{ "id": 583908, "title": "Are the velocities always between 1500 m/s and 4500 m/s?", "url": "/competitions/waveform-inversion/discussion/583908", "author_name": "", "post_date": "2025-06-10T10:29:59.746930900Z", "votes": 2, "comment_count": 3, "views": 0, "content": "

Are the velocities always between 1500 m/s and 4500 m/s?

", "messages": [ { "id": "3221049", "postDate": "06/10/2025 10:29:59", "content": "

Are the velocities always between 1500 m/s and 4500 m/s?

", "rawMarkdown": "Are the velocities always between 1500 m/s and 4500 m/s?", "votes": null }, { "id": "3221060", "postDate": "06/10/2025 10:52:36", "content": "

yes @jamalsaeedi

", "rawMarkdown": "yes @jamalsaeedi", "votes": null }, { "id": "3221145", "postDate": "06/10/2025 13:22:45", "content": "

FlatVel_A min: 1500.00 max: 4500.00 \nFlatVel_B min: 1500.00 max: 4500.00 \nStyle_A min: 1515.00 max: 4448.00 \nStyle_B min: 1522.00 max: 4428.00 \nCurveVel_A min: 1500.00 max: 4500.00 \nCurveVel_B min: 1500.00 max: 4500.00\nCurveFault_A | min: 1500.00 | max: 4500.00 | \nCurveFault_B | min: 1500.00 | max: 4500.00 |\nFlatFault_A | min: 1500.00 | max: 4500.00 | \nFlatFault_B | min: 1500.00 | max: 4500.00 |\n

", "rawMarkdown": "`FlatVel_A min: 1500.00 max: 4500.00 \nFlatVel_B min: 1500.00 max: 4500.00 \nStyle_A min: 1515.00 max: 4448.00 \nStyle_B min: 1522.00 max: 4428.00 \nCurveVel_A min: 1500.00 max: 4500.00 \nCurveVel_B min: 1500.00 max: 4500.00\nCurveFault_A | min: 1500.00 | max: 4500.00 | \nCurveFault_B | min: 1500.00 | max: 4500.00 |\nFlatFault_A | min: 1500.00 | max: 4500.00 | \nFlatFault_B | min: 1500.00 | max: 4500.00 |\n`", "votes": null }, { "id": "3221509", "postDate": "06/11/2025 05:32:53", "content": "

You can have even more detailed info about the OpenFWI dataset below.
\nHelped me while experimenting normalization techniques, interpolation and many more.

\n
{\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    }\n}\n
", "rawMarkdown": "You can have even more detailed info about the OpenFWI dataset below.\nHelped me while experimenting normalization techniques, interpolation and many more. \n\n```\n{\n \"flatvel-a\": {\n \"data_min\": -26.95,\n \"data_max\": 52.77,\n \"data_mean\": -3.3804e-05,\n \"data_std\": 1.4797,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2782.0442,\n \"label_std\": 786.1557,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvevel-a\": {\n \"data_min\": -27.11,\n \"data_max\": 55.10,\n \"data_mean\": -5.0961e-05,\n \"data_std\": 1.4870,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 500,\n \"label_mean\": 2788.1562,\n \"label_std\": 794.2432,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatvel-b\": {\n \"data_min\": -27.17,\n \"data_max\": 56.05,\n \"data_mean\": -0.0002,\n \"data_std\": 1.7832,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3001.3389,\n \"label_std\": 866.6636,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvevel-b\": {\n \"data_min\": -29.04,\n \"data_max\": 57.03,\n \"data_mean\": -0.0002,\n \"data_std\": 1.7648,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3000.5669,\n \"label_std\": 865.4404,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n\t\"flatfault-a\": {\n \"data_min\": -26.10,\n \"data_max\": 50.86,\n \"data_mean\": -0.00043503073,\n \"data_std\": 1.5410482,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3088.6873,\n \"label_std\": 855.37024,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvefault-a\": {\n \"data_min\": -26.48,\n \"data_max\": 52.32,\n \"data_mean\": -0.00045603843,\n \"data_std\": 1.5448948,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3082.6616,\n \"label_std\": 852.38995,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatfault-b\": {\n \"data_min\": -24.86,\n \"data_max\": 50.28,\n \"data_mean\": -0.0001,\n \"data_std\": 1.4952,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 500,\n \"label_mean\": 3055.4231,\n \"label_std\": 875.8992,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvefault-b\": {\n \"data_min\": -24.93,\n \"data_max\": 50.98,\n \"data_mean\": -8.882544e-05,\n \"data_std\": 1.50228,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3035.5576,\n \"label_std\": 890.48785,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"style-a\": {\n \"data_min\": -24.96,\n \"data_max\": 48.93,\n \"data_mean\": 0.00024991733,\n \"data_std\": 1.4618,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2728.5144,\n \"label_std\": 665.83215,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"style-b\": {\n \"data_min\": -23.76,\n \"data_max\": 46.01,\n \"data_mean\": 0.00013498258,\n \"data_std\": 1.4579,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2837.3164,\n \"label_std\": 637.6763,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatvel-tutorial\": {\n \"data_min\": -26.95,\n \"data_max\": 52.77,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 120,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n }\n}\n```", "votes": null } ], "comments": [ { "id": 3221060, "author_name": "seshurajup", "author_url": "", "post_date": "06/10/2025 10:52:36", "content": "

yes @jamalsaeedi

", "votes": null, "replies": [] }, { "id": 3221145, "author_name": "waterjoe", "author_url": "", "post_date": "06/10/2025 13:22:45", "content": "

FlatVel_A min: 1500.00 max: 4500.00 \nFlatVel_B min: 1500.00 max: 4500.00 \nStyle_A min: 1515.00 max: 4448.00 \nStyle_B min: 1522.00 max: 4428.00 \nCurveVel_A min: 1500.00 max: 4500.00 \nCurveVel_B min: 1500.00 max: 4500.00\nCurveFault_A | min: 1500.00 | max: 4500.00 | \nCurveFault_B | min: 1500.00 | max: 4500.00 |\nFlatFault_A | min: 1500.00 | max: 4500.00 | \nFlatFault_B | min: 1500.00 | max: 4500.00 |\n

", "votes": null, "replies": [] }, { "id": 3221509, "author_name": "", "author_url": "", "post_date": "06/11/2025 05:32:53", "content": "

You can have even more detailed info about the OpenFWI dataset below.
\nHelped me while experimenting normalization techniques, interpolation and many more.

\n
{\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    },\n     {\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         ,\n         \n    }\n}\n
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3221049": "Are the velocities always between 1500 m/s and 4500 m/s?", "3221060": "yes @jamalsaeedi", "3221145": "`FlatVel_A min: 1500.00 max: 4500.00 \nFlatVel_B min: 1500.00 max: 4500.00 \nStyle_A min: 1515.00 max: 4448.00 \nStyle_B min: 1522.00 max: 4428.00 \nCurveVel_A min: 1500.00 max: 4500.00 \nCurveVel_B min: 1500.00 max: 4500.00\nCurveFault_A | min: 1500.00 | max: 4500.00 | \nCurveFault_B | min: 1500.00 | max: 4500.00 |\nFlatFault_A | min: 1500.00 | max: 4500.00 | \nFlatFault_B | min: 1500.00 | max: 4500.00 |\n`", "3221509": "You can have even more detailed info about the OpenFWI dataset below.\nHelped me while experimenting normalization techniques, interpolation and many more. \n\n```\n{\n \"flatvel-a\": {\n \"data_min\": -26.95,\n \"data_max\": 52.77,\n \"data_mean\": -3.3804e-05,\n \"data_std\": 1.4797,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2782.0442,\n \"label_std\": 786.1557,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvevel-a\": {\n \"data_min\": -27.11,\n \"data_max\": 55.10,\n \"data_mean\": -5.0961e-05,\n \"data_std\": 1.4870,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 500,\n \"label_mean\": 2788.1562,\n \"label_std\": 794.2432,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatvel-b\": {\n \"data_min\": -27.17,\n \"data_max\": 56.05,\n \"data_mean\": -0.0002,\n \"data_std\": 1.7832,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3001.3389,\n \"label_std\": 866.6636,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvevel-b\": {\n \"data_min\": -29.04,\n \"data_max\": 57.03,\n \"data_mean\": -0.0002,\n \"data_std\": 1.7648,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3000.5669,\n \"label_std\": 865.4404,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n\t\"flatfault-a\": {\n \"data_min\": -26.10,\n \"data_max\": 50.86,\n \"data_mean\": -0.00043503073,\n \"data_std\": 1.5410482,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3088.6873,\n \"label_std\": 855.37024,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvefault-a\": {\n \"data_min\": -26.48,\n \"data_max\": 52.32,\n \"data_mean\": -0.00045603843,\n \"data_std\": 1.5448948,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3082.6616,\n \"label_std\": 852.38995,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatfault-b\": {\n \"data_min\": -24.86,\n \"data_max\": 50.28,\n \"data_mean\": -0.0001,\n \"data_std\": 1.4952,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 500,\n \"label_mean\": 3055.4231,\n \"label_std\": 875.8992,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"curvefault-b\": {\n \"data_min\": -24.93,\n \"data_max\": 50.98,\n \"data_mean\": -8.882544e-05,\n \"data_std\": 1.50228,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 3035.5576,\n \"label_std\": 890.48785,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"style-a\": {\n \"data_min\": -24.96,\n \"data_max\": 48.93,\n \"data_mean\": 0.00024991733,\n \"data_std\": 1.4618,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2728.5144,\n \"label_std\": 665.83215,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"style-b\": {\n \"data_min\": -23.76,\n \"data_max\": 46.01,\n \"data_mean\": 0.00013498258,\n \"data_std\": 1.4579,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"label_mean\": 2837.3164,\n \"label_std\": 637.6763,\n \"file_size\": 500,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n },\n \"flatvel-tutorial\": {\n \"data_min\": -26.95,\n \"data_max\": 52.77,\n \"label_min\": 1500,\n \"label_max\": 4500,\n \"file_size\": 120,\n \"nbc\": 120,\n \"dx\": 10,\n \"nt\": 1000,\n \"dt\": 1e-3,\n \"f\": 15,\n \"n_grid\": 70,\n \"ns\": 5,\n \"ng\": 70,\n \"sz\": 10,\n \"gz\": 10\n }\n}\n```" }, "source": "meta" }