Earthquake spatial statistics analysis and its adaptation to faults and quaternary loess sediments using GIS (Case of study Khuzestan province)
Subject Areas : Regional Planning
1 - Assistant Professor of Geography Geomorphology, Islamic Azad University of Larestan, Larestan, Iran
Keywords: Earthquake, Khuzestan Province, spatial statistics, fault, Quaternary loess sediments,
Abstract :
What is important is the condition of cities and metropolises that are located on faults or in the vicinity of them and are in danger of earthquakes. The main objective of this research is to analyze the spatial statistics of earthquakes in Khuzestan province and its adaptation to loose faults and sediments. Quaternary. In order to investigate earthquake clusters, the first step was to determine the best earthquake interpolation method from the kriging index. The results showed that the inverse weighing method with the coefficient of 0.75 is the best model for earthquake zoning. For the analysis and distribution of the earthquake spatial distribution, Hot applications using the ArcGIS 10 software and the very flexible Spatial Statistics Tools tool were used. The cluster analysis is a general trend and can be obtained by various algorithms. The largest earthquake cluster pattern is related to earthquakes larger than 6 magnitudes of 003665/1. The results of the total earthquake investigation in Khuzestan province during the statistical period of 2014-2019 showed that the magnitude of the earthquakes in this province is from the northwest to the south-east. The earthquake has a direct relationship with faults and quaternary formation. Extended abstract Introduction Statistical studies of earthquakes, especially in recent decades, show that Iran is one of the most important earthquake-prone countries in the world and we have witnessed many incidents (Hashemi 2010: 10). Also in the Alpine-Himalayan earthquake belt, Iran has experienced 130 earthquakes of magnitude 7.5 or more over the past centuries, killing tens of thousands of people (Aghamohammadi 2016: 47). Undoubtedly, such earthquakes are not the last earthquakes to occur in Iran and lead to national crises. Today, the importance of the earthquake in our country is further understood by the intensification of the country's development process, urban development, population concentration and increasing material and spiritual capital and increasing the vulnerability of these assets in the seismic zone of Iran. With the increasing development and expansion of large cities in earthquake-prone areas in terms of population, economy, politics and society, the vulnerability of these cities to destructive earthquakes is increasing (Adib 2016: 6). Land features of the Zagros and Khuzestan plains have caused a large number of faults that are the source of earthquakes in this region (Naseri 2016: 47). Methodology: In this study, for the construction of the earthquake zone in Khuzestan province, data related to the severity of the earthquake in the study area entered the GIS database and by using statistical techniques, zoning was used to measure the earthquake in the study area. Was. Self-correlation is related to the relationship between residual values along the regression line. Strong self-correlation occurs when the values of a variable that are geographically close are related to each other, in other words, their changes occur systematically. If the effects or values of the variables related to them are randomly distributed in space, there should apparently be no connection between them. The Moran test examines the distribution pattern of these effects by considering the values of the studied traits in terms of cluster pattern or scattering. Indicates a completely unipolar (cluster) pattern, a value of zero indicates a random or multipolar aggregation pattern, and a value of 1. indicates a scattered pattern. The higher the coefficient, the higher the accumulation and the lower It is scattered. The result is displayed as clustered, random, or dispersed on the model output. The tools of this model are located in spatial statistics tools -> analyzing tools. Khuzestan province with an area of 64236 square kilometers is located between 47 degrees and 41 minutes to 50 degrees and 29 minutes east longitude and 29 degrees and 58 minutes to 33 degrees and 4 minutes north latitude of the equator in southwestern Iran. It is bordered by Lorestan province to the north, Chaharmahal Bakhtiari, Kohkiluyeh and Boyer-Ahmad provinces to the east and northeast, the Persian Gulf to the south, Iraq to the west, Ilam province to the northwest, and Iraq to the northeast. It is neighboring with Ilam province and from the southeast with Bushehr province and has 24 cities, 51 districts, 127 villages and 54 cities. Result and discution: At this stage, after extracting the earthquakes in Khuzestan province, we analyze the space of the earthquake in this province. At this stage, the optimal map, which has a redemption status, becomes a point where each area values both the points and the values side by side, which are also known as value groups. One of the indicators of spatial analysis is Moran index, this index relies on two important principles, one has a specific distribution of indicators and also relies on the values of coexistence. One of the weaknesses of this analysis is that it cannot identify the various types of spatial patterns. To analyze the spatial clustering of the earthquake in Khuzestan province using ArcGIS10 software and with the tools of Spatial Statistics Tools, which is very flexible, it was used. Conclusion: Most faults and fractures in Khuzestan province, northwest-southeast and consequently the distribution of the epicenter of the earthquake has a wide pattern, with a northwest-southeast trend. Northwest-southeast faults play an important role in seismic activity in the area, and the high density of seismic activity in areas with higher density of faults in the province confirms this. Studies have shown that seismic indexing, muran index, and clustering index can be used to segregate earthquakes, fault densities, and seismic zones. Moran statistics and hot spots were used to determine the pattern of earthquakes of different magnitudes in Khuzestan province. The largest cluster pattern of earthquakes is related to earthquakes larger than 6 Richter (003665/1). The hotspot analysis was used to show in which areas of high or low spatial focus. In the results of this analysis, hot and cold points in terms of magnitude of earthquakes in the area of each region are well identified and show that hot spots are seen in the province. How close are these centers to the city centers or are they located near any of the cities in each city? By examining these centers periodically, we can monitor the movement of the epicenter and be more prepared for the change of time. The results of the general direction of earthquake in Khuzestan province in the statistical period of 1929-2014 showed that the direction of earthquakes with different magnitudes except earthquakes with magnitudes greater than 6 Richter in this province is from northwest to southeast. It is likely that the presence of weak Quaternary faults and sediments along this route has been the main cause of these earthquakes in this direction. That is, the existence of weak Quaternary faults and sediments has been one of the main reasons for the earthquake in such a direction. Regarding the compatibility of earthquakes with weak Quaternary faults and sediments, it can be said that the greatest compatibility of faults and weak Quaternary sediments is related to earthquakes with magnitudes greater than 6 Richter.
1. Amin Naseri Mohammad Reza Zafarani Hamid 1395 Seismic data modeling using clustering in order to predict earthquakes, Sharif Civil Engineering Quarterly, Volume 2-32 No. 1/2 Summer 2016 Pages 29 to 37
2. Bahri Ali Khosravi Younes 2018 Application of space statistics statistics tools in Ark GIS software in environmental sciences Journal of Mapping Engineering and Environmental Information, Volume 9, Number 3, September 2016
3. Adib Ahmad Afzal Peyman Zare Masoumeh 2016 Seismic zoning of East Stan Yazd based on earthquakes and Quaternary faults using fractal modeling of Advanced Applied Geological Journal No. 22 Winter 2016
4. Agha Mohammadi Zanjirabad Hossein Afshari Somayeh Nouri Mohammad Reza 2016 Extraction of geological faults using remote sensing data Case study of Kopeh Dagh in the north of North Khorasan province
5. Hashemi Seyed Nasser Hassanloo Azra 2010 Spatial-temporal spatial analysis of the interaction of seismic faults on each other in the middle part of Zagros region Journal of Advanced Applied Geology No. 2 Volume 1 Winter 2011
6. Zangiabadi, Ali. Tabrizi, Nazanin (2015). Tehran earthquake and spatial assessment of urban areas. Geographical Research, Summer 2016, Volume 38, Number 56.
7. Fal Suleiman, Mahmoud; Mohammad Haj Yapour; Kamal Jamshidi (2012). Vulnerability of physical elements of rural settlements in earthquake-prone areas (example: Qaenat and Zirkuh counties), Geographical Spatial Planning Journal. Second year. Number six.
8. Gholizadeh, Mohammad Hossein, Heiman Shahabi and Hadi Nairi (2010). Earthquake risk zoning by multi-criteria spatial analysis method, Journal of Geography and Development, No. 17, Sistan and Baluchestan University Press, Zahedan.
9. Maliki, Fariborz (2018). The basics of seismic zoning and seismic hazard analysis methods. Earthquake Basics Training Seminar Construction and Analysis of Relative Earthquake Risk.14.Anselin, L. (2000). GIS, spatial econometrics and social science research,Journal of Geographical Systems, 2: 11–15.
_||_