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
VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data

VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data Introduction Requirements Installation and Setup Supported Hardware and Software R

SigmaLab 1 Jun 14, 2022
Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Deep Learning to Improve Breast

Li Shen 305 Jan 03, 2023
REGTR: End-to-end Point Cloud Correspondences with Transformers

REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme

Zi Jian Yew 108 Dec 17, 2022
The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“.

SCINet This is the original PyTorch implementation of the following work: Time Series is a Special Sequence: Forecasting with Sample Convolution and I

386 Jan 01, 2023
Matplotlib Image labeller for classifying images

mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more

Ian Hunt-Isaak 5 Sep 24, 2022
All public open-source implementations of convnets benchmarks

convnet-benchmarks Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below. Machine: 6-cor

Soumith Chintala 2.7k Dec 30, 2022
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

Cognitive Modeling 20 Dec 18, 2022
Fast RFC3339 compliant Python date-time library

udatetime: Fast RFC3339 compliant date-time library Handling date-times is a painful act because of the sheer endless amount of formats used by people

Simon Pirschel 235 Oct 25, 2022
Small little script to scrape, parse and check for active tor nodes. Can be used as proxies.

TorScrape TorScrape is a small but useful script made in python that scrapes a website for active tor nodes, parse the html and then save the nodes in

5 Dec 04, 2022
Illuminated3D This project participates in the Nasa Space Apps Challenge 2021.

Illuminated3D This project participates in the Nasa Space Apps Challenge 2021.

Eleftheriadis Emmanouil 1 Oct 09, 2021
Object Tracking and Detection Using OpenCV

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, yo

Happy N. Monday 4 Aug 21, 2022
pytorch implementation of GPV-Pose

GPV-Pose Pytorch implementation of GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting. (link) UPDATE A new version

40 Dec 01, 2022
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

DeepLab with PyTorch This is an unofficial PyTorch implementation of DeepLab v2 [1] with a ResNet-101 backbone. COCO-Stuff dataset [2] and PASCAL VOC

Kazuto Nakashima 995 Jan 08, 2023
A full pipeline AutoML tool for tabular data

HyperGBM Doc | 中文 We Are Hiring! Dear folks,we are offering challenging opportunities located in Beijing for both professionals and students who are k

DataCanvas 240 Jan 03, 2023
Latent Execution for Neural Program Synthesis

Latent Execution for Neural Program Synthesis This repo provides the code to replicate the experiments in the paper Xinyun Chen, Dawn Song, Yuandong T

Xinyun Chen 16 Oct 02, 2022
Breast-Cancer-Prediction

Breast-Cancer-Prediction Trying to predict whether the cancer is benign or malignant using REGRESSION MODELS in Python. Team Members NAME ROLL-NUMBER

Shyamdev Krishnan J 3 Feb 18, 2022
Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

#backdoor-HSIC (bd_HSIC) Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment". To generate

Robert Hu 0 Nov 25, 2021
Python code for the paper How to scale hyperparameters for quickshift image segmentation

How to scale hyperparameters for quickshift image segmentation Python code for the paper How to scale hyperparameters for quickshift image segmentatio

0 Jan 25, 2022
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals

Federico Lopez 2 Jan 14, 2022