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Geomatics and Geo-Information in Earthquake Studies

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 52442

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Special Issue Editors

Dr. Christian Bignami

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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
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Dr. Hiroyuki Miura

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Guest Editor

Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
Interests: earthquake engineering; geospatial analysis for damage assessment; remote sensing for disaster response; DEM analysis for geomorphology
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Dr. Maurizio Pollino

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Guest Editor

ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 00123 Rome, Italy
Interests: geomatics applications; land planning; spatial analysis; risk analysis; multi-risk assessment; GIS-based; decision support systems (DSS)
Special Issues, Collections and Topics in MDPI journals

Dr. Sonia Giovinazzi

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Guest Editor

ENEA National Agency for New Technologies, Energy and Sustainable Economic Development, 00196 Rome, Italy
Interests: models for assessing and monitoring the resilience of the built environment to natural hazards and climate change; models for assessing the vulnerability of buildings, distributed infrastructure, historic areas and cultural heritage to natural hazards and extreme events
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 1700 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 (14 papers)

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15 pages, 4218 KiB

Open AccessArticle

Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS

by Lina Han, Jiquan Zhang, Yichen Zhang, Qing Ma, Si Alu and Qiuling Lang

ISPRS Int. J. Geo-Inf. 2019, 8(5), 210; https://doi.org/10.3390/ijgi8050210 - 07 May 2019

Cited by 21 | Viewed by 4915

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 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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13 pages, 3184 KiB

Open AccessArticle

Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective

by Sadra Karimzadeh, Masashi Matsuoka, Jianming Kuang and Linlin Ge

ISPRS Int. J. Geo-Inf. 2019, 8(10), 462; https://doi.org/10.3390/ijgi8100462 - 22 Oct 2019

Cited by 17 | Viewed by 3769

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 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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16 pages, 6122 KiB

Open AccessEditor’s ChoiceArticle

DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

by Sadra Karimzadeh, Bakhtiar Feizizadeh and Masashi Matsuoka

ISPRS Int. J. Geo-Inf. 2019, 8(12), 537; https://doi.org/10.3390/ijgi8120537 - 27 Nov 2019

Cited by 14 | Viewed by 5443

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, 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)

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18 pages, 5705 KiB

Open AccessEditor’s ChoiceArticle

V_S30 Seismic Microzoning Based on a Geomorphology Map: Experimental Case Study of Chiang Mai, Chiang Rai, and Lamphun, Thailand

by Patcharavadee Thamarux, Masashi Matsuoka, Nakhorn Poovarodom and Junko Iwahashi

ISPRS Int. J. Geo-Inf. 2019, 8(7), 309; https://doi.org/10.3390/ijgi8070309 - 18 Jul 2019

Cited by 5 | Viewed by 3920

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 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 (V_S30), which was collected by the spatial autocorrelation (SPAC) method of microtremor array measurements. The V_S30 values were obtained from the phase velocity of the Rayleigh wave corresponding to a 40 m wavelength (C(40)). From the point feature, the V_S30 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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22 pages, 5261 KiB

Open AccessArticle

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

by Sangki Park and Kichul Jung

ISPRS Int. J. Geo-Inf. 2021, 10(9), 574; https://doi.org/10.3390/ijgi10090574 - 24 Aug 2021

Cited by 5 | Viewed by 2191 | Correction

Abstract

Seismic activities are serious disasters that induce natural hazards resulting in an incalculable amount of damage to properties and millions of deaths. Typically, seismic risk assessment can be performed by means of structural damage information computed based on the maximum displacement of the structure. In this study, machine learning models based on GPR are developed in order to estimate the maximum displacement of the structures from seismic activities and then used to construct fragility curves as an application. During construction of the models, 13 features of seismic waves are considered, and six wave features are selected to establish the seismic models with the correlation analysis normalizing the variables with the peak ground acceleration. Two models for six-floor and 13-floor buildings are developed, and a sensitivity analysis is performed to identify the relationship between prediction accuracy and sampling size. A 10-fold cross-validation method is used to evaluate the model performance, using the R-squared, root mean squared error, Nash criterion, and mean bias. Results of the six-parameter-based model apparently indicate a similar performance to that of the 13-parameter-based model for the two types of buildings. The model for the six-floor building affords a steadily enhanced performance by increasing the sampling size, while the model for the 13-floor building shows a significantly improved performance with a sampling size of over 200. The results indicate that the heighted structure requires a larger sampling size because it has more degrees of freedom that can influence the model performance. Finally, the proposed models are successfully constructed to estimate the maximum displacement, and applied to obtain fragility curves with various performance levels. Then, the regional seismic damage is assessed in Gyeonjgu city of South Korea as an application of the developed models. The damage assessment with the fragility curve provides the structural response from the seismic activities, which can assist in minimizing damage. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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16 pages, 10260 KiB

Open AccessArticle

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

by Ziyao Xing, Xiaohui Su, Junming Liu, Wei Su and Xiaodong Zhang

ISPRS Int. J. Geo-Inf. 2019, 8(8), 359; https://doi.org/10.3390/ijgi8080359 - 13 Aug 2019

Cited by 13 | Viewed by 3430

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 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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22 pages, 11839 KiB

Open AccessEditor’s ChoiceArticle

Post-Earthquake Recovery Phase Monitoring and Mapping Based on UAS Data

by Nikolaos Soulakellis, Christos Vasilakos, Stamatis Chatzistamatis, Dimitris Kavroudakis, Georgios Tataris, Ermioni-Eirini Papadopoulou, Apostolos Papakonstantinou, Olga Roussou and Themistoklis Kontos

ISPRS Int. J. Geo-Inf. 2020, 9(7), 447; https://doi.org/10.3390/ijgi9070447 - 17 Jul 2020

Cited by 9 | Viewed by 3068

Abstract

Geoinformatics plays an essential role during the recovery phase of a post-earthquake situation. The aim of this paper is to present the methodology followed and the results obtained by the utilization of Unmanned Aircraft Systems (UASs) 4K-video footage processing and the automation of geo-information methods targeted at both monitoring the demolition process and mapping the demolished buildings. The field campaigns took place on the traditional settlement of Vrisa (Lesvos, Greece), which was heavily damaged by a strong earthquake (Mw=6.3) on June 12th, 2017. For this purpose, a flight campaign took place on 3rd February 2019 for collecting aerial 4K video footage using an Unmanned Aircraft. The Structure from Motion (SfM) method was applied on frames which derived from the 4K video footage, for producing accurate and very detailed 3D point clouds, as well as the Digital Surface Model (DSM) of the building stock of the Vrisa traditional settlement, twenty months after the earthquake. This dataset has been compared with the corresponding one which derived from 25th July 2017, a few days after the earthquake. Two algorithms have been developed for detecting the demolished buildings of the affected area, based on the DSMs and 3D point clouds, correspondingly. The results obtained have been tested through field studies and demonstrate that this methodology is feasible and effective in building demolition detection, giving very accurate results (97%) and, in parallel, is easily applicable and suit well for rapid demolition mapping during the recovery phase of a post-earthquake scenario. The significant advantage of the proposed methodology is its ability to provide reliable results in a very low cost and time-efficient way and to serve all stakeholders and national and local organizations that are responsible for post-earthquake management. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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16 pages, 5005 KiB

Open AccessArticle

Balancing Hazard Exposure and Walking Distance in Evacuation Route Planning during Earthquake Disasters

by Wonjun No, Junyong Choi, Sangjoon Park and David Lee

ISPRS Int. J. Geo-Inf. 2020, 9(7), 432; https://doi.org/10.3390/ijgi9070432 - 10 Jul 2020

Cited by 12 | Viewed by 3662

Abstract

Efficient evacuation planning is important for quickly navigating people to shelters during and after an earthquake. Geographical information systems are often used to plan routes that minimize the distance people must walk to reach shelters, but this approach ignores the risk of exposure to hazards such as collapsing buildings. We demonstrate evacuation route assignment approaches that consider both hazard exposure and walking distance, by estimating building collapse hazard zones and incorporating them as travel costs when traversing road networks. We apply our methods to a scenario simulating the 2016 Gyeongju earthquake in South Korea, using the floating population distribution as estimated by a mobile phone network provider. Our results show that balanced routing would allow evacuees to avoid the riskiest districts while walking reasonable distances to open shelters. We discuss the feasibility of the model for balancing both safety and expediency in evacuation route planning. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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19 pages, 5613 KiB

Open AccessArticle

Detecting Destroyed Communities in Remote Areas with Personal Electronic Device Data: A Case Study of the 2017 Puebla Earthquake

by Andrew Marx, Mia Poynor, Young-Kyung Kim and Lauren Oberreiter

ISPRS Int. J. Geo-Inf. 2020, 9(11), 643; https://doi.org/10.3390/ijgi9110643 - 28 Oct 2020

Cited by 1 | Viewed by 2318

Abstract

Large-scale humanitarian disasters often disproportionately damage poor communities. This effect is compounded when communities are remote with limited connectivity and response is slow. While humanitarian response organizations are increasingly using a wide range of satellites to detect damaged areas, these images can be delayed days or weeks and may not tell the story of how many or where people are affected. In order to address the need of identifying severely damaged communities due to humanitarian disasters, we present an algorithmic approach to leverage pseudonymization locational data collected from personal cell phones to detect the depopulation of localities severely affected by the 2017 Puebla earthquake in Mexico. This algorithm capitalizes on building a pattern of life for these localities, first establishing which pseudonymous IDs are a resident of the locality and then establishing what percent of those residents leave those localities after the earthquake. Using a study of 15 localities severely damaged and 15 control localities unaffected by the earthquake, this approach successfully identified 73% of severely damaged localities. This individual-focused system provides a promising approach for organizations to understand the size and severity of a humanitarian disaster, detect which localities are most severely damaged, and aid them in prioritizing response and reconstruction efforts. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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19 pages, 3164 KiB

Open AccessEditor’s ChoiceArticle

Capacitated Refuge Assignment for Speedy and Reliable Evacuation

by Takanori Hara, Masahiro Sasabe, Taiki Matsuda and Shoji Kasahara

ISPRS Int. J. Geo-Inf. 2020, 9(7), 442; https://doi.org/10.3390/ijgi9070442 - 16 Jul 2020

Cited by 5 | Viewed by 2429

Abstract

When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not taken into account. In this paper, we propose a refuge assignment scheme that considers both the speediness and safety of evacuation under the refuge capacity constraint. We first formulate the refuge assignment problem as a two-step integer linear program (ILP). Since the two-step ILP requires route candidates between evacuees and their possible refuges, we further propose a speedy and reliable route selection scheme as an extension of the existing route selection scheme. Through numerical results using the actual data of Arako district of Nagoya city in Japan, we show that the proposed scheme can improve the average route reliability among evacuees by 13.6% while suppressing the increase of the average route length among evacuees by 7.3%, compared with the distance-based route selection and refuge assignment. In addition, we also reveal that the current refuge capacity is not enough to support speedy and reliable evacuation for the residents. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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26 pages, 10809 KiB

Open AccessArticle

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

by Maurizio Pollino, Sergio Cappucci, Ludovica Giordano, Domenico Iantosca, Luigi De Cecco, Danilo Bersan, Vittorio Rosato and Flavio Borfecchia

ISPRS Int. J. Geo-Inf. 2020, 9(4), 262; https://doi.org/10.3390/ijgi9040262 - 19 Apr 2020

Cited by 7 | Viewed by 3964

Abstract

Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble’s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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21 pages, 11365 KiB

Open AccessEditor’s ChoiceArticle

A New Agent-Based Methodology for the Seismic Vulnerability Assessment of Urban Areas

by Annalisa Greco, Alessandro Pluchino, Luca Barbarossa, Giovanni Barreca, Ivo Caliò, Francesco Martinico and Andrea Rapisarda

ISPRS Int. J. Geo-Inf. 2019, 8(6), 274; https://doi.org/10.3390/ijgi8060274 - 12 Jun 2019

Cited by 7 | Viewed by 3463

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 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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15 pages, 2880 KiB

Open AccessArticle

Multifractal Characteristics of Seismogenic Systems and b Values in the Taiwan Seismic Region

by Chun Hui, Changxiu Cheng, Lixin Ning and Jing Yang

ISPRS Int. J. Geo-Inf. 2020, 9(6), 384; https://doi.org/10.3390/ijgi9060384 - 10 Jun 2020

Cited by 5 | Viewed by 2273

Abstract

Seismically active fault zones are complex natural systems and they exhibit multifractal correlation between earthquakes in space and time. In this paper, the seismicity of the Taiwan seismic region was studied through the multifractal characteristics of the spatial-temporal distribution of earthquakes from 1st January 1995 to 1st January 2019. We quantified the multifractal characteristics of Taiwan at different scales and defined them as ΔD values. Furthermore, we studied the relationship between the ΔD and b values, which signifies the average size distribution of those earthquakes. The results are as follows. (1) The temporal multifractal curve changes substantially before and after the strong earthquakes. (2) The maximum ΔD value of the seismic region in Taiwan occurs at depths of 0~9 km, indicating that geological structures and focal mechanisms is the most complex at these depths compared with other depths. (3) ΔD values for different regions range from 0.2~1.5, and b values range from 0.65~1.3, with a significant positive correlation between them (ΔD = 1.5 × b − 0.68). For this purpose, a statistical relationship is developed between b and ΔD values, and regional and temporal changes of these parameters are analyzed in order to reveal the potential of future earthquakes in the study region. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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27 pages, 10881 KiB

Open AccessArticle

Assessing Earthquake Impacts and Monitoring Resilience of Historic Areas: Methods for GIS Tools

by Sonia Giovinazzi, Corinna Marchili, Antonio Di Pietro, Ludovica Giordano, Antonio Costanzo, Luigi La Porta, Maurizio Pollino, Vittorio Rosato, Daniel Lückerath, Katharina Milde and Oliver Ullrich

ISPRS Int. J. Geo-Inf. 2021, 10(7), 461; https://doi.org/10.3390/ijgi10070461 - 06 Jul 2021

Cited by 16 | Viewed by 4441

Abstract

Historic areas (HAs) are highly vulnerable to natural hazards, including earthquakes, that can cause severe damage, if not total destruction. This paper proposes methods that can be implemented through a geographical information system to assess earthquake-induced physical damages and the resulting impacts on the functions of HAs and to monitor their resilience. For the assessment of damages, making reference to the universally recognised procedure of convoluting hazard, exposure, and vulnerability, this paper proposes (a) a framework for assessing hazard maps of both real and end-user defined earthquakes; (b) a classification of the exposed elements of the built environment; and (c) an index-based seismic vulnerability assessment method for heritage buildings. Moving towards the continuous monitoring of resilience, an index-based assessment method is proposed to quantify how the functions of HAs recover over time. The implementation of the proposed methods in an ad hoc customized WebGIS Decision Support System, referred to as ARCH DSS, is demonstrated in this paper with reference to the historic area of Camerino-San Severino (Italy). Our conclusions show how ARCH DSS can inform and contribute to increasing awareness of the vulnerabilities of HAs and of the severity of the potential impacts, thus supporting effective decision making on mitigation strategies, post-disaster response, and build back better. Full article

(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)

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