https://github.com/tnwei/geohack2022

APGCE Geohack 2022 post-hack documentation
https://github.com/tnwei/geohack2022

geoscience hackathon machine-learning python

Last synced: 3 months ago
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APGCE Geohack 2022 post-hack documentation

• Host: GitHub
• URL: https://github.com/tnwei/geohack2022
• Owner: tnwei
• Created: 2022-11-27T03:02:33.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-18T08:59:16.000Z (over 2 years ago)
• Last Synced: 2025-01-09T02:11:21.419Z (4 months ago)
• Topics: geoscience, hackathon, machine-learning, python
• Language: Shell
• Homepage:
• Size: 9.77 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# APGCE Geohack 2022

On Nov 25 to 27, 11 teams of five gathered at Common Ground Bukit Bintang to hack together geo-data science solutions in the span of 48 hours. 

[Challenge info](https://drive.google.com/file/d/1prDO1VuTmucoZCLc3QB7nND2dHoVlAR1/view?usp=share_link) / [Participant handbook](https://drive.google.com/file/d/1prn8LjXLmJY64o9rM7dZ7A4TMT9NgTvr/view?usp=share_link)

Following is a compilation of the hard work done by all teams in this hackathon! The hyperlinks will take you to the individual team repos.

+ Team 1: Interpol: Seismic gathers trace infill

+ [Team 2: TriloBYTES](https://github.com/lawmayy/geohack2022-panna-cotta): Blank seismic infill across offsets using convolutional autoencoders

+ [Team 3: Banana Leaf](https://github.com/haizadtarik/ai-well-top-picker): Machine learning for picking formation tops from well logs

+ Team 4: LogHacker: Bulk density prediction for well logs

+ [Team 5: All Is Wells](https://github.com/AnselmAdrian/geohack): DTS well log prediction and missing log data imputation, for well logs with similar intervals

+ [Team 6: Rock Paper Scissors](https://github.com/maisaramajid/geohack2022-team06): Formation classification from well logs

+ [Team 7: Midnight Spirit](https://github.com/MaulHutama14/geohackaton_UTP_PETRONAS): Missing trace infill using pix2pix GAN

+ Team 8: Milky Way: Well formation top picking

+ Team 9: Lucky Stick: Rapid well tops identification tool

+ Team 10: To The Sea: Machine learning assisted sonic log and reservoir properties prediction

+ [Team 11: Run Data Run](https://github.com/haikalbaik/GeoHackathon2022): Formation top prediction with machine learning