A significant number of sensors are being used during emergency situations based on their function, flight time, and usefulness. The benefits and limitations of those sensors are reviewed and analyzed in the following sections and .
Volatile organic compound (VOC) detection technology refers to identifying characteristic compounds in the exhaled air, blood, and urine of trapped individuals by determining the type and content of VOCs in the environment [ 22 ]. Breathing is considered a unique feature that can determine if a trapped individual is still alive [ 23 ] by detecting CO 2 and O 2 levels. Ion Mobility Spectrometry (IMS) and electronic sensors are common VOC life-detection instruments [ 24 ], where IMS separates these volatile organic compounds according to the difference in the drift velocity of the product ions in the inert buffer gas under the influence of an electric field [ 25 ]. The electronic sensor (also known as the electronic nose) uses an array of gas sensors to simulate animal olfactory organs to recognize odors [ 26 ]. VOC life-detection technology has some limitations, such as interference from dust and other particles at the rescue site, different VOCs of different groups of people (especially the VOCs of people trapped for a long time, with a lack of water and food), and the insufficient miniaturization of equipment [ 27 ].
A CW radar transmits a monophonic continuous wave signal to demodulate the phase change of the reflected wave and obtain the breathing and heart rate of the person [ 19 ]. This is because the phase change of the reflected waves is linearly proportional to the displacement of the chest caused by cardiopulmonary activity [ 20 ]. The UWB radar life-detection system emits pulsed microwave beams on the biological body. The beam reflects the echo pulse according to the circular sequence modulated by biological activity that extracts the parameters of the life signal through the digital signal processing system [ 21 ]. However, due to the radiation effect of electromagnetic waves on the human body and interference caused by the simultaneous use of multiple radar life detectors at the earthquake site, radar life-detection systems still have some limitations in their use.
Radar life-detection technology is one of the most mature and widely studied life-detection technologies at present. It was used extensively in the 2008 Wenchuan earthquake in Sichuan, China, and in the 2023 Türkiye–Syria earthquake. The common radar life-detection system is divided into Continuous Wave (CW) and Ultra-Wide Band (UWB) radar life-detection systems.
Optical detection technology includes visible light detection and infrared detection technology. The optical detection technology involves using a small camera equipped with a light source connected by a flexible data transmission line to penetrate the aperture of a collapsed building and avoid moving it. One form of life-detection technology, also known as a Snake Eye (SE) life detector [ 15 ], can determine the position and living condition of trapped individuals while avoiding secondary collapse. Infrared detection technology uses the infrared characteristics of the human body to distinguish a human body from the surrounding environment. Currently, Unmanned Aerial Vehicles (UAVs) are becoming popular for collecting video information, audio information, infrared information, and other information at the scene of disaster areas synchronously. The collected data are further classified by operating software to analyze the images and audio in the video and determine the location and living state of personnel [ 16 , 17 , 18 ].
Acoustic life-detection technology is used to locate trapped individuals by detecting cries for help, movements, tapping, and even small chest fluctuations during breathing [ 12 ]. Passive sensors that receive trapped people’s cries for help and knocking sounds have the advantage that rescue workers can hear these sounds and locate them if they are within the detectable range. However, the practical application of this technology requires sufficient experience from operators due to noisy sound at the earthquake site. Recent advancements in sensor technology have enabled the detection of the chest fluctuations of a trapped person during breathing by transmitting sound waves and analyzing reflected waves [ 13 ]. This approach has become more effective in recent years because acoustic signals can penetrate metal walls and detect stationary people through breathing movements alone without being disturbed by the remains of the victim [ 13 , 14 ].
Life-detection sensors are used to collect physiological, physical, and chemical information of trapped survivors to effectively identify their location immediately after a disaster [ 8 ]. Based on their principles and types of sensors used, life-detection technologies can be classified into acoustic life-detection techniques, optical detection techniques, radar life-detection techniques [ 9 ], and volatile organic compound (VOC) detection techniques [ 10 , 11 ].
Seismic monitoring sensors are essential for measuring abnormal activity and precursor signals of earthquakes [28]. They provide invaluable data on the position, depth, magnitude, onset time of shocks, and source mechanism of earthquakes, both before and after they occur.
Sensors play a crucial role in seismic monitoring and are used in various applications such as mobile gravity monitoring [29], electromagnetic wave signal detection [30], and cross-fault deformation measurement [31]. The first seismic network was established in California, USA, in 1929 using Wood–Anderson seismometers [32].
Modern seismic networks typically consist of broadband and strong-motion seismometers. Broadband seismometers have a wide recording capacity ranging from hundreds of seconds to hundreds of hertz. The Southern California Seismic Network (SCSN) is an exemplary seismic network that has grown from 7 seismometers in 1929 to over 600 seismometers in 2021 [33] Each station is now equipped with co-located, three-component broadband and strong-motion seismometers.
Mobile gravity monitoring is an effective technique for earthquake prediction and exploration, primarily for two reasons. First, changes in gravity directly reflect crustal deformation and variations in the focus medium during earthquake incubation [34]. Second, seismic activity is intricately linked to the spatial inhomogeneity and temporal discontinuity of gravity change.
Earthquake incubation and occurrence involve multiple stages, starting from stress accumulation to energy release. During the earthquake breeding process, stress builds up in the source, leading to the migration of material in the crust and changes in crustal density, which then affect the corresponding surface gravity.
One notable success story comes from China, where a forecast system was developed based on the principle of “A field, a network”. This system uses mobile gravity monitoring to predict earthquakes and has been successful in detecting abnormalities in gravity prior to several significant earthquakes [35]. Gravity monitoring and prediction are foundational for earthquake prevention and disaster reduction efforts. This involves the use of gravity sensors mounted on both ground-based instruments and satellites. By continuously monitoring changes in gravity, researchers can detect patterns and anomalies that may indicate the potential for an earthquake. This information can then be used to inform early warning systems and evacuation plans, potentially saving lives and minimizing damage.
Several countries have developed earthquake early warning systems using various techniques, including Japan, Mexico, China, and the USA [36]. Among them, the most advanced system is the Japanese REIS earthquake early warning system. REIS can accurately calculate the location and magnitude of an earthquake just 5 s after receiving the seismic wave signal. Additionally, it can estimate the source mechanism of an earthquake rupture within approximately 2 min [37]. It is important to note that Japan’s ability to develop such an advanced earthquake early warning system is due in large part to its dense seismic station network. In Japan, there is approximately one seismic station every 20 km, which provides the necessary data to accurately calculate an earthquake’s location and magnitude within seconds of receiving the seismic wave signal.
The Shake Alert earthquake early warning system in the USA is composed of six components, including the station observation system, data transmission system, data processing and alarm center, test and certification platform, information release system, and end-users. When an earthquake occurs, the system’s automatic rapid reporting system takes between 3 to 5 min to relay the relevant earthquake information to the appropriate authorities and end-users. This includes location, magnitude, and estimated shaking intensity based on the seismic waves detected by the network of monitoring stations [38].
The earthquake early warning system in Mexico City (SAS) is composed of four main components. (1) There is an earthquake detection system that employs 12 digital seismometers spaced 25 km apart within a 300 km coastal area of Guerrero. Each station is equipped with a microcomputer capable of determining the magnitude of an earthquake within 10 s. (2) There is a communication system with a very-high-frequency (VHF) central radio relay station and three ultra-high-frequency (UHF) radio relay stations that transmit seismic information to Mexico City within just 2 s. (3) The central control system, located in Mexico City approximately 320 km from the Guerrero Coast area, continuously receives seismic signals and automatically processes them to determine the magnitude and decide whether to issue an alarm. (4) The alarm issuance system issues warnings via commercial radio, and relevant departments are equipped with special receivers where trained personnel are responsible for receiving and coordinating disaster prevention activities [39].
A change in magnetic field can be taken as a precursor of an earthquake because the huge accumulations of crustal pressure may change the properties of the rock layer. This phenomenon affects its electrical conductivity, and the trapped gas accumulated in the formation will also produce an electric current to affect the geomagnetic activity [40]. Therefore, it is sometimes controversial to regard electromagnetic motion as an earthquake precursor. It is not clear yet, but the reasons might be as follows: (1) the signal is too weak and easily mixed with background noise to distinguish it, such as noise from nearby vehicles or small changes in solar activity that can be mistaken for geological disturbance signals; (2) accurate measurement equipment at a fixed position with enough statistical recordings are required to resolve reliable signals [41]. A number of researchers have used artificial noise signals for seismic wave velocity monitoring [42]. Artificial seismic noise is usually dominated by high-frequency body waves, providing a high spatial resolution. In addition, the location of artificial noise sources is often fixed (e.g., industrial operations) or moves along a fixed trajectory (e.g., trains and cars), which is easy to track and simulate the movement of noise sources [43].
Micro-electromechanical systems (MEMS) are devices or systems that combine microstructures, micro transducers, and micro-actuators with signal processing and control circuits [44]. Nowadays, these are commonly found in smartphones and laptops. These sensors are inexpensive and can be used to construct ultra-dense arrays. Additionally, MEMS sensors are known for their high accuracy, low power consumption, and robustness, which makes them ideal for use in harsh environments [45,46].
Distributed Acoustic Sensing (DAS) is another effective technique to measure strain rate that consists of two parts, namely, a demodulator and sensing fiber optic cable ( ). This fiber optic is deformed by the movement of the Earth’s crust, which causes the refractive index of the cable to change the phase of the back-scattered light [47]. The demodulator can detect seismic activity by analyzing the coherent Rayleigh scattered light phase information of the fiber [48]. Since 2017, DAS has emerged as a novel technology to obtain numerous seismic sensors at a relatively low cost. The concept of DAS was proposed in the 1990s, followed by being applied in various fields. However, its applicability in earthquake seismology has only recently been considered.
Post-earthquake monitoring is being carried out using audio signals to locate human targets in a hidden way [41,49], and it can be strengthened by using Wi-Fi and Long-Term Evolution (LTE) in future [50]. This sensor is small and monitors the environment in a narrow space by sensing different physical characteristics such as temperature, humidity, pressure, and vibration. The collected sensor data are first sent to the monitoring node based on ZigBee technology and then transmitted to the monitoring center together with the monitoring images. The results of physical experiments show that using these wireless sensors, the monitoring center can display the monitoring image of the monitoring area in real time and visualize the collected sensor data [29]. The ongoing research has been using intelligent monitoring algorithms (such as object recognition or intrusion detection) on monitoring nodes to achieve better monitoring performance [51]. Other advancements include the optimization of the mechanical design of the monitoring nodes (e.g., miniaturization or lightweight) and the positioning algorithms for the sensor nodes.
The co-seismic dislocation and optical data are the main parts of seismic monitoring via remote satellites [52], where GNSS and InSAR measure the co-seismic dislocation. Ground-based receivers using satellite signals from global navigation satellite systems (GNSS) such as the Global Positioning System (GPS) have served as primary sensors for over a decade to measure co-seismic ground deformation [53,54,55]. The combination of ground-based GPS and remote satellite information is very useful to improve earthquake deformation [56].
Synthetic Aperture Radar (SAR) is an imaging radar that uses a small antenna that moves at a constant speed along a trajectory of a long array and radiates coherent signals to process the echoes received at different locations coherently for a higher resolution [57]. Similarly, InSAR (Interferometric Synthetic Aperture Radar) is an advanced technique that combines synthetic aperture radar imaging technology with interferometry to measure the phase difference of two or more SAR images [58]. InSAR accurately measures the three-dimensional position and small changes in any points on the Earth’s surface and has been demonstrated to be a reliable tool for measurements [59].
The use of optical satellite data to detect various anomalies before a strong earthquake is the key to predict seismic activity because it can identify phenomena related to thermal radiation in the initial stage of an earthquake. Therefore, satellite observations are powerful tools for monitoring earthquake preparedness areas in near real time on a large scale [60].
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