Moving target recognition with seismic sensing: A review

Bin, Kangcheng; Lin, Jun; Tong, Xunqian; Zhang, Xiaopu; Wang, Junqiu; Luo, Shihao

doi:10.1016/j.measurement.2021.109584

Cited by 16 publications

(5 citation statements)

References 89 publications

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“…The results suggest that embedding analysis of traffic-induced seismic noise into decision-making protocols may be useful for detecting large-scale mobilization in real time. Most seismic-based traffic detection systems rely on near-target recordings (Bin et al, 2021), however, analysis of the October 7 dataset suggests that, in certain conditions, remote stations could also be used for these purposes. Deploying stations further from the target area also means the network is less likely to be decapitated during the attack, as was the fate of many of the IDF near-fence systems on October 7.…”

Section: Discussionmentioning

confidence: 99%

Forensic seismic evidence for precursory mobilization in Gaza leading to the October 7 terrorist attack

Inbal

2024

Preprint

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Seismic waves excited by human activity frequently mask signals due to tectonic processes, and are therefore discarded as nuisance. Seismic noise-field analysis is, however, a powerful tool for characterizing anthropogenic activities. Here, I apply this analysis to examine seismic precursors to the October 7 Hamas attack on Israel. The precursory activity in Gaza included massive mobilization which took place in the hours leading to the attack, and was documented on multiple media outlets. Favorable conditions, which arise due to a temporary lack of anthropogenic activity in Israel, allow remote seismic stations to record signals due to Gaza vehicle traffic. I use these seismograms to identify anomalous ground-motions, associate them with preattack mobilization, and precisely determine their location. By applying array analysis to three seismic stations located tensof-kilometers from the Gaza Strip, I was able to obtain valuable information on the Hamas attack plans. This suggests that embedding seismic noise-field analysis into decision-making protocols could enhance preparedness, thus providing an opportunity to blunt terrorist attacks and reduce the number of casualties.

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Section: Discussionmentioning

confidence: 99%

Forensic seismic evidence for precursory mobilization in Gaza leading to the October 7 terrorist attack

Inbal

2024

Preprint

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“…Bin et al [ 52 ], 2021, made a review of IoGN sensing devices, algorithms, architecture, and applications for seismic data acquisition units and data servers. The main techniques that could be applied to IoGN are denoising methods, including compressed sensing (CS) and autoencoders (AE) used to reduce seismic noise.…”

Section: Methodology Validationmentioning

confidence: 99%

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Arrais

Urquiza-Aguiar

Tripp-Barba

2022

Sensors

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Organizations responsible for seismic and volcanic monitoring worldwide mainly gather information from instrumental networks composed of specialized sensors, data-loggers, and transmission equipment. This information must be available in seismological data centers to improve early warning diffusion. Furthermore, this information is necessary for research purposes to improve the understanding of the phenomena. However, the acquisition data systems could have some information gaps due to unstable connections with instrumental networks and repeater nodes or exceeded waiting times in data acquisition processes. In this work, we performed a systematic review around information availability issues and solutions in data acquisition systems, instrumental networks, and their interplay with transmission media for seismic and volcanic monitoring. Based on the SLR methodology proposed by Kitchenham, B., a search string strategy was considered where 1938 articles were found until December 2021. Subsequently, through selection processes, 282 articles were obtained and 51 relevant articles were extracted using filters based on the content of articles mainly referring to seismic–volcanic data acquisition, data formats, monitoring networks, and early warnings. As a result, we identified two independent partial solutions that could complement each other. One focused on extracting information in the acquisition systems corresponding to continuous data generated by the monitoring points through the development of mechanisms for identifying sequential files. The other solution focused on the detection and assessment of the alternative transmission media capabilities available in the seismic–volcanic monitoring network. Moreover, we point out the advantage of a unified solution by identifying data files/plots corresponding to information gaps. These could be recovered through alternate/backup transmission channels to the monitoring points to improve the availability of the information that contributes to real-time access to information from seismic–volcanic monitoring networks, which speeds up data recovery processes.

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“…All five features are discussed within one signal sampling period defined in the software flow below. These features have the advantages of lowcomputational complexity and easy hardware implementation in microsystems [26].…”

Section: Feature Extractionmentioning

confidence: 99%

Self-Powered Long-Life Microsystem for Vibration Sensing and Target Recognition

Yang

Duan

Xuan

et al. 2022

Sensors

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Microsystems play an important role in the Internet of Things (IoT). In many unattended IoT applications, microsystems with small size, lightweight, and long life are urgently needed to achieve covert, large-scale, and long-term distribution for target detection and recognition. This paper presents for the first time a low-power, long-life microsystem that integrates self-power supply, event wake-up, continuous vibration sensing, and target recognition. The microsystem is mainly used for unattended long-term target perception and recognition. A composite energy source of solar energy and battery is designed to achieve self-powering. The microsystem’s sensing module, circuit module, signal processing module, and transceiver module are optimized to further realize the small size and low-power consumption. A low-computational recognition algorithm based on support vector machine learning is designed and ported into the microsystem. Taking the pedestrian, wheeled vehicle, and tracked vehicle as targets, the proposed microsystem of 15 cm3 and 35 g successfully realizes target recognitions both indoors and outdoors with an accuracy rate of over 84% and 65%, respectively. Self-powering of the microsystem is up to 22.7 mW under the midday sunlight, and 11 min self-powering can maintain 24 h operation of the microsystem in sleep mode.

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Moving target recognition with seismic sensing: A review

Cited by 16 publications

References 89 publications

Forensic seismic evidence for precursory mobilization in Gaza leading to the October 7 terrorist attack

Forensic seismic evidence for precursory mobilization in Gaza leading to the October 7 terrorist attack

Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Self-Powered Long-Life Microsystem for Vibration Sensing and Target Recognition

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