Special Issue "Geomatics and Geo-Information in Earthquake Studies"

Special Issue Editors

Dr. Christian Bignami
E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), National Earthquake Observatory, Rome, Italy
Interests: SAR interferometry; earthquakes; volcanoes; subsidence; landslide; satellite image analysis; natural hazards
Special Issues and Collections in MDPI journals
Assoc. Prof. Hiroyuki Miura
E-Mail Website1 Website2 Website3
Guest Editor
Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan
Tel. +81-82-424-7798
Interests: earthquake engineering; ground motion analysis; microtremor analysis; GIS for damage assessment; remote sensing for damage assessment; DEM analysis for landslides
Dr. Maurizio Pollino
E-Mail Website
Guest Editor
ENEA - Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Laboratory for the Analysis and Protection of Critical Infrastructures (APIC) Rome, Italy
Interests: GIS and remote sensing applications to environmental studies; risk analysis; critical infrastructures protection; design and development of GIS-based decision support systems (DSSs)
Special Issues and Collections in MDPI journals
Dr. Sonia Giovinazzi
E-Mail Website
Guest Editor
ENEA - Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Laboratory for the Analysis and Protection of Critical Infrastructures (APIC) Rome, Italy
Interests: GIS-based risk analysis for natural and human-induced hazards; seismic vulnerability analysis for structures and infrastructures at territorial scale; decision support systems for risk reduction, emergency management, and post-event resilience enhancement

Special Issue Information

Dear Colleagues,

In the past decade, large-scale earthquakes and tsunamis have struck major populated areas and produced heavy casualties and losses in many countries.

Geomatics methodologies and GIS-based hazard and risk analysis can be powerful tools to inform and support the development of effective disaster mitigation strategies, to reduce the impact of future earthquakes, and to assist early recovery and reconstruction activities.

Advances in geomatics and geospatial technologies are envisaged for extracting the most suitable information to assess seismic hazard and the seismic vulnerability of structures and infrastructures from the currently available large set of geographical data, including remote sensing imagery from satellites. Lessons learned from recent major earthquakes are also important to understand the mechanisms of ground shakings and structural damage.

GIS (Geographic Information Systems) have great potentialities for characterizing spatial patterns of natural and built environments. Within GIS, the location and inventory of buildings and infrastructures as well as their constructive features can be overlaid with seismic hazard maps (describing ground conditions, seismic shaking, amplification and co-seismic effects, etc.), as well as with information related to the resident communities, thus allowing investigation of the mechanisms that lead to physical damages to structures and infrastructures, and to social and economic impacts and losses in the short- and long-term. Information stored and processed via GIS can also be effective for deriving lessons learned.

DSSs (decision support systems), incorporating GIS-based analysis, are essential for the development of spatial analysis to support risk mitigation and risk management decision-making processes, but their studies have rarely been summarized. In order to concentrate the knowledge and experiences accumulated thus far, we would like to invite you to submit articles about your recent work. The topics of interest include but are not limited to the following keywords.

Dr. Christian Bignami
Dr. Hiroyuki Miura
Dr. Maurizio Pollino
Dr. Sonia Giovinazzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk analysis
  • building inventory data development for damage assessment
  • ground condition and seismic shaking mapping
  • damage and loss estimation
  • disaster mitigation planning
  • spatial data analysis for recovery/reconstruction process
  • vulnerability assessment
  • critical infrastructure protection against earthquakes
  • GIS-based decision support systems for risk analysis, emergency management, scenario simulations
  • resilience enhancement strategies

Published Papers (6 papers)

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Research

Open AccessArticle
DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran
ISPRS Int. J. Geo-Inf. 2019, 8(12), 537; https://doi.org/10.3390/ijgi8120537 - 27 Nov 2019
Abstract
Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, [...] Read more.
Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
Open AccessArticle
Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective
ISPRS Int. J. Geo-Inf. 2019, 8(10), 462; https://doi.org/10.3390/ijgi8100462 - 22 Oct 2019
Abstract
Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of [...] Read more.
Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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Open AccessArticle
Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake
ISPRS Int. J. Geo-Inf. 2019, 8(8), 359; https://doi.org/10.3390/ijgi8080359 - 13 Aug 2019
Abstract
Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant [...] Read more.
Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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Open AccessArticle
VS30 Seismic Microzoning Based on a Geomorphology Map: Experimental Case Study of Chiang Mai, Chiang Rai, and Lamphun, Thailand
ISPRS Int. J. Geo-Inf. 2019, 8(7), 309; https://doi.org/10.3390/ijgi8070309 - 18 Jul 2019
Abstract
Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and [...] Read more.
Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and primary data required for implementation of seismic disaster management are insufficient, including data on soil character which are used in amplification analyses for further ground motion prediction evaluations. Therefore, in this study, soil characterization was performed to understand the seismic responses of soil rigidity. The final output is presented in a seismic microzoning map. A geomorphology map was selected as the base map for the analysis. The geomorphology units were assigned with a time-averaged shear wave velocity of 30 m (VS30), which was collected by the spatial autocorrelation (SPAC) method of microtremor array measurements. The VS30 values were obtained from the phase velocity of the Rayleigh wave corresponding to a 40 m wavelength (C(40)). From the point feature, the VS30 values were transformed into polygonal features based on the geomorphological characteristics. Additionally, the automated geomorphology classification was explored in this study. Then, the seismic microzones were compared with the locations of major damage from the 2014 records for validation. The results from this study include geomorphological classification and seismic microzoning. The results suggest that the geomorphology units obtained from a pixel-based classification can be recommended for use in seismic microzoning. For seismic microzoning, the results show mainly stiff soil and soft rocks in the study area, and these geomorphological units have relatively high amplifications. The results of this study provide a valuable base map for further disaster management analyses. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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Open AccessArticle
A New Agent-Based Methodology for the Seismic Vulnerability Assessment of Urban Areas
ISPRS Int. J. Geo-Inf. 2019, 8(6), 274; https://doi.org/10.3390/ijgi8060274 - 12 Jun 2019
Abstract
In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost-intensive and require great processing time and [...] Read more.
In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost-intensive and require great processing time and expertise judgment. The aim of the present study is to propose a new methodology able to combine information and tools coming from different scientific fields in order to reproduce the effects of a seismic input in urban areas with known geological features and to estimate the entity of the damages caused on existing buildings. In particular, we present a new software called ABES (Agent-Based Earthquake Simulator), based on a Self-Organized Criticality framework, which allows to evaluate the effects of a sequence of seismic events on a certain large urban area during a given interval of time. The integration of Geographic Information System (GIS) data sets, concerning both geological and urban information about the territory of Avola (Italy), allows performing a parametric study of these effects on a real context as a case study. The proposed new approach could be very useful in estimating the seismic vulnerability and defining planning strategies for seismic risk reduction in large urban areas Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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Open AccessArticle
Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS
ISPRS Int. J. Geo-Inf. 2019, 8(5), 210; https://doi.org/10.3390/ijgi8050210 - 07 May 2019
Cited by 1
Abstract
The impacts of earthquakes and secondary disasters on ecosystems and the environment are attracting increasing global attention. Development of uncertainty reasoning models offers a chance to research these complex correlations. The primary aim of this research was to construct a disaster chain hazard [...] Read more.
The impacts of earthquakes and secondary disasters on ecosystems and the environment are attracting increasing global attention. Development of uncertainty reasoning models offers a chance to research these complex correlations. The primary aim of this research was to construct a disaster chain hazard assessment model that combines a Bayesian Network model and the ArcGIS program software for Changbai Mountain, China, an active volcano with a spate of reported earthquakes, collapses, and landslide events. Furthermore, the probability obtained by the Bayesian Networks was used to determine the disaster chain probability and hazard intensity of the earthquake events, while ArcGIS was used to produce the disaster chain hazard map. The performance of the Bayesian Network model was measured by error rate and scoring rules. The confirmation of the outcomes of the disaster chain hazard assessment model shows that the model demonstrated good predictive performance on the basis of the area under the curve, which was 0.7929. From visual inspection of the produced earthquake disaster chain hazard map, highly hazardous zones are located within a 15 km radius from the Tianchi center, while the northern and the western parts of the studied area are characterized mainly by “very low” to “low” hazard values. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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