Karakteristik Sedimen Permukaan Dasar Laut Berdasarkan Data Seismik Di Perairan Bengkalis Utara
Bibliographic record
Abstract
The northern waters of Bengkalis Island, Riau Archipelago, represent a coastal region vulnerable to abrasion and anthropogenic pressures such as sand mining and marine traffic. This study employs an integrative approach combining seismic reflection methods and grain-size analysis from grab-sampler data to quantitatively assess the characteristics of surface seafloor sediments. Seismic sections from three profiles reveal a relatively flat seabed topography, with depths ranging from 9.5 to 14.9 meters and total sediment layer thickness reaching up to 76.5 meters. Data processing through AGC, FFT, bandpass filtering, and sparse spike deconvolution produced sharp and informative seismic sections, with estimated reflection coefficient values ranging from 0,245 to 0,257. The interpretation results indicate consistency between the reflection coefficients extracted from seismic data and the theoretical values derived from sediment grain sizes of 0,021–0,025 mm, classified as coarse silt. These findings reinforce the effectiveness of hydroacoustic methods in sediment characterization within coastal environments. This approach is considered efficient, broadly spatially covered, and holds strategic potential for shallow marine sedimentology studies. Perairan Bengkalis Utara, Kepulauan Riau, merupakan wilayah pesisir yang rentan terhadap abrasi dan tekanan antropogenik seperti penambangan pasir dan pelayaran. Penelitian ini menggunakan pendekatan integratif antara metode seismik refleksi dan analisis ukuran butir sedimen dari penginti comot (grab sampler) untuk mengkaji karakteristik sedimen permukaan dasar laut secara kuantitatif. Penampang seismik dari tiga lintasan menunjukkan topografi dasar laut yang relatif datar, dengan kedalaman seabed antara 9,5 hingga 14,9 meter dan ketebalan total lapisan sedimen mencapai 76,5 meter. Pengolahan data melalui tahapan AGC, FFT, bandpass filter, dan dekonvolusi menghasilkan visualisasi penampang yang tajam dan informatif, serta estimasi koefisien refleksi dalam kisaran 0,245–0,257. Hasil interpretasi menunjukkan kesesuaian nilai koefisien refleksi dari data seismik dengan nilai teoritis berdasarkan ukuran butir sedimen sebesar 0,021–0,025 mm, yang diklasifikasikan sebagai lanau kasar. Temuan ini memperkuat efektivitas metode hidroakustik dalam karakterisasi sedimen di wilayah pesisir. Pendekatan ini dinilai efisien, luas cakupannya, dan potensial menjadi metode strategis dalam studi sedimentologi laut dangkal.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".