MEASUREMENT AND ANALYSIS OF ACOUSTIC BACKSCATTER USING MULTIBEAM ECHOSOUNDER TECHNOLOGY FOR SEDIMENT CLASSIFICATION OF THE GULF OF PALU

  • Rizqi Ayu Farihah Sekolah Pascasarjana Program Studi Teknologi Kelautan Fakultas Perikanan dan Ilmu Kelautan Institut Pertanian Bogor
  • Henry Munandar Manik Program Studi Teknologi Kelautan Departemen Ilmu dan Teknologi Kelautan Fakultas Perikanan dan Ilmu Kelautan Institut Pertanian Bogor https://orcid.org/0000-0002-4418-5815
  • Gentio Harsono Pusat Hidrografi dan Oseanografi TNI AL, Jakarta 14310
Keywords: backscatter, multibeam echosounder, Palu gulf, sediment type, SVM

Abstract

Backscattering can describe sediments' condition in the bottom waters, including the grain size of the bottom waters sediments. This study aims to detect, classify, and estimate the bottom watershed based on backscattering values using Angular Response Analysis (ARA)  and Support Vector Machine (SVM) so that a spatial map of sediment distribution is obtained in Gulf of Palu. Bathymetry data and backscattering intensity were taken on 5-9 October 2018 using the multibeam echosounder Kongsberg EM 302 with a frequency of 30 kHz, and ten sediment samples in 2012 belong to PUSHIDROSAL. The sediment distribution from the Gulf of Palu with the ARA method is dominated by sand and silt. Simultaneously, the distribution of sediments using the SVM method is dominated by silty sand, silt, and sand. Accuracy test results for the ARA methods produce an overall accuracy with a value of 50%. In comparison, Accuracy test results for the SVM method produce an overall accuracy with a value of 60%. The prediction of the basic types of waters in Palu Bay that are most close to the actual state is the prediction results using the SVM method, namely silt, silt, and sand.

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Author Biographies

Rizqi Ayu Farihah, Sekolah Pascasarjana Program Studi Teknologi Kelautan Fakultas Perikanan dan Ilmu Kelautan Institut Pertanian Bogor
Mahasiswa Pascasarjana IPB Program Master angkatan 2017
Henry Munandar Manik, Program Studi Teknologi Kelautan Departemen Ilmu dan Teknologi Kelautan Fakultas Perikanan dan Ilmu Kelautan Institut Pertanian Bogor

Prof. Henry M Manik, Ph.D

Ketua Program Studi Teknologi Kelautan

Departemen Ilmu dan Teknologi Kelautan
Fakultas Perikanan dan Ilmu Kelautan
Institut Pertanian Bogor

Gentio Harsono, Pusat Hidrografi dan Oseanografi TNI AL, Jakarta 14310

Letkol Laut (KH) Dr. Gentio Harsono, S.T., M.Si

Staf Ahli Dis Osemet

Pusat Hidrografi dan Oseanografi , TNI AL

References

Adi, A.P., H.M. Manik, & S. Pujiyati. 2016. Integrasi data multibeam batimetri dan mosaik backscatter untuk klasifikasi tipe sedimen (data integration bathymetry multibeam and backscatter mosaic for classification type of sedimen). J. Teknologi Perikanan dan Kelautan, 7(1): 77–84. https://doi.org/10.24319/jtpk.7.77-84

Akbar, H., S. Pujiyati, & M. Natsir. 2013. Hubungan tipe dasar perairan dengan distribusi ikan demersal di perairan Pangkajene Sulawesi Selatan. J. Teknologi Perikanan dan Kelautan, 4(1): 31-39. https://doi.org/10.24319/jtpk.4.31-39

Akbar, K., D.G. Pratomo, & Khomsin. 2017. Analisis nilai hambur balik sedimen permukaan dasar perairan menggunakan data multibeam echosounder EM302. J. Teknik ITS, 6(2): 154-157. https://doi.org/10.12962/j23373539.v6i2.24415

Bishop, C.M. 2006. Pattern recognition and machine learning. Springer science and business media. Singapore. 738 p

Draper, N. dan H. Smith. 1992. Analisis regresi terapan. Gramedia. Jakarta. 671 p.

Debese, N., R. Moitié, & N. Seube. 2012. Multibeam echosounder data cleaning through a hierarchic adaptive and robust local surfacing. J. Computer and Geoscience, 46: 330–339. https://doi.org/10.1016/j.cageo.2012.01.012

Fahrulian, H.M. Manik, & H. Djoko. 2013. Dimensi gunung bawah laut dengan menggunakan multibeam echo-sounder di perairan Bengkulu. J. Ilmu dan Teknologi Kelautan Tropis, 5(1): 93-102. https://doi.org/10.29244/jitkt.v5i1.7754

Fonseca, L., C. Brown, B. Calder, L. Mayer, & Y. Rzhanov. 2009. Angular range analysis of acoustic themes from stanton banks ireland: a link between visual interpretation and multibeam echosounder angular signatures. Applied Acoustics J., 70(10): 1298-1304. https://doi.org/10.1016/j.apacoust.2008.09.008

Fonseca, L. & L.A. Mayer. 2007. Remote estimation of surficial seafloor properties through the application angular range analysis to multibeam sonar data. Marine Geophysical Research, 28: 119-126. https://doi.org/10.1007/s11001.007.9019.4

Gusman, A.R., P. Supendi, A.D. Nugraha, W. Power, H. Latief, H. Sunendar, S. Widiyantoro, Daryono, S.H. Wiyono, & A. Hakim. 2019. Source model for the tsunami inside palu bay following the 2018 Palu earthquake, Indonesia. Geophysical Research Letters, 46(15): 8721–8730. https://doi.org/10.1029/2019GL082717

Hamuna, B., S. Pujiyati, N.M.N. Natih, & L. Dimara. 2018. Analisis hambur balik akustik untuk klasifikasi dan pemetaan substrat dasar perairan di Teluk Yos Sudarso, Kota Jayapura. J. Ilmu dan Teknologi Kelautan Tropis, 10(2): 291-300. https://doi.org/10.29244/jitkt.v10i2.24045

Hasan, R.C., D. Ierodiaconou, L. Laurenson, & A. Schimel. 2014. Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping. PLOS One J., 9(5): 1–14. https://doi.org/10.1371/journal.pone.0097339

Kurniawan. 2008. R: a language and environment for statistical computing. R foundation for statistical computing. Austria. 257 p.

Kusumatuti, I.D., D. Anugrah, A. Listalatu, & R. Farhan. 2018. Menata kembali pemukiman penduduk di sulawesi tengah dengan rencana terpadu. Badan pengembang infrastruktur wilayah (BPIW) kementerian PUPR. Jakarta. 73 p.

Landis, J.R. & G.G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics, 33: 159‐174. https://doi.org/10.2307/2529310

Ludtke, A., K. Jerosch, O. Herzog, & Schluter. 2012. Development of a machine learning techniquw for automatic analysis of seafloor image data: case example, Pogonophora coverage at mud volcanoes. Computer and Geosciences J., 39: 120-128. https://doi.org/10.1016/j.cageo.2011.06.020

MacDonald, A. & C. Collins. 2008. Taking geocoder to work. In: MacDonald, A. (ed). Proceedings of the Shallow Survey Conference 2008. Portsmouth. 1-9 pp.

Mandal, S., S. Rao, N. Harish, & Lokesha. 2012. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models. International J. Archit Oc Engineering, 4: 112-122. https://doi.org/10.2478/IJNAOE-2013-0082

Manik, H.M. 2016. Acoustical measurement and biot model for coral reef detection and quantification. Advances in Acoustics and Vibration, 2016(235061511): 1-11. https://doi.org/10.1155/2016/2350615

Manik, H.M. 2015. Acoustic characterization of fish and seabed using underwater acoustic technology in Seribu Island Indonesia. Marine Science Research & Development J., 5(1): 1-9. https://doi.org/10.4172/2155-9910.1000157

Manik, H.M. 2012. Seabed identification and characterization using sonar. Advances in Acoustics and Vibration, 2012(532458): 1-5. https://doi.org/10.1155/2012/532458

Manik, H.M., M. Furusawa, & K. Amakasu. 2006. Measurement of sea bottom surface backscattering strength by quantitative echo sounder. Fisheries Science J., 72(3): 503-512. https://doi.org/10.1111/j.1444-2906.2006.01178.x

Nasril, C.H. 2013. Kajian upaya peningkatan produksi bongkar muat di pelabuhan pantoloan dalam rangka menekan lama kapal di tambatan. Warta Penelitian Perhubungan, 25(5): 328–336. https://doi.org/10.25104/w

Ningsih, E.N., F. Supriyadi, & S. Nurdawati. 2013. Pengukuran dan analisis nilai hambur balik akustik untuk klasifikasi dasar perairan Delta Mahakam. J. Penelitian Perikanan Indonesia, 19(3): 139-146. https://doi.org/10.15578/jppi.19.3.2013.139-146

Park, Y., S. Lee, & S. Jung. 2011. Characterization of backscattering signal of 300 kHz multibeam echo sounder. Proceeding of Symposium on Ultrasonic Electronics, 32: 289–290.

Paundanan, M., E. Riani, & S. Anwar. 2015. Kontaminasi logam berat merkuri (Hg) dan timbal (Pb) pada air, sedimen dan ikan selar tetengkek (Megalaspis cordyta) di Teluk Palu, Sulawesi Tengah. J. Pengelolaan Sumberdaya Alam dan Lingkungan, 5(2): 161-168. https://doi.org/10.19081/jpsl.5.2.161

Pujiyati, S., S. Hartati, & W. Priyono. 2010. Efek ukuran butiran, kekasaran, dan kekerasan dasar perairan terhadap nilai hambur balik hasil deteksi hydroakustik. J. Ilmu dan Teknologi Kelautan Tropis, 2(1): 59-67. https://doi.org/10.29244/jitkt.v2i1.7863

Rahman, A., Arfiah, & Y. Mudin. 2017. Model distribusi salinitas dan temperatur air laut dengan menggunakan metode numerik 2D di muara sungai toaya dan muara sungai palu. Gravitasi J., 16(2): 8–14. https://doi.org/10.22487/gravitasi.v18i2

Robotham, H., P. Bosch, J.C. Gutiérrez-Estrada, J. Castillo, & I. Pulido-Calvo. 2010. Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish Research J., 102: 115–122. https://doi.org/10.1016/j.fishres.2009.10.015

Rzhanov, Y., L. Fonseca, & L.A. Mayer. 2012. Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data. J. Computer and Geoscience, 41: 181–187. https://doi.org/10.1016/j.cageo.2011.09.001

Sarwono, J. 2006. Metode penelitian kuantitatif dan kualitatif. Graha Ilmu. Yogyakarta. 286 p.

Shahua A. 2008. Introduction to machine learning. School of Computer Science and Engineering. The Hebrew University Press. Jerusalem.148 p.

Schnare, T. 2014. Caris hips & sips 8.0 manuals for hydrography and survey use. MGEO. Canada. 24 p.

Stanic, S., K.B. Briggs, P. Fleischer, W.B. Sawyer, & R.I. Ray. 1989. High frequency acoustic backscattering from a coarse shell ocean bottom. J. of the Acoustical Society of America, 85(1): 125-136. https://doi.org/10.1121/1.397720

Vapnik, V. 1995. The nature of statistical learning theory. Springer-Verlag. New York. 299 p.

Widhiarso W. 2005. Mengestimasi reliabilitas. Fakultas Psikologi UGM. Yogyakarta. 30 p.

Published
2020-08-31
How to Cite
FarihahR. A., ManikH. M., & HarsonoG. (2020). MEASUREMENT AND ANALYSIS OF ACOUSTIC BACKSCATTER USING MULTIBEAM ECHOSOUNDER TECHNOLOGY FOR SEDIMENT CLASSIFICATION OF THE GULF OF PALU. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 12(2), 437-453. https://doi.org/10.29244/jitkt.v12i2.28465