Highly accuracy classification and mapping of seafloor hydrothermal fields provides an important addition to research into the geological background and exploration of seafloor massive sulfides (SMS). Currently, acoustic remote sensing using multibeam sounding systems (MBES) is the primary means of achieving large-scale seafloor classification. However, the characteristics of complex topography, complex seafloor distribution, and deep water depth of the hydrothermal fields make it difficult to obtain accurate high-resolution seafloor classification maps using shipboard MBES surveys. Here, a seafloor classification strategy combining shipboard MBES and autonomous underwater vehicle (AUV) sidescan sonar data is proposed. First, a downscaling model is established, which downscales the MBES backscatter mosaic (12-kHz and 10-m resolution) to a resolution of 2 m. The second step performs feature extraction of the downscaled MBES backscatter mosaic (12-kHz and 2-m resolution), the AUV sidescan sonar backscatter mosaic (150-kHz and 2-m resolution), and a seafloor digital elevation model (2-m resolution). Finally, a deep neural network model was built for training and classification. To evaluate the classification performance of the model, the method was applied to the survey of the Duanqiao hydrothermal field. Results were verified using field data (deep-tow video). The overall root mean square error and coefficient of determination ( $R^{2}$ ) for the classification were 0.032 and 0.840, respectively. The experimental results show that the method can effectively meet the challenges to seafloor classification presented by complex seafloor distribution, and can obtain an accurate high-resolution seafloor classification map.

中文翻译：

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结合船载低频和AUV高频声学数据进行海底分类——以西南印度洋中脊断桥热液田为例


海底热液场的高精度分类和绘图为海底块状硫化物（SMS）的地质背景和勘探研究提供了重要补充。目前，利用多波束探测系统（MBES）进行声学遥感是实现大规模海底分类的主要手段。然而，由于热液田地形复杂、海底分布复杂、水深较深等特点，船载MBES测量很难获得准确的高分辨率海底分类图。这里，提出了一种结合船载MBES和自主水下航行器（AUV）侧扫声纳数据的海底分类策略。首先，建立缩小模型，将 MBES 反向散射马赛克（12 kHz 和 10 m 分辨率）缩小到 2 m 的分辨率。第二步对缩小的 MBES 反向散射镶嵌（12 kHz 和 2 米分辨率）、AUV 侧扫声纳反向散射镶嵌（150 kHz 和 2 米分辨率）以及海底数字高程模型（2 米）进行特征提取。解决）。最后，建立了深度神经网络模型用于训练和分类。为了评价模型的分类性能，将该方法应用于断桥热液田的调查。使用现场数据（深拖视频）验证结果。总体均方根误差和决定系数（ $R^{2}$ ）分类的分别为 0.032 和 0.840。 实验结果表明，该方法能够有效应对复杂海底分布对海底分类带来的挑战，能够获得准确的高分辨率海底分类图。