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使用Python地理空间库检测安纳托利亚板块的异常地震活动区域和变形方向。

Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries.

作者信息

Güneyli Hakan, Saleh Ahmed Shaheen Mohammed

机构信息

Department of Geology, Cukurova University, Balcali-Adana, Turkey.

Department of Geology, College of Science, Kirkuk University, Kirkuk, Iraq.

出版信息

Heliyon. 2023 Mar 8;9(3):e14394. doi: 10.1016/j.heliyon.2023.e14394. eCollection 2023 Mar.

DOI:10.1016/j.heliyon.2023.e14394
PMID:36938436
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10020111/
Abstract

Various inaccurate traditional models have resulted in major ambiguities and gaps in the interpretation of Anatolian plate deformation directions. To address this issue, a GIS-based spatial statistical analysis method was used for the first time to detect the directional distribution of deformation along the Anatolian Plate in Turkey. Two strategies were used in this study: firstly, identifying the abnormally active seismic areas by detecting significant hotspot and cold spot clusters and confirming this detection using optimized hotspot analysis for earthquake events that occurred from 1900 to the end of 2019. Secondly, detecting the directional distribution of deformation using a Standard Deviational Ellipse (SDE) by calculating the standard deviation of the x and y coordinates from the mean center for each set of earthquake events in the Anaconda Python Platform and ArcGIS 10.8 software. Our improved geostatistical analysis results confirmed the existence of abnormal seismic hazard zones within the study area and three deformation directions: the east-west trend, the southeast-northwest trend, and the south-north trend.

摘要

各种不准确的传统模型导致了在安纳托利亚板块变形方向解释上的重大模糊性和空白。为解决这一问题，首次使用基于地理信息系统（GIS）的空间统计分析方法来检测土耳其安纳托利亚板块沿线变形的方向分布。本研究采用了两种策略：第一，通过检测显著的热点和冷点聚类来识别异常活跃的地震区域，并使用优化的热点分析对1900年至2019年底发生的地震事件进行确认。第二，在Anaconda Python平台和ArcGIS 10.8软件中，通过计算每组地震事件距平均中心的x和y坐标的标准差，使用标准偏差椭圆（SDE）检测变形的方向分布。我们改进的地统计分析结果证实了研究区域内存在异常地震危险区以及三个变形方向：东西走向、东南西北走向和南北走向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/4f24790c4584/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/c7ec1ef62b79/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/f37eaff846c8/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/4f24790c4584/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/adefbaf6f0c0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/d594c274e2ca/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/d8d8a64d45f1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/35ce7285cac3/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/e7809278a953/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/cd1557f6d377/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/d32aeefc0cde/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/c7ec1ef62b79/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/f37eaff846c8/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4886/10020111/4f24790c4584/gr10.jpg

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