Guyots are a special type of seamount with a flat top and are widely distributed in the global ocean. In this paper, a geomorphologic classification method for guyots based on multibeam bathymetry data is proposed. By studying typical guyots, namely, the Jiaxie Guyots, the Caiwei Guyots, and the DD Guyot in the Western Pacific Ocean, in this study, a multilevel classification system was established, integrating elevation, slope, and bathymetric position index (BPI). The method successfully classified seafloor geomorphology into nine types: summit platform, extremely steep slope, steep slope, gentle slope, very gentle slope, gully on the slope, seafloor plain, local crest, and local depression. Significant differences in the area distribution, depth characteristics, and slope extent of different geomorphologic units in the guyots were revealed by quantitative analysis. The flexibility and accuracy of the method were demonstrated through depth profile validation and method comparison validation. This classification system provides a new cognitive framework for defining the boundaries of seamounts, as well as for the study of the genesis mechanisms of the gullies on the slopes, local crests, and local depressions formed by volcanic activity and other actions.

To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from −173.76 dB to −171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment.

The deep sea is gradually being exploited, yet research on the stability of the deep seabed is scarce. In this study, the seafloor stability of the Jiaxie Seamount Group in the western Pacific Ocean was assessed using the weight-of-evidence (WoE) method based on seafloor topographic data. Slope failure features were identified by analyzing multibeam bathymetric data, revealing 21 failure zones and multiple debris accumulation areas. Topographic factors, such as water depth, slope, slope direction, planar curvature, profile curvature, and ruggedness, were selected as assessment indicators. These indicators were reclassified as evidence factors, and a WoE model was constructed to assess the failure probability in the study area. A stability zoning map indicated that over 93% of the area had high stability. In comparison, areas with low and very low stability comprised less than 4%, mainly located on steep ridges and rugged slopes. The model’s performance was validated through an ROC curve, yielding an AUC value of 0.929, indicating a high predictive capability. This study presents a statistical framework for assessing the stability of deep-sea floors and provides theoretical support for upcoming seabed mining and deep-sea engineering endeavors, despite limitations due to data constraints and dependence on visually interpreted slope failure zones.

The DD Guyot, a flat-topped seamount located in the Western Pacific, was completely mapped using multibeam echosounders (MBESs) in 2024. Clarifying substratum patterns is crucial for understanding seafloor evolution, sediment transport processes, and resource assessment. This study integrates near-bottom video data from the manned submersible Jiaolong, multibeam bathymetry and backscatter data from EM124, and a convolutional neural network (CNN) model to classify the four substratum types (exposed bedrock, thinly sedimented bedrock, sediment–rock transition zone, and continuous sediment) of the DD Guyot. The results indicate that exposed bedrock predominates on the summit platform, while sediment cover increases with water depth along the flank. The base of the guyot is almost entirely covered by sediments. Two landslide areas were identified, with clear main scarps, sidewalls, and debris accumulations. These features, together with underflow erosion, collectively influence sediment distribution patterns. The resulting substratum maps provide guidance for seabed resource exploration. The results are consistent with a post-drowning onlap framework, which points to a drowning unconformity, but video and surface acoustic data alone are insufficient for definitive confirmation. Further investigation is required to more clearly elucidate the substratum characteristics of the DD Guyot.