PENINGKATAN KUALITAS DATA PRE STACK TIME \nMIGRATION MENGGUNAKAN CADZOW FILTERING \nUNTUK IDENTIFIKASI SERTA KARAKTERISASI \nENDAPAN CHANNEL DI DAERAH NOVA SCOTIA
Bibliographic record
Abstract
Peningkatan kualitas data seismik menjadi target utama dalam eksplorasi \nhidrokarbon dengan mengaplikasikan ide serta metode – metode baru. Salah \nsatunya adalah Cadzow filtering yang efektif dalam mereduksi random noise. \nSelain itu, penerapan Cadzow filtering juga untuk meningkatkan rasio signal to \nnoise. Data hasil pemrosesan Cadzow filtering dapat ditampilkan secara analisis \nsimultan dengan penggabungan tiga warna frekuensi menjadi satu pencitraan \nberdasarkan dekomposisi spektral yang dikenal sebagai R-G-B blending. Setelah \nmendapatkan struktur yang diperkirakan sebagai endapan channel, maka \ndilakukan proses inversi dengan bantuan dua buah data sumur. Nilai korelasi \nsumur AND-30 adalah 0, 863 dan sumur AND-41 adalah 0, 874. Inversi yang \ndigunakan adalah inversi model based dengan hasil karakterisasi berupa slice \npseudo gamma ray dengan nilai 50 – 58 API, pseudo densitas dengan nilai 2, 3 – \n2, 4 g/cm \n, serta pseudo porositas dengan nilai 15 - 18%. Informasi ini dapat \nberguna sebagai bahan pertimbangan untuk pengembangan lapangan selanjutnya.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".