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Temporal and Spatial Evolution Analysis of Earthquake Events in California and Nevada Based on Spatial Statistics

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Temporal and Spatial Evolution Analysis of Earthquake Events in California and Nevada Based on Spatial Statistics International Journal of Geo-Information Article Temporal and Spatial Evolution Analysis of Earthquake Events in California and Nevada Based on Spatial Statistics Weifeng Shan 1,2 , Zhihao Wang 2, Yuntian Teng 1,* and Maofa Wang 3 1 Institute of Geophysics, China Earthquake Administration, Beijing 100081, China; [email protected] 2 School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China; [email protected] 3 School of Computer and Information Security, Guilin University of Electronic Science and Technology, Guilin 541004, China; [email protected] * Correspondence: [email protected] Abstract: Studying the temporal and spatial evolution trends in earthquakes in an area is beneficial for determining the earthquake risk of the area so that local governments can make the correct decisions for disaster prevention and reduction. In this paper, we propose a new method for analyzing the temporal and spatial evolution trends in earthquakes based on earthquakes of magnitude 3.0 or above from 1980 to 2019 in California and Nevada. The experiment’s results show that (1) the frequency of earthquake events of magnitude 4.5 or above present a relatively regular change trend of decreasing–rising in this area; (2) by using the weighted average center method to analyze the spatial concentration of earthquake events of magnitude 3.0 or above in this region, we find that the weighted average center of the earthquake events in this area shows a conch-type movement law, where it moves closer to the center from all sides; (3) the direction of the spatial distribution of earthquake events in this area shows a NW–SE pattern when the standard deviational ellipse (SDE) Citation: Shan, W.; Wang, Z.; Teng, method is used, which is basically consistent with the direction of the San Andreas Fault Zone across Y.; Wang, M. Temporal and Spatial the north and south of California; and (4) the spatial distribution pattern of the earthquake events in Evolution Analysis of Earthquake this region is found to be clustered using the global spatial autocorrelation analysis method. This Events in California and Nevada study provides a new perspective for the exploration of the temporal and spatial evolution trends in Based on Spatial Statistics. ISPRS Int. earthquakes and understanding the earthquake risk in an area. J. Geo-Inf. 2021, 10, 465. https:// doi.org/10.3390/ijgi10070465 Keywords: spatial statistics; temporal and spatial evolution; weighted average center; standard deviational ellipse; global spatial autocorrelation analysis; earthquake risk Academic Editor: Wolfgang Kainz Received: 5 May 2021 Accepted: 3 July 2021 1. Introduction Published: 7 July 2021 Earthquakes are one of the main natural disasters on Earth [1,2]. An earthquake of Publisher’s Note: MDPI stays neutral magnitude 7.8 occurred in San Francisco, California, USA, on 18 April 1906, affecting with regard to jurisdictional claims in most areas of California, from Oregon City to Los Angeles, causing more than USD published maps and institutional affil- 4 million in economic damage [3,4]. The prediction of earthquakes is an important task iations. worldwide [5]. If the location, time, and magnitude of future large earthquakes can be accurately predicted, millions of lives may be saved. Although it is difficult to predict the occurrence of earthquakes, recent studies have shown that we can analyze the trends in the change in earthquake risk in a certain region by studying the temporal and spatial evolution processes of earthquake events [6,7], which is beneficial to disaster prevention Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. and reduction. This article is an open access article Earthquake events can be regarded as data with geospatial attributes. Due to the spa- distributed under the terms and tial constraints of different directions and distances between the spatial data, the traditional conditions of the Creative Commons statistical analysis methods cannot be used to describe constraint relationships between Attribution (CC BY) license (https:// these spatial data. Spatial statistics are a more recent application of spatial data [8,9]; the creativecommons.org/licenses/by/ discipline combines statistics with modern graphical computing technology and shows the 4.0/). implied spatial distribution, spatial pattern and spatial interactions in spatial data using ISPRS Int. J. Geo-Inf. 2021, 10, 465. https://doi.org/10.3390/ijgi10070465 https://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2021, 10, 465 2 of 13 intuitive methods [10]. After continuous development, the spatial statistics method has been widely applied in fields such as sociology, biology, demography, criminology and geology [11]. From the perspective of spatial statistics, the temporal and spatial evolution processes of things in geospatial data have certain rules. It is helpful for us to study and master these rules in all aspects of our lives. In the response to disasters, for example, Gan et al. [12] studied the temporal and spatial evolution of the vegetation in the Mianyuan River Basin using remote sensing images of vegetation from 1994 to 2017. The results showed that slopes of 30 to 40 degrees in this area are more susceptible to earthquake disturbances but are easier to repair, and the recovery period of vegetation in this area is eight to nine years after being damaged. Wang et al. [13] studied the temporal and spatial evolution trends in droughts in Northern Shanxi using the multi-model ensemble mean value; the results showed that the precipitation and temperature in this region will increase from south to north in the future, with the largest increase in the west. This study provided information that can be used to alleviate the threat of drought in this area. Based on the measured data of 56 rainfall stations and 11 hydrological stations in Central Guizhou, China, He et al. [14] developed the lagging index (LI) to analyze the temporal and spatial evolution characteristics of rainfall runoff in this area. The results showed that the lagged effect of runoff to rainfall in the area mainly demonstrated three lagged periods, and the order of temporal and spatial distribution differences of the lagged intensities was two lagged periods (Cv = 0.62). Zhang et al. [15] used the method of spatial statistics to conduct a systematic statistical analysis of the Iburi landslides, and determined several of its specific characteristics, such as high concentration and high mobility in the severe- disaster area. Qi et al. [16] studied the remote sensing data of landslides caused by the Wenchuan earthquake, and found that landslides’ incidence varies in different slopes with different structures. Many researchers have explored the temporal and spatial evolution characteristics of earthquake events. For example, Dias et al. [17] proposed a method for studying the temporal and spatial probability distributions of earthquakes, where the influence of the earthquakes’ data set on the temporal and spatial probability distribution of earthquakes was discussed based on different depths of hypocenters and thresholds. The experimental results showed that the increase in the jump forming the sequence between earthquakes affects the non-extensive characteristics in the temporary probability distribution. This analysis demonstrated the role of the robustness of the non-extensive statistical mechanics treatments in earthquake research, showing that it is an effective method with which to test memory and interactions. Yang et al. [18] used the temporal and spatial scan method to identify earthquake clusters based on the data of earthquakes with a moment magnitude (Mw) of not less than 5.6 from 1960 to 2014. The results indicated that earthquake clusters can be classified into two types based on duration: persistent clusters and burst clusters. Huang et al. [19] used the matched-filter detection method to obtain a relatively complete (magnitude of completeness ≈ 0.9) and precisely relocated earthquake catalog during the 2019 M 6.4 and M 7.1 Ridgecrest, California earthquake sequence, and discussed the influence of the spatial-temporal evolution process of the foreshock sequence on the preparation and nucleation process of the mainshock, which is important to improve our understanding of fault interactions, earthquake triggering, and evolving earthquake hazards during an ongoing earthquake sequence. In recent years, with the rapid development of artificial intelligence, researchers have begun to use artificial intelligence to study earthquake risk. Jena et al. [20] proposed a new earthquake risk assessment method that combines artificial neural network cross-validation (four-fold ANN-CV) and the hybrid Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution (AHP–TOPSIS), and applied it to Aceh, Indonesia. The experimental results showed that the accuracy of the model was 85.4% and the consis- tency ratio was 0.06. The model provides the advantages of a simple structure, convenient parameter adjustment, easy extension to other areas, and conduciveness to earthquake risk ISPRS Int. J. Geo-Inf. 2021, 10, 465 3 of 13 prevention and mitigation. Jia et al. [21] propose a machine learning method for seismic data interpolation. DeVries et al. [22] proposed a method of deep learning to predict the spatial distribution of aftershocks. Karimzadeh et al. [23] used different machine learning algorithms to predict the aftershock patterns. Mignan et al. [24] compared
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