Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments.

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments.

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity.