Earthquake Monitoring Research Articles

Predicting the Future Performance of the Planned Seismic Network in Chinese Mainland

Abstract The China Earthquake Administration has currently launched an ambitious nationwide seismicity monitoring network project that will increase the number of stations from ∼950 to 2000 for the broadband seismic stations used to compile the earthquake catalog. The new network is planned to go online by the end of 2023. For more than half of Chinese mainland, the interstation distance of the broadband seismic network will soon be smaller than 100 km, for 27% smaller than 50 km, and for 6% smaller than 25 km. Of all possible ways to characterize the higher-resolution monitoring of the frequent smaller earthquakes expected inside Chinese mainland, the completeness magnitude (Mc) remains one of the most commonly used. Using the prior model of the Bayesian magnitude of completeness method calibrated on the Chinese earthquake catalog from 1 January 2009 to 26 June 2022, we predict the spatial distribution of Mc for the new network based on the planned network configuration. If almost the entire Chinese mainland is at present covered down to Mc=3.3, this threshold will fall to Mc=2.9 in the near future. This means approximately two times more earthquakes will be recorded in the complete catalog available for statistical analysis per year (for a = 6.77 and b = 0.80 in the Gutenberg–Richter law log10N=a−b·M, in which N represents the number of events of magnitude larger than or equal to M and M≥Mc). Based on the observation that abnormal seismicity as precursors are most likely to be observed at least at three units below the mainshock magnitude, and assuming earthquakes to be potentially damaging at M ≥ 5, the new seismic network shall achieve the goal of 76% coverage for optimal seismic-based earthquake prediction research.

Monitoring of Local Earthquakes in Haiti Using Low-Cost, Citizen-Hosted Seismometers and Regional Broadband Stations

Abstract Seismic monitoring in Haiti is currently provided by a mixed network of low-cost Raspberry Shake (RS) seismic stations hosted by citizens, and short-period and broadband stations located mainly in neighboring countries. The level of earthquake detection is constantly improving for a better spatio-temporal distribution of seismicity as the number of RS increases. In this article, we analyze the impact of the quality of the signals recorded by the RS—low-cost seismometers with the smallest magnitude that the network can detect by studying the ambient noise level at these stations. Because the RS stations are installed as part of a citizen-science project, their ambient noise estimated by the power spectral density (PSD) method often shows a high-noise level at frequencies above 1 Hz. In the near field (<50 km), we show that the network detects seismic events of local magnitude on the order of 2.2 with signal-to-noise ratios (SNRs) greater than 4. Improving the network detection threshold requires densifying the network with more RS stations in locations that are less noisy, if possible. In spite of these limitations, this mixed network has provided near-field data essential to rapidly understand the mechanism of the mainshock of the 14 August 2021 Mw 7.2 earthquake, to monitor its sequence of aftershocks in near-real time, and to monitor background seismicity in Haiti on a routine basis.

Peak Ground Velocity (PGV) estimation of Mw 5.6 Cianjur earthquake November 11, 2022 using Global Positioning System (GPS)

The primary purpose of GPS is to be used for positioning, navigation, and timing (PNT). In the last decade, research on GPS for earthquake hazards has increased rapidly, such as for tectonic deformation, GPS seismology, modeling earthquake source parameters, estimation of magnitude, etc. In this study, following the Mw 5.6 Cianjur earthquake on November 11, 2022, we estimate the peak ground velocity from the CJUR GPS station, which is located about 15 km from the epicenter. We compute GPS velocity from the high-rate GPS receiver independent exchange (RINEX) data and broadcast ephemeris. Using a narrow line combination of L1 and L2 observations, we invert velocity epoch by epoch. The result shows the CJUR GPS station has a peak ground velocity of about 11.5 cm/s and equal to VI MMI scale. The peak ground velocity from the GPS station could be important for deriving the earthquake shakemap. By increasing the number of GPS stations in Indonesia, it is very important to support earthquake hazard monitoring.

Investigating Earthquake-Induced Changes in the Persian Gulf Marginal Strip, Using Sentinel-1A Images and Radar Interferometry

Geohazards, such as earthquakes, pose significant threats to human life and infrastructure in various regions across the globe. Iran, in particular, is highly vulnerable to earthquakes due to its unique structural and tectonic characteristics. Therefore, it is crucial to monitor geohazards in order to mitigate their impacts. Several techniques, including the global positioning system, geodesy, tacheometry, and mapping cameras, have been developed for this purpose. Among these methods, radar interferometry has emerged as a particularly accurate and cost-effective approach. It is capable of operating under all weather conditions, 24 h a day, and can cover large areas with high spatial and temporal resolution. In this research, we employed Sentinel 1A images and radar interferometry to investigate the changes in the Earth’s surface following earthquakes in the marginal strip of the Persian Gulf. Specifically, our focus was on earthquakes in Bandar Khamir, and we analyzed the Earth’s surface changes three days and fifteen days after the events. The findings of our study revealed that the most significant uplift occurred around Bandar Khamir, with an uplift rate of 14 cm. Conversely, the highest subsidence was observed near Bandar Charak, with a subsidence of 12 cm. Furthermore, we observed a rise of 32 cm around the eastern and northern regions of Bandar Khamir 11 days after the initial period, accompanied by a subsidence of 31 cm around Bandar Lange and Bandar Charak. These results underscore the importance of continuous monitoring of earthquakes and their impact on the Earth’s surface, particularly in coastal areas where the effects on the water table and coastal infrastructure can be severe. In conclusion, this study highlights the significance of employing radar interferometry as a powerful tool for monitoring and assessing the impacts of earthquakes.

Research on Earthquake Data Prediction Method Based on DIN–MLP Algorithm

This paper proposes a recommendation algorithm that combines MLP with the DIN model and conducts simulation experiments in the field of earthquake missing data prediction. The original DIN model may face challenges and weaknesses in earthquake monitoring data prediction, such as a limited capability in handling data loss or anomalies in seismic monitoring stations. To overcome these issues, we innovatively treat seismic monitoring stations as special users and historical data patterns as recommended items. Based on the DIN model, we implement data processing and prediction for seismic monitoring stations and introduce an attention mechanism based on MLP neural networks in the model, while leveraging the prior knowledge base to enhance predictive capabilities. Compared to the original DIN model, our proposed approach not only recommends sequence combinations that meet the demands of seismic monitoring stations but also enhances the matching between station behavior attributes and historical data characteristics, thereby significantly improving prediction accuracy. To validate the effectiveness of our method, we conducted comparative experiments. The results show that the GAUC achieved by the DIN–MLP model reaches 0.69, which is an 11 percent point improvement over the original DIN model. This highlights the remarkable advantages of our algorithm in earthquake missing data prediction. Furthermore, our research reveals the potential of the DIN–MLP algorithm in practical applications, providing more accurate data processing and time series combination recommendations for the field of earthquake monitoring stations, thus contributing to the improvement of monitoring efficiency and accuracy.

An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°, 0.02°, 2 ​km, and 1.25 ​s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.

Multi-station volcano tectonic earthquake monitoring based on transfer learning

Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed.Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems—a recurrent neural network based on long short-term memory cells (RNN–LSTM) and a temporal convolutional network (TCN)—both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption).Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals.

Characteristic analysis of earthquake sound signal based on wavelet transform

Earthquake is a kind of natural disaster with strong destructive power. As a kind of earthquake precursor signal, earthquake sound is of great significance of earthquake prediction. According to the characteristics of the earthquake sound signal, the earthquake sound monitoring data ten days before the earthquake was analysed using wavelet technology, the earthquake sound monitoring signals of different frequency bands was compared, and the change rule of the earthquake sound signal within ten days before the earthquake was emphatically studied. It was concluded that: the infrasound wave signal with normal fluctuation changed regularly into time; Ten days before the earthquake, the peak mutation of infrasonic signal occurred, which increased exponentially compared with the mean peak mutation value of earthquake sound signal. The number of this abnormal phenomenon increased to the shortening of the impending earthquake time, especially five days before the earthquake; The intensity of the earthquake sound signal in the frequency band of the infrasonic wave is gradually decreasing from the increase in the signal frequency. By studying the variation characteristics of earthquake sound signal with time before earthquake, it is of certain reference significance to study the correlation between abnormal ground earthquake signal and earthquake in earthquake prediction.

Spatial Characteristics of Global Strong Constant-Frequency Electromagnetic Disturbances from Electric-Field VLF Data of the CSES

Ionospheric disturbances are mainly caused by solar and Earth surface activity. The electromagnetic data collected by the CSES (China Seismo-Electromagnetic Satellite, popularly known as the Zhangheng-1 satellite) can capture many space disturbances. Different spatial disturbances can exhibit distinctive shapes on spectrograms. Constant-frequency electromagnetic disturbances (CFEDs) such as artificially transmitted VLF radio waves, power line harmonics, and satellite platform disturbances can appear as horizontal lines on spectrograms. Therefore, we used computer vision and machine learning techniques to extract the frequency of global CFEDs and analyze their strong spatial signal characteristics. First, we obtained time-frequency spectrograms from CSES VLF electric-field waveform data using Fourier transform. Next, we employed an unsupervised clustering algorithm to automatically recognize CFED horizontal lines on spectrograms, merging horizontal lines from different spectrograms, to obtain the CFED horizontal-line frequency range. In the third stage, we verified the presence of CFEDs in power spectrograms, thus extracting their true frequency values. Finally, for strong CFED signals, we generated eight revisited periods, resulting in 10,230 power spectrograms for analyzing each CFED’s spatial characteristics using a combined periodic sequence and spatial region that included frequency offsets, frequency fluctuations, and signal non-observation areas. These findings contribute to enhancing the quality of CSES observational data and provides a theoretical basis for constructing global CFED spatial background fields and earthquake monitoring and early prediction systems.

Inelastic Analysis and Damage Assessment of Hydraulic Arched Tunnels in Different Near-Fault Regions

The seismic natural hazard event is a threat of unpredictable events, which can easily cause incalculable damage to hydraulic tunnels. Traditionally, it is necessary to introduce as-recorded ground motions (GMs) appropriately for hydraulic arched tunnels during seismic design to avoid the unfavorable situation of large-scale collapse. However, the lack of adequate current selected methodology considering the accurate engineering site relative to the fault line in this type can result in more considerable deformations and severe damage to structures. Actually, the detailed explanation of the seismic design code of hydraulic tunnels cannot support the specific consideration of the spatial region effects since the considerable uncertainty of engineering sites in water resources and hydropower engineering. To address this issue, the present study proposes a novel selected methodology of as-recorded GMs that integrates the geographic location distribution of the strong earthquake monitoring station instead of the actual engineering site relative to the fault line. Subsequently, three-dimensional finite element models of a hydraulic arched tunnel reveal the possible seismic behavior of the tunnel induced by as-recorded GMs in different near-fault regions. Furthermore, the optimal IM between different near-fault regions and structural responses is also discussed through correlation analysis because it can describe the seismological features of as-recorded GMs, which has a crucial role in seismic design and performance assessment. The results reveal that the as-recorded GMs in the hanging wall region can induce more significant deformations and severe damage to the structure than other near-fault regions of as-recorded GMs. Meanwhile, the seismological characteristics and selected optimal IM for as-recorded GMs in different near-fault regions significantly differ. In summary, data and observations from these results highlight that considering the near-fault region effect is critical in optimizing the seismic design code.

Application of traditional manual measurements and remote sensing technologies in monitoring natural disasters

Natural disasters have occurred frequently worldwide. Some manual measurements and remote sensing technologies are widely believed to be effective and convenient in hazard monitoring. This study illustrates the causes and impacts of three natural disasters including floods, wildfires and earthquakes, and analyzes the application of traditional and remote sensing technologies in monitoring these natural disasters. In flood monitoring, traditional tools include meteorological and hydrological detection facilities and equipment could obtain rainfall and water level. Remote sensing technologies could extract water bodies and make disaster evaluation. In wildfire monitoring, traditional tools include the standard equipment of urban fire departments, portable pumps, tank trucks, and earth-moving equipment. Remote sensing technologies could be applied to fuel mapping and fire estimates. In earthquake monitoring, traditional tools include seismic measurement, crustal deformation observation, geomagnetic measurement, telluric observation, gravity observation, in-situ stress observation, and groundwater dynamic observation. Remote sensing technologies could help monitor through house images and thermal data. Remote sensing technologies bring natural disaster monitoring systems a great revolution, both in data acquisition and processing methods.

Evaluation of the earthquake monitoring network in Taiwan

In this work, we perform an evaluation of the coverage of the earthquake monitoring network of Taiwan. The capability of a general network is a function of an adequate number of optimally distributed nodes. For this case study, the evaluation is performed with a statistical approach which includes descriptive spatial statistics in combination with point pattern techniques. The spatial distribution of the nodes of the earthquake monitoring network is analyzed in comparison with the distribution of seismicity, completeness magnitude, active seismogenic sources, seismic hazard, and population distribution. All these data can be put in relationship with the objectives of an earthquake monitoring network; therefore, they can be used, in turn, to retrieve information about the consistency of the network itself. In particular, we investigate the “Real-time Seismic Monitoring Network” and the “Strong-Motion Earthquake Observation Network,” each one characterized by its own objectives, and therefore respectively compared with external information related to their purposes such as seismicity, seismogenic sources, seismic hazard, and population distribution. This simple and reliable approach reveals the high quality of the networks established in Taiwan. In general, it is able to provide quantitative information on the coverage of any type of network, identifying possible critical areas and addressing their future development.

Seismic monitoring in Greece, 1899–2014: catalogue completeness 1966–2014

SUMMARY Instrumental earthquake monitoring in Greece started in 1899–1906 with the first five seismic stations of the National Observatory of Athens, Institute of Geodynamics (NOA). Subsequent upgrades and expansions led to today’s Unified National Seismic Network, which includes almost all permanent seismic stations in Greece and provides waveform and parametric data. We examine the detection capabilities of the Greek seismic networks using phase, location and magnitude data from the International Seismological Centre (ISC) and the various National Institutes. We apply two methods to measure the network performance. In one, we form a grid, and find the 50 per cent probability detection threshold for each station-grid cell pair for different times. In the other, we find the probability-based magnitude of completeness grid for every three months from 1970 to 2014. Both methods show that in 1990 the detection threshold improved significantly in the north part of Greece. A much greater improvement took place in 2010 throughout Greece, yielding a completeness magnitude of $M_p\, \sim$ 1.

Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.

Recurrence intervals for M > 7 Miyagi-ken-Oki earthquakes during an M ~ 9 earthquake cycle

The 2011 Tohoku-Oki great earthquake increased the difficulty of evaluating the long-term probability of seismic activity along the Japan Trench because of the unknown impact of the unprecedentedly large slip. In this study, the long-term activity of “Miyagi-ken-Oki earthquakes”, an M > 7 earthquake sequence off Miyagi Prefecture, located at the edge of the source area of the Tohoku-Oki earthquake was simulated. We conducted numerical simulations of earthquake generation cycles based on the rate- and state-dependent friction law representing the stress accumulation and release process on the plate interface. We also validated the results based on analyses of observational data, including time intervals and afterslip distributions for the previous Miyagi-ken-Oki earthquakes. The simulation results were then compared with repeating-earthquake-derived interplate slip observations over 30 years. The results showed that the spatial and temporal characteristics of aseismic slips on the plate interface near the M > 7 Miyagi-ken-Oki source were qualitatively reproduced. The time interval between the M ~ 9 earthquake and the first M > 7 earthquake is shorter than the average recurrence interval of M > 7 earthquakes during the latter stage of each M ~ 9 earthquake cycle. In contrast, the interval between the first and the second M > 7 earthquakes is the longest in each M ~ 9 earthquake cycle. The time intervals of the M > 7 earthquakes fluctuated in the early stage compared to those in the latter stage of the M ~ 9 earthquake cycle. These characteristics were associated with the extent of the locked and afterslip areas in and around the source. Hence, monitoring the spatio-temporal distribution of the slip rate in and around the source area during the preparation process of earthquakes occurring in the downdip area off Miyagi Prefecture is very important to assess whether the next M > 7 earthquake is approaching. Furthermore, earthquake cycle simulations combined with earthquake and slow slip monitoring can provide meaningful insights for long-term assessments of great interplate earthquakes.

Review on Low-Noise Broadband Fiber Optic Seismic Sensor and Its Applications

Fiber optic seismic sensors have important application prospects in the field of earthquake science research and underground structure detection, due to their strong adaptability in harsh environments such as deep wells, seabeds, and glaciers. It has always been one of the key problems to achieve ultra-low noise levels over a wide measurement frequency band for fiber optic seismic sensors. This article reviews two typical low-noise broadband fiber optic seismic sensing techniques, including narrow-linewidth laser frequency scanning and frequency locking sensing technique, laser interferometric phase interrogation technique. The principles are given. The measurement frequency band and noise level are tested and discussed. Recent progresses on crustal deformation observation, earthquake monitoring, water temperature solid tide detection and deep earth structure exploration based on fiber optic seismic sensors are introduced. Finally, the possible research trends are discussed.

Development and Verification of Wireless Vibration Sensors

Structural vibration testing is an effective guarantee for the Structural Health Monitoring (SHM) of large-scale civil engineering. Traditional vibration testing has drawbacks such as difficulties in wiring and picking up low-frequency signals, low communication speed, and susceptibility to testing site conditions. In order to improve the universality of wireless vibration sensors, this article develops a wireless vibration sensor, introduces the module composition and basic principles of the sensor, and conducts standard vibration table performance comparison tests between wired acceleration sensors and wireless vibration sensors, verifying the accuracy of wireless vibration sensors. In order to further explore the feasibility of wireless vibration sensor applications, the wired acceleration sensor and wireless vibration sensor were used to analyze the structural dynamic characteristics of the four-layer steel frame structure model in the laboratory, and the comparison was made based on ABAQUS finite element simulation. Finally, the field vibration test was carried out outdoors. The results show that the natural frequency identification results of the wireless vibration sensor and the wired acceleration sensor for the four-story steel frame structure through fast Fourier transform, short-time Fourier transform, and wavelet transform are basically the same, the half-power bandwidth method and logarithmic decrement rate method are used to identify the damping, and wavelet transform is used to identify the vibration mode with minimal error and high accuracy. It shows that the wireless vibration sensor is feasible in practical engineering, has stable and reliable transmission capacity, and can provide certain reference values for earthquake monitoring, building Structural Health Monitoring, etc.

Observation of Shallow Slow Earthquakes by Distributed Acoustic Sensing Using Offshore Fiber‐Optic Cable in the Nankai Trough, Southwest Japan

AbstractOff Cape Muroto area, along the Nankai Trough in southwest Japan, is a typical area with adjacent occurrences of slow and megathrust earthquakes. High‐resolution monitoring of slow earthquakes is necessary to understand tectonic conditions. In the off Muroto area, distributed acoustic sensing (DAS) measurement, which provides high‐density strain data, has been conducted using offshore fiber‐optic cable. We observed shallow tremors, a type of slow earthquakes, using DAS measurement for the first time. The characteristics of the tremor signals recorded by DAS were longer durations than those recorded in seismographs and composed of several phases with apparent velocities of several hundreds of m/s to several km/s that are coherent only in tens of meters. By combining DAS and seismograph data, we located these tremors around a subducted seamount peak. Therefore, spatial relationship between slow earthquakes and structural characteristics is suggested. This is a pioneering study of slow earthquake observation using DAS.

DESCRIPTIVE STATISTICAL CALIBRATION METHOD OF TRIAXIAL DIGITAL ACCELEROMETER ADXL345 AS EARTHQUAKES SENSOR

Seismic monitoring networks are the crucial elements in strong motion seismology for effective risk reduction. Low scale lateral variation of high intensity ground movement caused by earthquakes will be detected more effectively with densely located networks. However, the limitations of developing such project are rooted in expensive costs associated with the construction and installation in addition to bulky size of the conventional seismic observation system. Recently, micro-electromechanical system (MEMS) has being recognized in the applications of seismological and earthquake engineering due to the high precision obtained in these micron size semiconductor instruments and cheaper alternative for traditional seismic detector. ADXL345 is a type of digital triaxial MEMS accelerometer that is ideal for measurement of low-frequency vibrations and static accelerations of gravity, which makes it suitable for ground motion detection. Thus, this study aims at calibrating ADXL345 sensor that is required as sensing component in an affordable earthquake monitoring system with the Earthquake Benchmarking System (Penanda Aras Gempa Bumi, PAG) available in the inventory of Department of Mineral and Geoscience Malaysia, Sabah. Soil vibrations in EW (east-west or x-axis), NS (north-south or y-axis), and UD (up-down or z-axis) directions during random forces hit on the surface are recorded by both accelerometers. Acceleration magnitudes recorded by PAG and ADXL345 are extracted and data exploration is performed. Predominantly, ADXL345 measurements in horizontal and vertical ground movements are on a higher scale than the reference device. Subsequently, evaluation by using descriptive statistical analysis is chosen to produce numerical equations for data correction operations. İmplementation of the mathematical functions in ADXL345 for observing land movements in EW, NS, and UD directions resulted in decreasing the range values of output readings. Higher approximation of magnitudes of ground motion with the PAG system is achieved.

Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors.

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, the physical or chemical mechanisms of sensors, and the location of sensor platforms. In this study, we analyzed available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.