Early Detection Of Earthquakes Research Articles

Environmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms 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. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)

Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph neural networks (GNN) is becoming an important research area. The multilayered structure of the GNN with a number of epochs takes more training time. It is also hard to train the model with saturating nonlinearities. The batch normalization technique is applied to each mini-batch to reduce epochs in training and obtain a steady distribution of activation values. It improves model training and prediction accuracy. This study proposes a deep learning model batch normalization graph convolutional neural network (BNGCNN) for early earthquake detection. It consists of two main components: CNN and GNN. Input to the CNN model is multi-station and three-component waveform data with magnitude ≥3.0 were collected from January 2000 to January 2015 for Southern California. The extracted features of CNN are appended with location information and input to GNN model for earthquake detection. After hyperparameter tuning of the BNGCNN, when testing and evaluating the model on the Southern California dataset, our method shows promising results to the baseline model GNN by obtaining a low error rate to predict the magnitude, depth, and location of an earthquake.

Early earthquake detection capabilities of different types of future-generation gravity gradiometers

SUMMARY Since gravity changes propagate at the speed of light, gravity perturbations induced by earthquake deformation have the potential to enable faster alerts than the current earthquake early warning systems based on seismic waves. Additionally, for large earthquakes (Mw > 8), gravity signals may allow for a more reliable magnitude estimation than seismic-based methods. Prompt elastogravity signals induced by earthquakes of magnitude larger than 7.9 have been previously detected with seismic arrays and superconducting gravimeters. For smaller earthquakes, down to Mw ≃ 7, it has been proposed that detection should be based on measurements of the gradient of the gravitational field, in order to mitigate seismic vibration noise and to avoid the cancelling effect of the ground motions induced by gravity signals. Here we simulate the five independent components of the gravity gradient signals induced by earthquakes of different focal mechanisms. We study their spatial amplitude distribution to determine what kind of detectors is preferred (which components of the gravity gradient are more informative), how detectors should be arranged and how earthquake source parameters can be estimated. The results show that early earthquake detections, within 10 s of the rupture onset, using only the horizontal gravity strain components are achievable up to about 140 km distance from the epicentre. Depending on the earthquake focal mechanism and on the detector location, additional measurement of the vertical gravity strain components can enhance the detectable range by 10–20 km. These results are essential for the design of gravity-based earthquake early warning systems.

Earthquake Early Warning System Prototype Based on Lot Using Backpropagation Algorithm

Earthquakes are vibrations that occur on the earth's surface due to the sudden release of energy from the inside that creates seismic waves. An earthquake is caused by the movement of the earth's crust (the earth's plate). The frequency of a region refers to the type and size of earthquakes experienced during a period. Along with the development of early earthquake detection system technology provides a solution to minimize earthquake events. This research will discuss the system's design to determine the occurrence of earthquakes through time pattern analysis and Peak Ground Acceleration value. By using the Radial Basis Function Method, which later to minimize the loss of life from earthquakes. And help the main tools owned by the government. This study aims to determine the occurrence of earthquakes from Peak Ground Acceleration values and time analysis patterns, which are obtained from the decision of the Backpropagation method with an accuracy rate of 88%.

Pengembangan Sensor Koil Datar 3-D untuk Deteksi Gempa Dini di Wilayah Lombok

In this study, a flat coil application as a three-dimensional (3-D) vibration sensor for early detection of earthquake is designed to detect earthquakes in Lombok Island. Flat coil is a very thin winding wire and is used as an inductor. When inductance changes, frequency will change as well. The frequency can be converted into voltage to represent the distance. This effect is applied to the low frequency using three-dimensional pendulum (x,y,z) to detect the earthquake in Lombok, West Nusa Tenggara. Lombok Island is a territory that lies between the large plate of Asia and plate of Australia, thus, it belongs to earthquake belt territory. The software of 3-D vibration sensor for early detection of earthquakes uses Atmega 16 microcontroller vibration data acquisition program as a voltage data recorder. Meanwhile, Arduino Nano is used to display the waveform and to calculate the frequency of vibration. Based on the calibration, the frequency of vibrations which can be measured are around 0.468 Hz on the x-axis (source frequency is 0.46 Hz), around 0.55 Hz on the y-axis (source frequency is 0.5 Hz), and around 0.48 Hz on the z-axis (source frequency is 0.5 Hz).

Reliability prediction of seismic switch for early detection of earthquakes around NPPs

The recent Fukushima accident has posed several challenges to nuclear community with respect to safety of the nuclear power plants (NPPs) during simultaneous occurrence of seismic, Tsunami, multiple failures of safety systems and also maintaining the long term sub criticality during these events. Safety Instrumented Systems (SISs) are used in many application areas to reduce the likelihood of such severe accidents and/or to mitigate the consequences. An indigenously developed seismic switch is employed to safely shut down the reactor upon the occurrence of a seismic event. The reliability of seismic switch is of vital importance to ensure safe operation of a NPP. This paper presents the results of reliability study performed on digital and analog seismic switches to assess their performance.

User-side adaptive protection of location privacy in participatory sensing

The participatory sensing paradigm, through the growing availability of cheap sensors in mobile devices, enables applications of great social and business interest, e.g., electrosmog exposure measurement and early earthquake detection. However, users' privacy concerns regarding their activity traces need to be adequately addressed as well. The existing static privacy-enabling approaches, which hide or obfuscate data, offer some protection at the expense of data value. These approaches do not offer privacy guarantees and heterogeneous user privacy requirements cannot be met by them. In this paper, we propose a user-side privacy-protection scheme; it adaptively adjusts its parameters, in order to meet personalized location-privacy protection requirements against adversaries in a measurable manner. As proved by simulation experiments with artificial- and real-data traces, when feasible, our approach not only always satisfies personal location-privacy concerns, but also maximizes data utility (in terms of error, data availability, area coverage), as compared to static privacy-protection schemes.

Early Earthquake Detection and Warning Alarm System in Iran by a Telegraph Operator: A 116-Year-Old Disaster Prevention Attempt

According to an article printed in 1909 in a local Iranian newspaper, the New Iran ( Irān‐e Nau ; Fig. 1), a telegraph operator at the remote desert town of Kermān in southeast Iran, using coils of a single wire with return circuit through the ground of his telegraph instrument, picked up the Earth’s movement as an unusual electromagnetic signal on the magnetic needle, which gave a few seconds warning of an earthquake. Apparently, the operator was using a Wheatstone/Cooke‐type needle magneto‐electric telegraph built in the late nineteenth century. Figure 1. Yusef the telegraph operator’s 1909 account of his observations of unusual electromagnetic signals detected just before earthquakes in 1897 and 1909. He first noticed this anomaly on his telegraph instrument during an earthquake in 1897. Two medium‐magnitude earthquakes during that year were strongly felt and documented in Kermān. These were (1) the 22 May 1897 MMI∼VII+ M S∼5.5, Kuhbanān earthquake with its epicenter about 145 km northwest of Kermān; and (2) the 27 May 1897, MMI∼VIII M S∼>5.5 Chatrud earthquake located about 25 km north‐northwest of Kermān (see fig. 3 in Berberian, 2005). The telegraph operator observed that the first earthquake took place during the evening, suggesting that he was referring to the 27 May 1897 event. His second observation of a seismically induced disturbance was during the 27 October 1909, ∼ M S>5.5, MMI>VII+, Jowshān earthquake, with its damage zone located about 58 km to the east‐southeast of Kermān (fig. 5 in Berberian, 2005). The article, translated below from Persian into English, is signed by “Yusef Telegerāfchi va Tahvildār‐e Telegerāf‐khāneh‐ye Kermān” (“Yusef /Joseph, the telegraph operator and cashier of the Kermān Telegraph Office”). The article was printed nine months after the occurrence of the …

Intensive seismic activity around the Nankai trough revealed by DONET ocean-floor seismic observations

Megathrust earthquakes anticipated in the Nankai trough will likely cause severe damage in central and western Japan. The Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET), a network of permanent ocean-bottom seismic stations for the early detection of earthquakes and tsunamis developed by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), is in place above the expected source region of such earthquakes. Data from DONET sensors are transmitted in real time to our laboratory at JAMSTEC. Intensive ongoing seismic activity is being detected off the Kii Peninsula by DONET, mainly distributed in three clusters that overlap with the aftershock distribution of the 2004 off the Kii Peninsula earthquakes (MJMA = 7.1 and 7.4), and most of them are also aftershocks of the 2004 earthquakes. Some are linearly distributed on a different trend from the strike of the 2004 foreshock and mainshock fault planes. This result implies that the 2004 events triggered seismic activity on different faults on the subducting Philippine Sea plate. We also observed changes in seismic activity caused by the 2011 Tohoku-oki earthquake. These results could not have been obtained with on-land observations alone, which indicates the importance of DONET for monitoring seismic activity along the Nankai trough.

Development of General Evaluation System for Seismographs and its Installation in Early Earthquake Detection Systems

Compared with their predecessor, the new systems (DataStation, DataCraft) that we have developed offer various advantages such as an increase in the number of types of seismographic equipment applications, the provision of an early earthquake detection process function and the capability to process a large quantity of data collectively. Early earthquake alarm seismographs will be installed along the meter gauge lines in the near future. Before the existing seismographs are replaced with these early earthquake alarm seismographs, we should confirm the suitability of the new system based on the analysis of past data.

A New Method of Quickly Estimating Epicentral Distance and Magnitude from a Single Seismic Record

Rapid estimation of the epicentral distance and magnitude is of fundamental importance for early earthquake detection and warning systems. We present a novel method of estimating the epicentral distance from a single seismic record in a short amount of time. In order to quantitatively evaluate the difference in observed seismic waveforms, we introduced a simple function with the form of Bt·exp (- At ) and determined A and B in terms of the least-squares method by fitting this function to the initial part of the waveform envelope. We have found that log B is inversely proportional to log Δ, where Δ is the epicentral distance. This relation holds true regardless of the earthquake magnitude. By using this relation, we can roughly estimate the epicentral distance nearly immediately after the P -wave arrival. Then, we can readily estimate the magnitude from the maximum amplitude observed within a given short time interval after the P -wave arrival by using an empirical magnitude-amplitude relation that includes the epicentral distance as a parameter. Manuscript received 11 January 2002.

Terremotos y animales: oportunidades y prospectiva

Iberoamérica en el curso de la historia ha sido parte de los mayores terremotos que han tenido lugar en el planeta. Basta con mencionar por el lado de la península, el terremoto de Lisboa ocurrido en el año de 1755. Por el lado de Latinoamérica, el terremoto de Chile ocurrido en el año 2010. Estos eventos telúricos y los ocurridos en el resto del mundo han despertado el interés de la comunidad científica en el contexto de desarrollar modelos de prevención o predicción. Los terremotos de acuerdo con la teoría de la fractura elástica pueden anticiparse, la detección temprana de estos fenómenos es posible debido a la energía interna acumulada que es liberada en forma de ondas sísmicas, percibidas por especies sismo-receptoras como Lumbricus terrestris Linnaeus. En esta lógica, se propone valorar el potencial de las especie sismo-receptoras en la detección temprana de terremotos. Metodológicamente, el estudio se valió de una amplia revisión de literatura, lo que permitió un acercamiento al estado del arte sobre el objeto de estudio. Se concluye que efectivamente L. terrestris es una especie capaz de anticiparse a los terremotos.