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

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

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

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

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

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

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

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

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

"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

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
🎃 Core identification module of AI powerful point reading system platform.

ppReader-Kernel Intro Core identification module of AI powerful point reading system platform. Usage 硬件: Windows10、GPU:nvdia GTX 1060 、普通RBG相机 软件: con

CrashKing 1 Jan 11, 2022
a reimplementation of UnFlow in PyTorch that matches the official TensorFlow version

pytorch-unflow This is a personal reimplementation of UnFlow [1] using PyTorch. Should you be making use of this work, please cite the paper according

Simon Niklaus 134 Nov 20, 2022
This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

Word-Level Coreference Resolution This is a repository with the code to reproduce the experiments described in the paper of the same name, which was a

79 Dec 27, 2022
Implements the training, testing and editing tools for "Pluralistic Image Completion"

Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "

Chuanxia Zheng 615 Dec 08, 2022
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation, ICMR 2021

This is the repo for the paper: Leveraging Two Types of Global Graph for Sequential Fashion Recommendation Requirements OS: Ubuntu 16.04 or higher ver

Yujuan Ding 10 Oct 10, 2022
The story of Chicken for Club Bing

Chicken Story tl;dr: The time when Microsoft banned my entire country for cheating at Club Bing. (A lot of the details are from memory so I've recreat

Eyal 142 May 16, 2022
Implementation of our paper 'RESA: Recurrent Feature-Shift Aggregator for Lane Detection' in AAAI2021.

RESA PyTorch implementation of the paper "RESA: Recurrent Feature-Shift Aggregator for Lane Detection". Our paper has been accepted by AAAI2021. Intro

137 Jan 02, 2023
Improving Calibration for Long-Tailed Recognition (CVPR2021)

MiSLAS Improving Calibration for Long-Tailed Recognition Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia [arXiv] [slide] [BibTeX] Introductio

Jia Research Lab 116 Dec 20, 2022
Functional TensorFlow Implementation of Singular Value Decomposition for paper Fast Graph Learning

tf-fsvd TensorFlow Implementation of Functional Singular Value Decomposition for paper Fast Graph Learning with Unique Optimal Solutions Cite If you f

Sami Abu-El-Haija 14 Nov 25, 2021
PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

Sangchun Ha 24 Nov 24, 2022
[Nature Machine Intelligence' 21] "Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence"

[UCADI] COVID-19 Diagnosis With Federated Learning Intro We developed a Federated Learning (FL) Framework for global researchers to collaboratively tr

HUST EIC AI-LAB 30 Dec 12, 2022
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Tutorials, assignments, and competitions for MIT Deep Learning related courses.

MIT Deep Learning This repository is a collection of tutorials for MIT Deep Learning courses. More added as courses progress. Tutorial: Deep Learning

Lex Fridman 9.5k Jan 07, 2023
Bottom-up Human Pose Estimation

Introduction This is the official code of Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation. This paper has been accepted to CVPR2

108 Dec 01, 2022
Galileo library for large scale graph training by JD

近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提

JD Galileo Team 128 Nov 29, 2022
Feature board for ERPNext

ERPNext Feature Board Feature board for ERPNext Development Prerequisites k3d kubectl helm bench Install K3d Cluster # export K3D_FIX_CGROUPV2=1 # use

Revant Nandgaonkar 16 Nov 09, 2022
this is a lite easy to use virtual keyboard project for anyone to use

virtual_Keyboard this is a lite easy to use virtual keyboard project for anyone to use motivation I made this for this year's recruitment for RobEn AA

Mohamed Emad 3 Oct 23, 2021
Algorithmic encoding of protected characteristics and its implications on disparities across subgroups

Algorithmic encoding of protected characteristics and its implications on disparities across subgroups This repository contains the code for the paper

Team MIRA - BioMedIA 15 Oct 24, 2022
Code for the paper Task Agnostic Morphology Evolution.

Task-Agnostic Morphology Optimization This repository contains code for the paper Task-Agnostic Morphology Evolution by Donald (Joey) Hejna, Pieter Ab

Joey Hejna 18 Aug 04, 2022
A implemetation of the LRCN in mxnet

A implemetation of the LRCN in mxnet ##Abstract LRCN is a combination of CNN and RNN ##Installation Download UCF101 dataset ./avi2jpg.sh to split the

44 Aug 25, 2022