We propose a magnetometer system fitted on an unmanned aerial vehicle (UAV, or drone) and a data-processing method for detecting metal antipersonnel landmines (M16) in the demilitarized zone (DMZ) in Korea, which is an undeveloped natural environment. The performance of the laser altimeter was improved so that the drone could fly at a low and stable altitude, even in a natural environment with dust and bushes, and a magnetometer was installed on a pendulum to minimize the effects of magnetic noise and vibration from the drone. At a flight altitude of 1 m, the criterion for M16 is 5 nT. Simple low-pass filtering eliminates magnetic swing noise due to pendulum motion, and the moving average method eliminates changes related to the heading of the magnetometer. Magnetic exploration was conducted in an actual mine-removal area near the DMZ in Korea, with nine magnetic anomalies of more than 5 nT detected and a variety of metallic substances found within a 1-m radius of each detection site. The proposed UAV-based landmine detection system is expected to reduce risk to detection personnel and shorten the landmine-detection period by providing accurate scientific information about the detection area prior to military landmine-detection efforts.

The array invariant technique has been recently proposed for passive source localization in the ocean. It has successfully estimated the source–receiver horizontal range in multipath-dominant shallow-water waveguides. However, it requires a relatively large-scale hydrophone array. This study proposes an array invariant method that uses acoustic intensity, which is a vector quantity that has the same direction as the sound wave propagating through a water medium. This method can be used to estimate not only the source–receiver horizontal range, but also the azimuth to an acoustic source. The feasibility of using a vector quantity for the array invariant method is examined through a simulation and an acoustic experiment in which particle velocity signals are obtained using a finite difference approximation of the pressure signals at two adjacent points. The source localization results estimated using acoustic intensity are compared with those obtained from beamforming of the acoustic signals acquired by the vertical line array.

An underwater glider equipped with a hydrophone observed the acoustic sounds of an earthquake that occurred on 15 November 2017 05:29:32 (UTC) in the Pohang area. The underwater glider observed the earthquake sounds after 19 s (05:29:51) at approximately 140 km from the Pohang epicenter. In order to distinguish the earthquake sound from the glider’s operation noise, the noise sources and Sound Pressure Level (SPL) of the underwater glider were analyzed and measured at laboratory tank and sea. The earthquake acoustic signal was distinguished from glider’s self-noises of fin, pumped Conductivity-Temperature-Depth profiler (CTD) and altimeter which exist over 100 Hz. The dominant frequencies of the earthquake acoustic signals due to the earthquake were 10 Hz. Frequencies at which the spectra had dropped 60 dB were 50 Hz. By analysis of time correlation with seismic waves detected by five seismic land stations and the earthquake acoustic signal, it is clearly shown that the seismic waves converted to Tertiary waves and then detected by the underwater glider. The results allow constraining the acoustic sound level of the earthquake and suggest that the glider provides an effective platform for enhancing the earth seismic observation systems and monitoring natural and anthropogenic ocean sounds.

Underwater thruster failures pose a critical risk to the mission reliability of unmanned surface vehicles (USVs), especially during long-term autonomous operations. Conventional fault diagnosis approaches often rely on vibration sensors or controlled laboratory settings, which limits their practicality in dynamic marine environments. To overcome these challenges, a noncontact acoustic fault diagnosis framework that leverages microphone-based sensing combined with deep-learning techniques is presented in this paper. Acoustic signals acquired from underwater thrusters are converted into time frequency spectrograms using short time Fourier transform (STFT), and background noise is suppressed through a spectral-aware denoising convolutional neural network (SA-DnCNN). The denoised spectrograms are then used to train a vision Transformer (ViT-B/16) for the classification of four representative thruster conditions: normal, breakage, entanglement, and biofouling. The proposed SA-DnCNN + ViT model was validated using acoustic data obtained both in a controlled water tank environment and during real-world sea trials. Classification accuracies of 99.27% and 96.69% were achieved. Comparative experiments with CNN, ResNet50, and other denoising-based models confirmed the superiority of the proposed method in terms of robustness and precision. Notably, this study is one of the first experimental demonstrations of microphone-based diagnosis of thruster faults under actual marine conditions and highlights the potential of the method as a scalable and practical solution for real-time health monitoring of USVs. Future work will focus on expanding the acoustic dataset through long-term marine deployments and incorporating multimodal sensor fusion to improve diagnostic generalization and resilience across diverse maritime environments and operational scenarios.

A detailed analysis of high-resolution (3.5 kHz) chirp seismic profiles acquired in the Gunsan Basin of the central Yellow Sea revealed that hydrocarbon gases are actively seeping via the formation of many plumes. The uppermost sedimentary layer was acoustically confirmed to be fully or partially charged with gases. Somewhat favored by the low-tide period, episodic gas seepage is mainly associated with the underlying fault systems of Cretaceous-Cenozoic sedimentary strata in the southwestern part of the basin. Catastrophic gas expulsion seems to have formed a crater at the sidewall of a sedimentary ridge and two diapirs. Here, methane is poorly concentrated but rich in the heavy carbon isotope (δ¹³C, −52.6‰ to −44.7‰ The Vienna Peedee Belemnite [VPDB]), indicating that methane formed mainly through biodegradation of heavy oils at depth remains in the shallow sediments following its expulsion. Episodic rapid upward advection of porewater is also manifest by unmixed heavy methane trapped in the upper part of the primary biogenic methane (δ¹³C, about −90‰ VPDB)-filled sediment core. These findings imply that the Gunsan Basin fulfills the requirements for possible generation and preservation of oil and gas, like the petroliferous basins of eastern China and the Yellow Sea.

Underwater gliders are suited for long-duration oceanographic observation, but their endurance is bounded by onboard energy capacity. An overlooked source of energy loss is the attitude control system, which repeatedly repositions the internal moving mass to hold the desired pitch angle throughout each gliding cycle. Conventional PID and manually tuned fuzzy controllers continue driving the actuator after pitch convergence and adapt poorly to nonlinear buoyancy variations at depth. To address this, we propose an ANFIS (Adaptive Neuro-Fuzzy Inference System)-based pitch control strategy for a 1000 m-class underwater glider. A nonlinear 6-DOF dynamic simulator incorporating experimentally derived power models for the buoyancy engine and attitude controller was validated up to 100 bar. A 13-rule Sugeno-type fuzzy inference system was optimized through ANFIS hybrid learning using approximately 5500 samples from PID steady-state data. Simulation results show energy savings of 57.05% over PID and 4.98% over a manually tuned fuzzy controller, with no degradation in tracking accuracy. Sea trials confirm a reduction in moving mass displacement under real disturbance conditions, providing qualitative evidence consistent with the simulation results. Further quantitative validation of the energy reduction effect through free-running sea trials remains as future work.