Spatial-temporal analysis of seismicity before the 2012 Varzeghan, Iran, Mw 6.5 earthquake

Talebi, Mohammad; Zaré, Mehdi; Zadeh, Rohollah Madahi; Bali-Lashak, Aref

doi:10.3906/yer-1410-13

Cited by 8 publications

(3 citation statements)

References 38 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ZMAP software was introduced by Wiemer ( 2001) and has been used for the declustering technique based on the algorithm modelled by Reasenberg (1985). A large number of assessments of seismicity rate changes by using the ZMAP and different techniques can be found in literature after Wiemer (2001), such as Console et al (2000), Wyss et al (2004), Polat et al (2008), Öztürk (2011, 2013), Talebi et al (2015), Puangjaktha, Pailoplee (2018), Gentili et al (2019).…”

Section: Elimination Of Dependent Events From the Catalogue (Declustering Process) And The Standard Normal Deviate Z-test (Precursory Quimentioning

confidence: 99%

Statistical behaviours of earthquake occurrences in the Central Anatolian Region of Turkey: region-time-magnitude analyses

Öztürk

2021

Baltica

View full text Add to dashboard Cite

The main objective of this work is to make detailed region-time-magnitude analyses by describing the statistical behaviours of earthquakes in the Central Anatolian Region of Turkey. In this scope, several seismic and tectonic parameters such as Mcomp, b-value, Dc-value, Z-value, recurrence times and annual probabilities were evaluated. For the analyses, a homogeneous catalogue including 10,146 earthquakes with 1.0 ≤ Md ≤ 5.7 between 30 July 1975 and 29 December 2018 was used and spatio-temporal changes of earthquake behaviours were mapped for the beginning of 2019. Earthquake magnitudes varied from 1.9 to 3.0 on average, and hence Mcomp was considered to be 2.6. The b-value was calculated as 1.26 ± 0.07, and this relatively large value indicates that small-magnitude events are dominant. The Dc-value was computed as 1.31 ± 0.03. This small value means that distances between epicentres approach the diameter of the cluster, and seismic activity is more clustered at smaller scales or in larger regions. The spatio-temporal analyses of recurrence times suggest that the Central Anatolian Region has an intermediate/long-term earthquake hazard in comparison to occurrences of strong earthquakes in the short term. Several anomaly regions of a small b-value and a large Z-value were found in and around the Tuzgölü Fault Zone, Central Anatolian Fault Zone, Salanda fault and Niğde fault at the beginning of 2019. Thus, a combination of the regions with a lower b-value, a higher Z-value and also moderate recurrence times may give significant clues for the future possible earthquakes, and detected regions may be thought to be the most likely areas for strong/large events in the Central Anatolian Region.

show abstract

Section: Elimination Of Dependent Events From the Catalogue (Declustering Process) And The Standard Normal Deviate Z-test (Precursory Quimentioning

confidence: 99%

Statistical behaviours of earthquake occurrences in the Central Anatolian Region of Turkey: region-time-magnitude analyses

Öztürk

2021

Baltica

View full text Add to dashboard Cite

show abstract

“…Sea level rise data is important to understand the pattern in sea level rise and prediction model of sea level rise [19][20]. Earthquake data is used to analyze earthquake return period and earthquake vulnerability model scenario [21][22]. Storm data is important for analyzing the storm return period and predicting the storm model [23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of disaster information website based on web usability evaluation and quality content of disaster information

2019

View full text Add to dashboard Cite

Disaster is one of the most important researches because every country in the world has their own disaster potential. Disaster assessment improves over time due to technological and information advancements; and it is very reliant to a long period of data record. Disaster information is already available online in various websites, and is presented in form of map, data, video and multimedia contents. Each country has different disaster information standards, the Web Usability and quality of disaster-related content. This research objective is to find good model WEBUSE and quality content both national and international disaster website. National disaster website includes: Geospatial Information Agency (Badan Informasi Geospasial/BIG), Meteorological, Climatological and Geophysical Agency (Badan Meteorologi Klimatologi dan Geofisika/BMKG), National Disaster Management Authority (Badan Nasional Penanggulangan Bencana/BNPB); while international website includes: National Oceanic and Atmospheric Administration (NOAA) and United States Geological Survey (USGS). Usability comparison method uses the WEBUSE questionnaire with 4 categories, content organization and readability, navigation and links, user interface design, and performance and effectiveness; while the disaster-related content quality is measured using scoring method, with 4 categories, the geospatial information, data quality, product, and accessibility, according to United Nations Office for Disaster Risk Reduction (UNISDR). The usability level analysis for all 5 websites are classified as “good”, with USGS achieving the highest score of 0.703. As for the disaster-related quality content analysis result, USGS fulfills all categories.

show abstract

“…The spatial and temporal variations in seismicity in the northwestern region of Iran were evaluated with a focus on seismic precursors on 11 August 2012, when the Mw 6.5 Varzeghan earthquake occurred [5]. Wang et al [6] analyzed the spatial pattern of wildfire-related tweets based on kernel density estimation.…”

Section: Introductionmentioning

confidence: 99%

A Map Spectrum-Based Spatiotemporal Clustering Method for GDP Variation Pattern Analysis Using Nighttime Light Images of the Wuhan Urban Agglomeration

Zhang

Liu

2017

IJGI

View full text Add to dashboard Cite

Abstract:Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a region. Various GDP studies have been reported, and several studies have focused on spatiotemporal GDP variations. This study presents a map spectrum-based clustering approach to analyze the spatiotemporal variation patterns of GDP growth. First, a sequence of nighttime light images (from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS)) is used to support the spatial distribution of statistical GDP data. Subsequently, the time spectrum of each spatial unit is generated using a time series of dasymetric GDP maps, and then the spatial units with similar time spectra are clustered into one class. Each category has a similar spatiotemporal GDP variation pattern. Finally, the proposed approach is applied to analyze the spatiotemporal patterns of GDP growth in the Wuhan urban agglomeration. The experimental results illustrated regional discrepancies of GDP growth existed in the study area.

show abstract

Spatial-temporal analysis of seismicity before the 2012 Varzeghan, Iran, Mw 6.5 earthquake

Abstract: The area and dataThe studied area is inside of a polygon bounded by (37°N, 44°E), (40°N, 44°E), (40°N, 49°E), and (37°N, 49°E) corner points in NW Iran (Figure 1).

Cited by 8 publications

References 38 publications

Statistical behaviours of earthquake occurrences in the Central Anatolian Region of Turkey: region-time-magnitude analyses

Statistical behaviours of earthquake occurrences in the Central Anatolian Region of Turkey: region-time-magnitude analyses

Comparative analysis of disaster information website based on web usability evaluation and quality content of disaster information

A Map Spectrum-Based Spatiotemporal Clustering Method for GDP Variation Pattern Analysis Using Nighttime Light Images of the Wuhan Urban Agglomeration

Contact Info

Product

Resources

About