airborne magnetic data of the Osborne Mine and Lightning Creek sill complex, Australia

Related tags

Deep Learningosborne-magnetic
Overview

Osborne Mine, Australia - Airborne total-field magnetic anomaly

This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.

Total field magnetic anomaly data and the flight height.

Summary
File osborne-magnetic.csv.xz
Size 2.2 Mb
Version v1
DOI https://doi.org/10.5281/zenodo.5882209
License CC-BY
MD5 md5:b26777bdde2f1ecb97dda655c8b1cf71
SHA256 sha256:12d4fc2c98c71a71ab5bbe5d9a82dd263bdbf30643ccf7832cbfec6249d40ded
Source Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419
Original license CC-BY
Processing code prepare.ipynb

Changes made

These are the changes made to the original dataset.

  • Change the horizontal datum from GDA94 to WGS84.
  • Convert terrain clearance to flight height using an SRTM grid.
  • Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID.
  • Cut to a smaller region containing only the 2 anomalies of interest.

Useful references

For prior interpretations and geological context:

About this repository

This is a place to format and prepare the original dataset for use in our tutorials and documentation.

We include the source code that prepares the datasets for redistribution by filtering, standardizing, converting coordinates, compressing, etc. The goal is to make loading the data as easy as possible (e.g., a single call to pandas.read_csv or xarray.load_dataset). Whenever possible, the code also downloads the original data (otherwise the original data are included in this repository).

💡 Tip: The easiest way to download this dataset is using Pooch, particularly to download straight from the DOI of a release.

Contributing

See our Contributing Guidelines for information on proposing new datasets and making changes to this repository.

License

All Python source code is made available under the BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors.

Unless otherwise specified, all data files and figures created by the code are available under the Creative Commons Attribution 4.0 License (CC-BY).

See LICENSE.txt for the full text of each license.

The license for the original data is specified in this README.md file.

You might also like...
Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning

Spiking Neural Network for Computer Vision using SpikingJelly framework and Pytorch-Lightning

Sami BARCHID 2 Oct 20, 2022
A Python library created to assist programmers with complex mathematical functions
A Python library created to assist programmers with complex mathematical functions

libmaths libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using mat

Simple 73 Oct 2, 2022
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022
Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers.

ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset

null 2 Oct 14, 2021
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Fast Symbolic Regression Symbolic Regression is a non-linear, non-parametric Machine Learning method capable of modeling complex data sets. fastsr aim

VAMSHI CHOWDARY 3 Jun 22, 2022
Autonomous Perception: 3D Object Detection with Complex-YOLO
Autonomous Perception: 3D Object Detection with Complex-YOLO

Autonomous Perception: 3D Object Detection with Complex-YOLO LiDAR object detect

Thomas Dunlap 2 Feb 18, 2022
Neural-fractal - Create Fractals Using Complex-Valued Neural Networks!

Neural Fractal Create Fractals Using Complex-Valued Neural Networks! Home Page Features Define Dynamical Systems Using Complex-Valued Neural Networks

Amirabbas Asadi 10 Dec 17, 2022
Compute execution plan: A DAG representation of work that you want to get done. Individual nodes of the DAG could be simple python or shell tasks or complex deeply nested parallel branches or embedded DAGs themselves.

Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In

null 12 Feb 8, 2022
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements

VITA 250 Jan 5, 2023
Releases(v1)

  • v1(Jan 20, 2022)

    Date: 2022/01/20

    DOI: https://doi.org/10.5281/zenodo.5882209

    Note: This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the Fatiando a Terra project. Please cite the original authors when using this dataset.

    Data source: Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419

    Changes:

    • 🎉 First release of the curated version of the Osborne Mine aeromagnetic data.

    | | Checksums | |--:|:--| | MD5 | md5:b26777bdde2f1ecb97dda655c8b1cf71 | | SHA256 | sha256:12d4fc2c98c71a71ab5bbe5d9a82dd263bdbf30643ccf7832cbfec6249d40ded |

    Source code(tar.gz)
    Source code(zip)
    osborne-magnetic.csv.xz(2.11 MB)
Owner
Fatiando a Terra Datasets
FAIR sample datasets for use in the Fatiando a Terra project
Fatiando a Terra Datasets
GitHub Repository
Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

16 Nov 19, 2022
A 3D Dense mapping backend library of SLAM based on taichi-Lang designed for the aerial swarm.

TaichiSLAM This project is a 3D Dense mapping backend library of SLAM based Taichi-Lang, designed for the aerial swarm. Intro Taichi is an efficient d

XuHao 230 Dec 19, 2022
Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

Monopol Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV. Background This applicat

Jayaku Briliantio 3 Apr 07, 2022
DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors

DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors By Anargyros Chatzitofis, Dimitris Zarpalas, Stefanos Kollias

tofis 24 Oct 08, 2022
Code for Domain Adaptive Video Segmentation via Temporal Consistency Regularization in ICCV 2021

Domain Adaptive Video Segmentation via Temporal Consistency Regularization Updates 08/2021: check out our domain adaptation for sematic segmentation p

36 Dec 12, 2022
Rank 3 : Source code for OPPO 6G Data Generation Challenge

OPPO 6G Data Generation with an E2E Framework Homepage of OPPO 6G Data Generation Challenge Datasets H1_32T4R.mat H2_32T4R.mat Please put the original

Sen Pei 97 Jan 07, 2023
Repository for the paper "Online Domain Adaptation for Occupancy Mapping", RSS 2020

RSS 2020 - Online Domain Adaptation for Occupancy Mapping Repository for the paper "Online Domain Adaptation for Occupancy Mapping", Robotics: Science

Anthony 26 Sep 22, 2022
PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

Keon Lee 63 Jan 02, 2023
Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Sh

Syed Waqas Zamir 859 Dec 22, 2022
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy Simplex Algorithm is a popular algorithm for linear programmi

Reda BELHAJ 2 Oct 12, 2022
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Agnieszka Mikołajczyk 88 Nov 17, 2022
Pytorch implementation of forward and inverse Haar Wavelets 2D

Pytorch implementation of forward and inverse Haar Wavelets 2D

Sergei Belousov 9 Oct 30, 2022
Flybirds - BDD-driven natural language automated testing framework, present by Trip Flight

Flybird | English Version 行为驱动开发（Behavior-driven development，缩写BDD），是一种软件过程的思想或者

Ctrip, Inc. 706 Dec 30, 2022
Pytorch implementation of XRD spectral identification from COD database

XRDidentifier Pytorch implementation of XRD spectral identification from COD database. Details will be explained in the paper to be submitted to NeurI

Masaki Adachi 4 Jan 07, 2023
Demonstration of transfer of knowledge and generalization with distillation

Distilling-the-Knowledge-in-a-Neural-Network This is an implementation of a part of the paper "Distilling the Knowledge in a Neural Network" (https://

26 Nov 25, 2022
Serving PyTorch 1.0 Models as a Web Server in C++

Serving PyTorch Models in C++ This repository contains various examples to perform inference using PyTorch C++ API. Run git clone https://github.com/W

Onur Kaplan 223 Jan 04, 2023
A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

Squirrel Core Share, load, and transform data in a collaborative, flexible, and efficient way What is Squirrel? Squirrel is a Python library that enab

Merantix Momentum 249 Dec 07, 2022
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi

TheSys Group @ CMU CS 78 Jan 07, 2023
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti

Zhuang AI Group 77 Dec 26, 2022
code for generating data set ES-ImageNet with corresponding training code

es-imagenet-master code for generating data set ES-ImageNet with corresponding training code dataset generator some codes of ODG algorithm The variabl

Ordinarabbit 18 Dec 25, 2022