Exploring the knowledge domain of earthquake prediction based on bibliometric analysis and text mining

Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field.

A Safe Zone SMOTE Oversampling Algorithm Used in Earthquake Prediction Based on Extreme Imbalanced Precursor Data

Earthquake prediction based on extreme imbalanced precursor data is a challenging task for standard algorithms. Since even if an area is in an earthquake-prone zone, the proportion of days with earthquakes per year is still a minority. The general method is to generate more artificial data for the minority class that is the earthquake occurrence data. But the most popular oversampling methods generate synthetic samples along line segments that join minority class instances, which is not suitable for earthquake precursor data. In this paper, we propose a Safe Zone Synthetic Minority Oversampling Technique (SZ-SMOTE) oversampling method as an enhancement of the SMOTE data generation mechanism. SZ-SMOTE generates synthetic samples with a concentration mechanism in the hyper-sphere area around each selected minority instances. The performance of SZ-SMOTE is compared against no oversampling, SMOTE and its popular modifications adaptive synthetic sampling (ADASYN) and borderline SMOTE (B-SMOTE) on six different classifiers. The experiment results show that the quality of earthquake prediction using SZ-SMOTE as oversampling algorithm significantly outperforms that of using the other oversampling algorithms.

Kamchatka seismic monitoring and Earthquake prediction system and its evolution. Main results of observations in 2016-2020

In this paper we present brief review of results of Kamchatka Seismic Monitoring and Earthquake Prediction System operations in the last five years. In addition, the retrospective of development of hardware, equipment and software of the System performed. The main direction in the System evolution in this period concerned the creation and modernization of data acquiring and pro-cessing methods. One of main results is creation basic informational space, that includes all pro-cesses if seismic observations, from data acquiring till exchange (including external users) of da-ta processing results. In particular, the system of data storage was deeply modernized, high-speed access to the data archive was provides, high-performance computing clusters were deployed, all seismic stations were combined in the unified network. Development algorithms and software for data processing and seismic regime controlling was continued. Creation and development of the Seismological Data Informational System (SDIS) provide the access to seismic observations re-sults for research community. The service of automatic data exchange with external users was created and incorporated in SDIS. Kamchatka Seismic Monitoring and Earthquake Prediction System in 2016-2020 allowed registering and processing over 83 thousand tectonic and volcanic earthquakes. The complex studies for seven the strongest ones were conducted. Detailed analysis showed, that magnitude of completeness for regional scale is MLc=2.5, and for local scale (for example – volcano seismic monitoring) – MLc=–0.2.

Alert System of Earthquake Detection

Global warning shows unpredictablenature that changes in subsurface geologicfeatures. Due to the complex nature ofseismic events, it is challengeable task to efficiently identify the prominent features that leads to seismic events. Taking the advantage of availability of Seismic dataset, AI using machine learning is a powerful statistical tools to mitigate these practical challenges for earthquake prediction. The paper focuses on the alert and prediction model of an earthquake using machine learning algorithm. The alert time is a function of distance from epicenter and most alert time is for high magnitude earthquake, because high magnitude earthquake rupture over much larger area and take time to propagate, thus delayed for warning. The main aim of the project is to build precise earthquake prediction model using XGboost regression in machine learning technique. The XGboost regression model implemented on the dataset of 14700 data records. The data is gathered from the Kaggle platform that contains data on events of the earthquake in Indian Subcontinent of 18 years and the last updated is of the year 2018. Tableau 2019 is explored for data visualization of the predicted outcome. The proposed technique is able to give precise prediction of earthquake with sufficient time that will reduce the maximum damages and many lives will be saved. Keywords: Seismic signals, XGboost, Tableau, Prediction.