High-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km² of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor ( < 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response.

需要对深海多金属结核进行高分辨率测绘，以便（a）了解其不均匀分布背后的原因，（b）将结核覆盖范围与海底动物群的出现联系起来，以及（c）实现准确的资源估算和采矿路径规划。这项研究使用自主水下航行器绘制了东克拉里昂-克利珀顿断裂带 37 公里²的地貌复杂地点的地图。使用 400 kHz 的多波束回声测深系统 (MBES) 和 230 kHz 的侧扫声纳来研究结节反向散射响应。我们分析了 30,000 多张海底图像，以获得结核覆盖范围并训练五种机器学习 (ML) 算法：广义线性模型、广义加性模型、支持向量机、随机森林 (RF) 和神经网络 (NN)。所有模型 ML 都生成相似的结节覆盖图，但预测值范围存在差异，特别是在地形不规则的部分。 RF 具有最佳拟合性，而 NN 具有最差的空间可迁移性。使用所有模型的变量重要性排名、部分依赖图和领域知识来关注模型输出的可解释性。在相对平坦的海底（< 3°）且斜坡朝东的情况下，结核覆盖率较高。最重要的预测因子是 MBES 反向散射，特别是从 25 到 55° 之间的入射角。水深测量、坡度和坡度方向是重要的地貌预测因子。在水深 4500 m 处，正射影像马赛克和具有 2 毫米和 5 毫米空间分辨率的图像衍生数字高程模型首次支持地貌分析、多金属结核产状解释和反向散射响应。