China
Africa
Explore chapters and articles related to this topic
CapsNet and KNN-Based Earthquake Prediction Using Seismic and Wind Data
Published in Lavanya Sharma, Mukesh Carpenter, Computer Vision and Internet of Things, 2022
Sandeep Dwarkanath Pande, Soumitra Das, Pramod Jadhav, Amol D. Sawant, Shantanu S. Pathak, Sunil L. Bangare
Early, reliable, and computationally efficient earthquake prediction is also a big challenge. In this chapter, the seismic data obtained from sensors, wind data, and longitude and latitude information are used to predict an earthquake in India. The longitude and latitude information is fed to the K-nearest neighbor (KNN) [4] classifier to obtain one of the five seismic zones. The latest and most suitable NN termed as CapsNet, is then used for quick and reliable earthquake prediction. CapsNet accepts the seismic and wind data for intermediate earthquake prediction. Eventually, it uses the blender network that combines the output of KNN and CapsNet for final earthquake prediction. The proposed method is compared with present cutting-edge earthquake prediction systems concerning performance and results. The comparison reveals proposed approach obtains promising and reliable results. In many parameters, it outperforms other earthquake prediction systems. The proposed method signifies an advantage of low resource overhead and yields accurate results.
Development Of Multi-Component Borehole Instrument For Earthquake Prediction Study:
Published in H. Ogasawara, T. Yanagidani, M. Ando, Seismogenic Process Monitoring, 2017
H. Ishii, T. Yamauchi, S. Matsumoto, Y. Hirata, S. Nakao
Crustal movement observation (especially strain) is important for earthquake prediction research. Up to now Japan Meteorological Agency deployed volume strainmeters (Sacks & Evertson 1971) in 31 places in the Kanto Tokai area. The instrument can only observe a volume strain change. The principle of these instruments is to transfer strain change into movement of silicon oil sealed up in a cylindrical vessel. Sensitivity of instruments is high but instruments are very large, usually 8 to 10 m long and heavy. In earthquake prediction research multi-component strain observation is necessary as earthquake occurs by slip of fault, namely shear strain change. Sakata (1981) developed a 3-component strainmeter applying Sacks type principle. Those instruments are sensitive for temperature variation as silicon oil is filled inside of the vessel. On the other hand Gladwin (1984) developed a 3-component strainmeter whose measuring principle is to measure diameter change of cylindrical vessel by using capacity sensor.
An improved CS-LS hybrid algorithm on microseismic source location
Published in Systems Science & Control Engineering, 2021
Shujin Da, Xuegui Li, Fei Han, Hanyang Li
The microseismic (MS) source location has been a hot research field for more than a hundred years (Batchelor et al., 1983). It plays a significant role in earthquake prediction, engineering earthquakes, earth structure research, and crustal stress analysis (Anikiev et al., 2014). The rapid determination of the seismic location provides very critical information for earthquake assessment and emergency rescue. Moreover, accurately determining the localization helps to identify seismogenic faults, studying the process of earthquake incubation and triggering (Maxwell et al., 2010). As a result, it is of great significance to determine the MS source. The technology is not only appraising the fracture but also saving resources (Grigoli et al., 2016). In consideration of the low permeability of these reservoirs (Han et al., 2020), improving the positioning accuracy has become one of the most urgent technology in oil exploitation. Only by improving the positioning accuracy can we promote the flow and acquisition of oil and gas reservoirs and finally achieve the purpose of increasing production(Fukui et al., 2017).
Recent progress in radon-based monitoring as seismic and volcanic precursor: A critical review
Published in Critical Reviews in Environmental Science and Technology, 2020
Nury Morales-Simfors, Ramon A. Wyss, Jochen Bundschuh
Prediction of earthquakes and volcanic eruptions has concerned researchers for many decades (Arora, Rawat, Kumar, & Choubey, 2012; Igarashi & Wakita, 1995; Petraki et al., 2015; Wakita, Nakamura, Notsu, Noguchi, & Asada, 1980) leading to the conclusion of some researchers that earthquake prediction is difficult if not altogether impossible (Geller, Jackson, Kagan, & Mulargia, 1997; Swinbanks, 1997). Current techniques such as seismic (Barman, Chaudhuri, Ghose, Deb, & Sinha, 2014; Chaudhuri et al., 2013; Das et al., 2006; Ghosh, Deb, & Sengupta, 2009; Koike, Yoshinaga, Ueyama, & Asaue, 2014; Piersanti, Cannelli, & Galli, 2016; Yang et al., 2011; Zeng et al., 2015), geodetic (Kuo et al., 2011; Roeloffs, 1999), geochemical (Chaudhuri, Das, Bhandari, Sen, & Sinha, 2010; Chaudhuri et al., 2011; Chaudhuri et al., 2013; Hartmann & Levy, 2005; Ingebritsen & Manga, 2014; Prasad, Prasad, Choubey, & Ramola, 2009; Wakita, 1996; Yang et al., 2011), geological (Alfonso et al., 2015; Koike, Yoshinaga, & Asaue, 2014; Tuccimei, Mollo, Soligo, Scarlato, & Castelluccio, 2015), atmospheric/ionospheric (Guo et al., 2015; Kotsarenko et al., 2012; Ondoh, 2009; Parrot et al., 2016), geomagnetic (Arora et al., 2012; Harada et al., 2005; Pulinets, Ouzounov, Karelin, & Davidenko, 2015; Pulinets, Ouzounov, Davydenko, & Petrukhin, 2016), and electrical (Harada et al., 2005; Pulinets et al., 2015; Trique, Richon, Perrier, Avouac, & Sabroux, 1999) precursors have been observed and evaluated as potential tools for prediction of these events (Figure 1).
Earthquake prediction with meteorological data by particle filter-based support vector regression
Published in Engineering Applications of Computational Fluid Mechanics, 2018
Pouria Hajikhodaverdikhan, Mousa Nazari, Mehrdad Mohsenizadeh, Shahaboddin Shamshirband, Kwok-wing Chau
The parameters such as “earthquake precursors” (Lu et al., 2018) have been used by scientists to predict the earthquake. During the past few decades, the hope for accurate earthquake prediction has dramatically increased with the advancement of computer systems. Up until now, about 30 precursors were identified. Since the probability of an earthquake cannot be predicted by focusing only on one precursor. Due to the importance of the subject, an acceptable technique can be found by improving the precursor systems or combining the signs. Earthquake processes are complex natural phenomena and it is difficult to capture diagnostic precursor, if any, before the occurrence of an earthquake (Asencio-Cortés, Martínez-Álvarez, Morales-Esteban, Reyes, & Troncoso, 2017).