Komputasi dan Visualisasi Hamburbalik Akustik Multibeam Echosounder Menggunakan Angular Range Analysis (ARA) di Perairan Pulau Panjang

Abstract

Pemanfaatan data hamburbalik multibeam echosounder (MBES) untuk pemetaan substrat dasar di Perairan Pulau Panjang masih belum banyak dilakukan. Penelitian ini bertujuan menganalisis nilai hamburbalik menggunakan metode ARA, memetakan sebaran substrat dasar, dan membandingkan tingkat kesesuaian klasifikasi substrat berdasarkan data MBES terhadap hasil analisis laboratorium. Data diperoleh menggunakan MBES SeaBat T20-P (400 kHz) dan dianalisis menggunakan software FMGT melalui inversi ARA untuk memperoleh nilai hamburbalik, dan estimasi nilai ukuran butir, impedansi akustik, dan kekasaran permukaan. Nilai hamburbalik berkisar antara -42 dB hingga -16 dB. Substrat dasar terdiri atas enam tipe sedimen, yaitu pasir, pasir berlempung, pasir berlanau, lanau, sedimen berkerikil, dan lempung. Tingkat kesesuaian klasifikasi sedimen hasil ARA terhadap analisis laboratorium sebesar 77.78% (7 dari 9 titik stasiun sesuai). Hasil ini menunjukkan bahwa metode ARA mampu mengklasifikasikan substrat dasar berdasarkan karakteristik hamburbalik akustik sehingga dapat mendukung pemetaan substrat dasar perairan.

The application of multibeam echosounder (MBES) backscatter data for substrate mapping in the waters of Pulau Panjang remains limited. This study aimed to analyze backscatter values using the ARA method, to map the spatial distribution of seabed substrates, and to evaluate the level of agreement between substrate classifications derived from MBES data and laboratory sediment analysis. Acoustic data were acquired using a SeaBat T20-P MBES operating at a frequency of 400 kHz and were processed using FMGT software through ARA inversion to obtain backscatter values and estimates of grain size, acoustic impedance, and seafloor roughness. Backscatter values ranged from -42 dB to -16 dB. The seabed substrate was classified into six sediment types, namely sand, clayey sand, silty sand, silt, gravelly sediment, and clay. The classification derived from ARA showed an agreement of 77.78% with the results of laboratory analysis (7 of 9 stations in agreement). These findings indicate that the ARA method is capable of classifying seabed substrates based on acoustic backscatter characteristics, thereby supporting the mapping of seabed substrates in coastal waters.