{"id":23955561,"url":"https://github.com/tnwei/geohack2022","last_synced_at":"2026-04-24T12:05:59.439Z","repository":{"id":129158798,"uuid":"571062864","full_name":"tnwei/geohack2022","owner":"tnwei","description":"APGCE Geohack 2022 post-hack documentation","archived":false,"fork":false,"pushed_at":"2022-12-18T08:59:16.000Z","size":10,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-28T19:51:21.114Z","etag":null,"topics":["geoscience","hackathon","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tnwei.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-11-27T03:02:33.000Z","updated_at":"2024-01-30T02:05:03.000Z","dependencies_parsed_at":null,"dependency_job_id":"a52c2233-a4f3-4888-8fc4-5f6bad6a4b66","html_url":"https://github.com/tnwei/geohack2022","commit_stats":{"total_commits":12,"total_committers":2,"mean_commits":6.0,"dds":"0.41666666666666663","last_synced_commit":"60632f7f647c0f2dd127b1fb65f0169d4d7ef9cd"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tnwei/geohack2022","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tnwei%2Fgeohack2022","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tnwei%2Fgeohack2022/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tnwei%2Fgeohack2022/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tnwei%2Fgeohack2022/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tnwei","download_url":"https://codeload.github.com/tnwei/geohack2022/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tnwei%2Fgeohack2022/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32222504,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-24T10:26:35.452Z","status":"ssl_error","status_checked_at":"2026-04-24T10:25:27.643Z","response_time":64,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["geoscience","hackathon","machine-learning","python"],"created_at":"2025-01-06T15:35:37.734Z","updated_at":"2026-04-24T12:05:59.424Z","avatar_url":"https://github.com/tnwei.png","language":"Shell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# APGCE Geohack 2022\n\nOn 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. \n\n[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)\n\nFollowing is a compilation of the hard work done by all teams in this hackathon! The hyperlinks will take you to the individual team repos.\n\n+ Team 1: Interpol: Seismic gathers trace infill\n+ [Team 2: TriloBYTES](https://github.com/lawmayy/geohack2022-panna-cotta): Blank seismic infill across offsets using convolutional autoencoders\n+ [Team 3: Banana Leaf](https://github.com/haizadtarik/ai-well-top-picker): Machine learning for picking formation tops from well logs\n+ Team 4: LogHacker: Bulk density prediction for well logs\n+ [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\n+ [Team 6: Rock Paper Scissors](https://github.com/maisaramajid/geohack2022-team06): Formation classification from well logs\n+ [Team 7: Midnight Spirit](https://github.com/MaulHutama14/geohackaton_UTP_PETRONAS): Missing trace infill using pix2pix GAN\n+ Team 8: Milky Way: Well formation top picking\n+ Team 9: Lucky Stick: Rapid well tops identification tool\n+ Team 10: To The Sea: Machine learning assisted sonic log and reservoir properties prediction\n+ [Team 11: Run Data Run](https://github.com/haikalbaik/GeoHackathon2022): Formation top prediction with machine learning\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftnwei%2Fgeohack2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftnwei%2Fgeohack2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftnwei%2Fgeohack2022/lists"}