ACOUSTIC CHARACTERISTICS OF SIDESCAN SONAR ALONG PROPOSED POWER CABLE ROUTE, DUMAI – RUPAT ISLAND

Subarsyah Subarsyah, Sahudin Sahudin
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Abstract

Cable power installation along the route with bedforms-sediment structures sometimes potentially to have problems in the future or near future. In order to mitigate the cable from exposure because of currents, it is important to know a detailed understanding of the seabed and its mobility. Seabed characteristics, either textures or sediment structures, could be interpreted from acoustic characters, one of which is based on sidescan sonar images. An automatic interpretation to classify seabed characteristics can be done by using an image processing software. Image processing has been done on sidescan sonar images along power cable route between Dumai and Rupat Island. The image processing was using simple textures and Grey-Level Co-occurrence Matrix (GCLM) textures. Manual interpretation of sidescan sonar images classifies the acoustic characters into six; (1) fine sand waves with ripple marks, wave length 2.5-4 meters, (2) fine sands, (3) fine sand waves with ripple marks, wave length 5-9 meters, (4) fine sand with ripple-mega ripples, (5) coarse sands with ripple-trawl marks, and (6) very fine sands. The results of automatic classification show that image processing with simple textures is unable to identify the textures and structures of sediments properly, but by combining simple texture classification and GCLM types of sediment textures and sediment structures are better identified. This classification results are in agreement with the results of manual interpretation of sidescan sonar images.
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杜马-鲁帕特岛拟建电力电缆线路侧扫声呐的声学特性
沿有河床的线路安装电缆,有时可能在未来或不久的将来出现问题。为了减少电缆因洋流而暴露在外,详细了解海床及其流动性很重要。海底特征,无论是纹理还是沉积物结构,都可以从声学特征中进行解释,其中之一是基于侧扫声纳图像。可以通过使用图像处理软件来进行对海底特征进行分类的自动解释。已经对杜迈和鲁帕特岛之间电力电缆沿线的侧扫声纳图像进行了图像处理。图像处理使用简单纹理和灰度共生矩阵(GCLM)纹理。侧扫声纳图像的人工判读将声学特征分为六类;(1) 具有波纹标记的细沙波,波长2.5-4米,(2)细沙,(3)具有波纹标记、波长5-9米的细沙浪,(4)具有波纹特大波纹的细沙,以及(5)具有波纹拖网标记的粗砂,以及(6)极细沙。自动分类的结果表明,简单纹理的图像处理无法正确识别沉积物的纹理和结构,但通过将简单纹理分类与GCLM相结合,可以更好地识别沉积物的质地和结构类型。该分类结果与侧扫声纳图像的人工解译结果一致。
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发文量
6
审稿时长
16 weeks
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