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
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

IDRL 105 Dec 17, 2022
This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Alex Gorodnitskiy 11 Mar 20, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Jason Ren 22 Nov 23, 2022
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representa

Bats Research 94 Nov 21, 2022
Implementation of Bagging and AdaBoost Algorithm

Bagging-and-AdaBoost Implementation of Bagging and AdaBoost Algorithm Dataset Red Wine Quality Data Sets For simplicity, we will have 2 classes of win

Zechen Ma 1 Nov 01, 2021
Fast and robust clustering of point clouds generated with a Velodyne sensor.

Depth Clustering This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velo

Photogrammetry & Robotics Bonn 957 Dec 21, 2022
Implementations of LSTM: A Search Space Odyssey variants and their training results on the PTB dataset.

An LSTM Odyssey Code for training variants of "LSTM: A Search Space Odyssey" on Fomoro. Check out the blog post. Training Install TensorFlow. Clone th

Fomoro AI 95 Apr 13, 2022
Episodic-memory - Ego4D Episodic Memory Benchmark

Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p

3 Feb 18, 2022
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
Physics-informed Neural Operator for Learning Partial Differential Equation

PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol

107 Jan 02, 2023
A tiny, pedagogical neural network library with a pytorch-like API.

candl A tiny, pedagogical implementation of a neural network library with a pytorch-like API. The primary use of this library is for education. Use th

Sri Pranav 3 May 23, 2022
Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation, arXiv 2021

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper "Hypercorrelation Squeeze for Few-Shot Segmentation" by Juh

Juhong Min 165 Dec 28, 2022
95.47% on CIFAR10 with PyTorch

Train CIFAR10 with PyTorch I'm playing with PyTorch on the CIFAR10 dataset. Prerequisites Python 3.6+ PyTorch 1.0+ Training # Start training with: py

5k Dec 30, 2022
DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

DSAC* for Visual Camera Re-Localization (RGB or RGB-D) Introduction Installation Data Structure Supported Datasets 7Scenes 12Scenes Cambridge Landmark

Visual Learning Lab 143 Dec 22, 2022
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

CARLA-Roach This is the official code release of the paper End-to-End Urban Driving by Imitating a Reinforcement Learning Coach by Zhejun Zhang, Alexa

Zhejun Zhang 118 Dec 28, 2022
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation

DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation This repository is the implementation of DynaTune paper. This folder

4 Nov 02, 2022
Contra is a lightweight, production ready Tensorflow alternative for solving time series prediction challenges with AI

Contra AI Engine A lightweight, production ready Tensorflow alternative developed by Styvio styvio.com » How to Use · Report Bug · Request Feature Tab

styvio 14 May 25, 2022
Implementations of polygamma, lgamma, and beta functions for PyTorch

lgamma Implementations of polygamma, lgamma, and beta functions for PyTorch. It's very hacky, but that's usually ok for research use. To build, run: .

Rachit Singh 24 Nov 09, 2021
Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create

Vector AI 267 Dec 23, 2022