Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets....
The detection of acoustic emission events from various failing mechanisms, such as plastic deformations, is a critical element in the monitoring and timely detection of structural failures in infrastructures. This study focuses on the detection of such failures in metal gates at rivers’ lifting dams aiming to increase the reliability of river transport compared to the current situation, thereby, increasing the resilience of transport corridors. During our study, we used lifting dams in both France and Italy where river transport is thriving. A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%....
Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms....
The paper considers the results of experiments on localization of the sources of geoacoustic radiation generated in near-surface sedimentary rocks. Geoacoustic signals from sources were recorded by a spaced sensor system consisting of two combined receivers and a hydrophone. The system was installed near the bottom of a natural water body (Mikizha lake) in Kamchatka. Radiation sources were located by two methods, a triangulation survey and estimation of the signal arrival time difference from spaced receivers. Coordinates for more than 40 sources were measured, and their space distribution was mapped. As the result of the experiment, it was stated that geoacoustic radiation sources are located in bottom rocks at the depths up to 2.20 ± 0.25 m at the distances of up to 8 ± 0.25 m. Localization of geoacoustic radiation sources is topical for projecting a new alarm system for the notification on the possibility of strong earthquake occurrence. The results of the analysis of the frequency of rock AE source generation and accurate estimation of their location will be the basis of this system....
Background: Building on our previous work, this study presents a cost-effective, non-invasive methodology for recording, identifying, and analyzing plant ultrasonic emissions in dynamic environments, both indoor and outdoor. While previous research has utilized contactless microphones to compare water-stressed and hydrated plants indoors, to the best of our knowledge, no similar studies have been conducted in outdoor conditions. The objective of this study is to address the need for scalable, accessible tools for monitoring plant stress in a variety of environmental settings. Methods: Pinto bean and tomato plants were exposed to water stress conditions and monitored in both indoor and outdoor environments, with plant acoustic emissions recorded using an ultrasonic microphone. Results: The proposed methodology successfully recognized plant ultrasonic emissions even in the presence of high levels of environmental noise typical of outdoor conditions (e.g., wind, rain, or insect chirping). Conclusions: We argue that this method, with its minimal equipment requirements, is a valuable addition to the range of tools available for Plant Acoustics research, offering the potential for non-invasive monitoring in a variety of different environments....
Loading....