Special Issue "Geoinformatics and Data Mining in Earth Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 31 January 2023 | Viewed by 13151

Special Issue Editors

Prof. Dr. Alexei Gvishiani
E-Mail Website
Guest Editor
Geophysical Center of the Russian Academy of Sciences (GC RAS), 119296 Moscow, Russia
Interests: geophysics; algorithmic systems analysis; big data; geoinformatics; earth magnetic field; earth sciences data mining; pattern recognition; seismic zonation
Dr. Boris Dzeboev
E-Mail Website
Co-Guest Editor
Geophysical Center of the Russian Academy of Sciences (GC RAS), 119296 Moscow, Russia
Interests: geophysics; algorithmic systems analysis; big data; geoinformatics; earth magnetic field; earth sciences data mining; pattern recognition; seismic zonation

Special Issue Information

Dear Colleagues,

The modern intensive accumulation of data on the wide range of observations in Earth Sciences requires sufficient methods and techniques for their comprehensive analysis and data mining. The analysis of data on potentially hazardous natural events such as earthquakes, tsunami, avalanches, landslides, geomagnetic storms, volcanic eruptions, mudflows, subsidence, floods, forest fires, tornadoes, etc. is of special significance. Analysis, assessment, and prediction of natural hazards are vital problems in the Earth Sciences.

This Special Issue will present an outlook of modern Data Science approaches of geoinformatics and data mining in application to a wide range of Earth Sciences disciplines, including the hazard studies listed above. Applications of systems analysis, geographic systems, data mining, Big Data aspects, etc. are welcome to this Special Issue. Particular attention will also be paid to the preservation of historical geodata.

We welcome original articles from various fields of Earth Sciences and Data Science. Articles dealing with interdisciplinary problems and challenges in Earth Sciences are especially welcome. Papers devoted to applications of Big Data theory and practice in geosciences are of particular interest.

Prof. Dr. Alexei Gvishiani
Dr. Boris Dzeboev
Guest Editors

Manuscript Submission Information

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Keywords

  • geophysics
  • geoinformatics
  • Earth sciences data mining

Published Papers (17 papers)

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Research

Article
Strong Earthquake-Prone Areas in the Eastern Sector of the Arctic Zone of the Russian Federation
Appl. Sci. 2022, 12(23), 11990; https://doi.org/10.3390/app122311990 - 23 Nov 2022
Viewed by 186
Abstract
This paper continues the series of publications by the authors on the recognition of areas prone to the strongest, strong, and significant earthquakes using the FCAZ system-analytical method. The areas prone to earthquakes with M ≥ 5.5 in the eastern sector of the [...] Read more.
This paper continues the series of publications by the authors on the recognition of areas prone to the strongest, strong, and significant earthquakes using the FCAZ system-analytical method. The areas prone to earthquakes with M ≥ 5.5 in the eastern sector of the Arctic zone of the Russian Federation were recognized. It is shown that certain potential high seismicity zones are well confined to the boundaries of the Eurasian, North American, and Okhotsk tectonic plates. In addition, according to the results of the FCAZ recognition, some areas located at a sufficient distance from the main tectonic structures of the studied region were also recognized as highly seismic. The results of the study, among other factors, justify the use of the assessment of the completeness magnitude in the catalog for choosing the set of recognition objects for the FCAZ method. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Deep Transfer Learning Model for Semantic Address Matching
Appl. Sci. 2022, 12(19), 10110; https://doi.org/10.3390/app121910110 - 08 Oct 2022
Cited by 1 | Viewed by 390
Abstract
Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods [...] Read more.
Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods that rely on rules or text similarity struggle when dealing with nonstandard address data. Deep-learning-based methods often require extracting address semantics for embedded representation, which not only complicates the matching process, but also affects the understanding of address semantics. Inspired by deep transfer learning, we introduce an address matching approach based on a pretraining fine-tuning model to identify semantic similarities between various addresses. We first pretrain the address corpus to enable the address semantic model (abbreviated as ASM) to learn address contexts unsupervised. We then build a labelled address matching dataset using an address-specific geographical feature, allowing the matching problem to be converted into a binary classification prediction problem. Finally, we fine-tune the ASM using the address matching dataset and compare the output with several popular address matching methods. The results demonstrate that our model achieves the best performance, with precision, recall, and an F1 score above 0.98. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Data Management and Processing in Seismology: An Application of Big Data Analysis for the Doublet Earthquake of 2021, 03 March, Elassona, Central Greece
Appl. Sci. 2022, 12(15), 7446; https://doi.org/10.3390/app12157446 - 25 Jul 2022
Viewed by 611
Abstract
On 3 March 2021 (10:16, UTC), a strong earthquake, Mw 6.3, occurred in Elassona, Central Greece. The epicenter was reported 10 km west of Tyrnavos. Another major earthquake followed this event on the same day at Mw 5.8 (3 March 2021, [...] Read more.
On 3 March 2021 (10:16, UTC), a strong earthquake, Mw 6.3, occurred in Elassona, Central Greece. The epicenter was reported 10 km west of Tyrnavos. Another major earthquake followed this event on the same day at Mw 5.8 (3 March 2021, 11:45, UTC). The next day, 4 March 2021 (18:38, UTC), there was a second event with a similar magnitude as the first, Mw 6.2. Both events were 8.5 km apart. The following analysis shows that the previous events and the most significant aftershocks were superficial. However, historical and modern seismicity has been sparse in this area. Spatially, the region represents a transitional zone between different tectonic domains; the right-lateral slip along the western end of the North Anatolian Fault Zone (NAFZ) in the north Aegean Sea plate-boundary structure ends, and crustal extension prevails in mainland Greece. These earthquakes were followed by rich seismic activity recorded by peripheral seismographs and accelerometers. The installation of a dense, portable network from the Aristotle University of Thessaloniki team also helped this effort, installed three days after the seismic excitation, as seismological stations did not azimuthally enclose the area. In the present work, a detailed analysis was performed using seismological data. A seismological catalogue of 3.787 events was used, which was processed with modern methods to calculate 34 focal mechanisms (Mw > 4.0) and to recalculate the parameters of the largest earthquakes that occurred in the first two days. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Identification of Areas of Anomalous Tremor of the Earth’s Surface on the Japanese Islands According to GPS Data
Appl. Sci. 2022, 12(14), 7297; https://doi.org/10.3390/app12147297 - 20 Jul 2022
Viewed by 517
Abstract
Statistical properties of Earth surface tremors measured by means of GPS were investigated. This article considers measurements of the Earth’s surface displacements in three orthogonal directions relayed by a network of GPS sensors with about 1200 points distributed across Japan in 2009–2021. Next, [...] Read more.
Statistical properties of Earth surface tremors measured by means of GPS were investigated. This article considers measurements of the Earth’s surface displacements in three orthogonal directions relayed by a network of GPS sensors with about 1200 points distributed across Japan in 2009–2021. Next, the following characteristics of the tremors were considered: the entropy of the distribution of squared orthogonal wavelet coefficients, the entropy of the distribution of power spectrum values, and the spectral index. The anomalous regions of maxima of probability densities of the distribution of extreme values of the tremor statistics were determined: entropy minima and spectral index maxima. The average density maps of the distribution of extreme value tremor statistics were found to be highly correlated with one another. This made it possible to consider a weighted average density map and identify five anomalous regions in the center and south of Japan. A trajectory of visiting anomalous regions by a sequence of points realizing local maxima of the average probability density was obtained, for which seasonal periodicity was set. Estimates of changes in the average and maximum values of the correlation coefficients of tremor properties in an auxiliary network of 16 reference points in a semi-annual time window were obtained. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
Appl. Sci. 2022, 12(12), 6259; https://doi.org/10.3390/app12126259 - 20 Jun 2022
Viewed by 339
Abstract
The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there [...] Read more.
The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there have been several studies that use type-1 fuzzy membership functions to mine spatial fuzzy co-location patterns, there is great uncertainty associated with such membership functions. To address this problem, we propose a spatial fuzzy co-location pattern mining method based on interval type-2 fuzzy sets. First, we collect the interval evaluation values of the interval data of attribute information from experts to form granular data. Next, the original type-1 fuzzy membership function is extended to a granular type-2 fuzzy membership function based on elliptic curves. We use a gradual method to adjust the parameters of the fuzzy membership function so that its footprint of uncertainty satisfies both the connectivity and the given confidence. Based on this granular type-2 fuzzy membership function, we fuzzify the attribute information of instances and define the concepts of fuzzy features and fuzzy co-location patterns. A fuzzy co-location pattern mining algorithm based on spatial cliques is then proposed, termed the FCPM-Clique algorithm. In order to improve the efficiency of the algorithm, we propose two pruning strategies. In addition, we extend two classical spatial pattern mining algorithms, the Join-based algorithm and the Joinless algorithm, to mine fuzzy co-location patterns based on interval type-2 fuzzy sets. Many experiments on synthetic and real-world datasets are conducted, the performance of the three algorithms is compared, and the effectiveness and efficiency of our proposed FCPM-Clique algorithm is demonstrated. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic
Appl. Sci. 2022, 12(10), 5010; https://doi.org/10.3390/app12105010 - 16 May 2022
Viewed by 531
Abstract
The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The [...] Read more.
The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The article describes the catalog compilation algorithm, as well as formalized procedures for removing duplicates and choosing the optimal magnitude scale. Due to different network configurations and record processing methods, different agencies may register/miss different events. This results in the absence of some events in different earthquake catalogs. Therefore, merging the data of various seismological agencies will provide the most complete catalog for the studied region. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) necessarily arises. An additional difficulty arises when distinguishing between aftershocks and duplicates since both are events that are close in space and time. To solve this problem, we used a modified nearest neighbor method developed earlier by the authors. The modified version, which is focused on identifying duplicates and distinguishing between duplicates and aftershocks, uses a probabilistic metric in the network error space to determine the epicenters and times of seismic events. In the present paper, a comparison and regression analysis of the different magnitude types of the integrated catalog is carried out, and based on the obtained ratios, the magnitude estimates are unified. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Prospects of Geoinformatics in Analyzing Spatial Heterogeneities of Microstructural Properties of a Tectonic Fault
Appl. Sci. 2022, 12(6), 2864; https://doi.org/10.3390/app12062864 - 10 Mar 2022
Viewed by 797
Abstract
The paper proposes a special technique for microstructural analysis (STMA) of rock samples based on two provisions. The first one is an algorithm for the automatic detection and digitalization of microstructures in images of oriented thin sections. The second one utilizes geographic information [...] Read more.
The paper proposes a special technique for microstructural analysis (STMA) of rock samples based on two provisions. The first one is an algorithm for the automatic detection and digitalization of microstructures in images of oriented thin sections. The second one utilizes geographic information system (GIS) tools for an automatized analysis of objects at the micro scale. Using STMA allows the establishment of geometric features of fissure and pore space of rock samples to determine the parameters of stress–strain fields at different stages of rock massif deformation and to establish a relationship between microstructures and macrostructures. STMA makes it possible to evaluate the spatial heterogeneity of physical and structural properties of rocks at the micro scale. Verification of STMA was carried out using 15 rock samples collected across the core of the Primorsky Fault of the Baikal Rift Zone. Petrographic data were compared to the quantitative parameters of microfracture networks. The damage zone of the Primorsky Fault includes three clusters characterized by different porosity, permeability, and deformation type. Findings point to the efficiency of STMA in revealing the spatial heterogeneity of a tectonic fault. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Temporal and Spatial Geophysical Data Analysis in the Issues of Natural Hazards and Risk Assessment (in Example of North Ossetia, Russia)
Appl. Sci. 2022, 12(6), 2790; https://doi.org/10.3390/app12062790 - 09 Mar 2022
Viewed by 499
Abstract
The paper considers the aspects of hazard assessment within the framework of a generalized approach. The aim of the study is to improve the methodology for more accurate and detailed probabilistic assessments of risks of various nature. A complex hazard map is constructed [...] Read more.
The paper considers the aspects of hazard assessment within the framework of a generalized approach. The aim of the study is to improve the methodology for more accurate and detailed probabilistic assessments of risks of various nature. A complex hazard map is constructed in an example of the territory of the Republic of North Ossetia-Alania and the construction site of the Mamison resort. Based on the analysis of data on Quaternary formations and quantitative estimates, it was concluded that the natural average static environmental evolution proceeds in the mode of the dynamic balance of two factors: mountain building and the equivalent increase in denudation, of which about 90% is transported and deposited by river waters and winds outside the territory. The remaining 10% is deposited in intermountain depressions and river valleys in situ. Geodynamic and climatic factors of influence on the geoenvironment create the danger of excessive environmental impact and disruption of its equilibrium development under anthropogenic impacts, which must be taken into account in designing. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Eco-Geophysical and Geoecological Factors in Assessing the State of the Geological Environment Based on the Analysis of Spatial Databases of the Territory of the Republic of North Ossetia–Alania
Appl. Sci. 2022, 12(5), 2644; https://doi.org/10.3390/app12052644 - 03 Mar 2022
Viewed by 466
Abstract
The article considers the main sources of pollutionin the territory of the Republic of North Ossetia–Alania. A study of environmental geophysical factors in the city of Vladikavkaz was carried out at 126 points; indicators of noise pollution, electric fields and the level of [...] Read more.
The article considers the main sources of pollutionin the territory of the Republic of North Ossetia–Alania. A study of environmental geophysical factors in the city of Vladikavkaz was carried out at 126 points; indicators of noise pollution, electric fields and the level of gamma radiation were measured. A geoaccumulation index of heavy metals in soils and indices of carcinogenic and non-carcinogenic risks were calculated and corresponding maps were constructed. The obtained data supporting a high level of carcinogenic risk are consistent with a high level of cancer morbidity in the city, which indicates a close relationship between morbidity and the carcinogenic risk index. It has been determined that emissions from road transport are greater by an order of magnitude than stationary sources emissions, while there is a steady trend towards an increase in air pollution as a result of the increasing negative impact of motor vehicle emissions. It has been established that the most hazardous way for heavy metals to enter the human body from the soil is by inhalation. It has been determined that in areas where environmental pollution with heavy metals is higher, cancer morbidity is also higher. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
GIS-Based Planning and Web/3D Web GIS Applications for the Analysis and Management of MV/LV Electrical Networks (A Case Study in Tunisia)
Appl. Sci. 2022, 12(5), 2554; https://doi.org/10.3390/app12052554 - 28 Feb 2022
Cited by 2 | Viewed by 1423
Abstract
Geographic Information Systems (GISs) have an essential part to play in the management and planning of electricity distribution. Since the management of electricity network data was previously conducted in Tunisia based on paper maps and plans, the purpose of this study is to [...] Read more.
Geographic Information Systems (GISs) have an essential part to play in the management and planning of electricity distribution. Since the management of electricity network data was previously conducted in Tunisia based on paper maps and plans, the purpose of this study is to present a case for the planning of an MV/LV (Medium Voltage/Low Voltage) electrical network in the region of Medjez El Bab (North-West of Tunisia), based on GIS, Web, and 3D Web GIS, to create an intelligent electricity network, which will be a decision-making tool. Analyses of vehicle transport and pedestrian accessibility between installations and a generation of Origin-Destination cost matrix to calculate the average transport distances between the service points were conducted. Moreover, an analysis of the network’s impedance allowed carrying out different scenarios to optimize performance and could obtain more efficient routes. The different analyses carried out were crucial for the maintenance of the electrical network and for future urban planning. A 3D virtual city has been developed to visualize graphical and attribute data for the study area. Web and 3D Web GIS applications that allow the publication of interactive maps on the Web as well as database information have been developed to offer users the possibility of consulting produced products by using the internet. A website related to the study was equally developed to gather the different obtained results. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Experiment Study on Mechanical Evolution Characteristics of Coal and Rock under Three-Dimensional Triaxial Stress
Appl. Sci. 2022, 12(5), 2445; https://doi.org/10.3390/app12052445 - 26 Feb 2022
Cited by 2 | Viewed by 533
Abstract
The surrounding rock is in a complex stress environment and its mechanical behavior is also complex, especially after the excavation of the coal seam, the phenomenon of stress release of surrounding rock often occurs. The vertical stress and horizontal stress of the surrounding [...] Read more.
The surrounding rock is in a complex stress environment and its mechanical behavior is also complex, especially after the excavation of the coal seam, the phenomenon of stress release of surrounding rock often occurs. The vertical stress and horizontal stress of the surrounding rock mass will have a series of complex changes. In underground engineering, rock mass is affected by dead weight pressure and tectonic stress. With coal mine production, the original stress of surrounding rock is demolished, and the destruction of surrounding rock is reflected in the loading and unloading failure of three-dimensional stress. Aiming at the phenomenon, this paper takes the Pingshuo East open-pit mine as the research background, and the experiments on physical and mechanical parameters of coal and rock mass was carried out, obtaining the coal and rock mechanics parameters, such as elastic modulus, Poisson’s ratio, internal friction angle, cohesive force, etc. The stress strain curve was created based on the conventional triaxial experiment of coal and rock under different confining pressure conditions. According to the characteristics of these curves, we obtain underground engineering rock mass unloading stress–strain variation characteristics. Through establishing a stress–strain equation based on confining pressure, we finally describe the mechanical failure characteristics of rock under triaxial stress. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste
Appl. Sci. 2022, 12(4), 2010; https://doi.org/10.3390/app12042010 - 15 Feb 2022
Cited by 1 | Viewed by 505
Abstract
In this study, we developed a new approach for feature engineering in geosciences. The main focus of this study was feature engineering based on the implementation of the dynamic activity index (MDAI) as a function of the anomaly of the spatial distribution of [...] Read more.
In this study, we developed a new approach for feature engineering in geosciences. The main focus of this study was feature engineering based on the implementation of the dynamic activity index (MDAI) as a function of the anomaly of the spatial distribution of data, using systems and discrete mathematical analysis. The methodology for calculating MDAI by groups, geomorphological variability, the density of tectonic faults, stress-strain state, and magnetic field anomalies, is presented herein for a specific area. A detailed analysis of the correlation matrix of MDAI revealed weak correlations between the development features. This showed that the considered properties of the geological environment are independent sets and can be used in the analysis of its geodynamic stability. As a result, it was found that most of the territory where high-level radioactive waste (HLRW) disposal is currently planned is in a relatively stable zone. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Assessment of the Influence of Astronomical Cyclicity on Sedimentation Processes in the Eastern Paratethys Based on Paleomagnetic Measurements Using Discrete Mathematical Analysis
Appl. Sci. 2022, 12(2), 580; https://doi.org/10.3390/app12020580 - 07 Jan 2022
Cited by 1 | Viewed by 372
Abstract
The introduction of modern methods for the mathematical processing of geological data is one of the promising areas of study and development in the field of geosciences. For example, today mathematical geology makes it possible to reliably identify astronomical cycles by measuring the [...] Read more.
The introduction of modern methods for the mathematical processing of geological data is one of the promising areas of study and development in the field of geosciences. For example, today mathematical geology makes it possible to reliably identify astronomical cycles by measuring the scalar magnetic parameters of rocks (magnetic susceptibility). The main aim of this study is to develop a mathematical tool for identifying stable oscillation cycles (periods) in the dataset of the magnetic susceptibility of rocks in a geological section. The author’s method (algorithm) is based on the concept of discrete mathematical analysis—an innovative mathematical approach to the analysis of discrete geological and geophysical data. Its reliability is also demonstrated, by comparison with the results obtained by classical methods: Fourier analysis, Lomb periodogram, and REDFIT. The proposed algorithm was applied by the authors to analyze the material of field geological studies of the Zhelezny Rog section (Taman Peninsula). As a result, stable cycles were determined for the Pontian and Lower Maeotian sedimentary strata of the Black Sea Basin (Paratethys). Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Theoretical Framework for Determination of Linear Structures in Multidimensional Geodynamic Data Arrays
Appl. Sci. 2021, 11(24), 11606; https://doi.org/10.3390/app112411606 - 07 Dec 2021
Cited by 2 | Viewed by 837
Abstract
The article addresses the issue of clustering of multidimensional data arrays with a noise using the methods of discrete mathematical analysis (DMA clustering). The theory of DMA clustering through the logical densities calculus is detailed, and the new algorithm Linear Discrete Perfect Sets [...] Read more.
The article addresses the issue of clustering of multidimensional data arrays with a noise using the methods of discrete mathematical analysis (DMA clustering). The theory of DMA clustering through the logical densities calculus is detailed, and the new algorithm Linear Discrete Perfect Sets (LDPS) is described. The main objective of the LDPS algorithm is to identify linearly stretched anomalies in a multidimensional array of geo-spatial data (geophysical fields, geochemistry, satellite images, local topography, maps of recent crustal movements, seismic monitoring data, etc.). These types of anomalies are associated with tectonic structures in the upper part of the Earth’s crust and pose the biggest threat for integrity of the isolation properties of the geological environment, including in regions of high-level radioactive waste disposal. The main advantage of the LDPS algorithm as compared to other cluster analysis algorithms that may be used in arrays with a noise is that it is more focused on searching for clusters that are linear. The LDPS algorithm can apply not only in the analysis of spatial natural objects and fields but also to elongated lineament structures. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Use of 2SFCA Method to Identify and Analyze Spatial Access Disparities to Healthcare in Jeddah, Saudi Arabia
Appl. Sci. 2021, 11(20), 9537; https://doi.org/10.3390/app11209537 - 14 Oct 2021
Viewed by 945
Abstract
The issue of reducing spatial disparities in access to healthcare is one of the most important healthcare planning issues that policy makers and planners investigate and consider as a key focus until present time. A healthcare system that meets the requirements of availability [...] Read more.
The issue of reducing spatial disparities in access to healthcare is one of the most important healthcare planning issues that policy makers and planners investigate and consider as a key focus until present time. A healthcare system that meets the requirements of availability and affordability will be useless if the spatial accessibility to healthcare is not provided to all equally. Therefore, this study aims to identify and analyze spatial disparities in access to healthcare centers in Jeddah, Saudi Arabia. The two-step floating catchment area (2SFCA) method was used to measure spatial accessibility of healthcare centers based on the travel time threshold (i.e., 30-min drive time in this study). The GIS technology was used to execute the 2SFCA method. A geodatabase, which includes the population districts, locations of healthcare centers, and road network, was created. Some procedures were performed within the road network database to set the travel time that is considered as an essential step to compute the origin–destination (OD) cost matrix. The OD matrix was later used as the source for calculating provider-to-population ratios and the spatial accessibility scores for population districts. The results of the study revealed spatial disparities in access to healthcare centers in Jeddah city. The majority of the Jeddah population (i.e., 97.51%) have accessibility to healthcare centers, but with disparate levels. The central districts have a higher access score compared to the rest of the city’s districts. Most districts that do not have accessibility to healthcare centers are concentrated in the southeast of the city. The results can help local health planners improve spatial equity in access to healthcare centers through giving the less-served districts a priority when allocating future healthcare centers in Jeddah city. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Keyboard Model of Seismic Cycle of Great Earthquakes in Subduction Zones: Simulation Results and Further Generalization
Appl. Sci. 2021, 11(19), 9350; https://doi.org/10.3390/app11199350 - 08 Oct 2021
Cited by 3 | Viewed by 623
Abstract
Catastrophic megaearthquakes (M > 8) occurring in the subduction zones are among the most devastating hazards on the planet. In this paper we discuss the seismic cycles of the megathrust earthquakes and propose a blockwise geomechanical model explaining certain features of the stress-deformation [...] Read more.
Catastrophic megaearthquakes (M > 8) occurring in the subduction zones are among the most devastating hazards on the planet. In this paper we discuss the seismic cycles of the megathrust earthquakes and propose a blockwise geomechanical model explaining certain features of the stress-deformation cycle revealed in recent decades from seismological and satellite geodesy (GNSS) observations. Starting with an overview of the so-called keyboard model of the seismic cycle by L. Lobkovsky, we outline mathematical formalism describing the motion of seismogenic block system assuming viscous rheology beneath and between the neighboring elastic blocks sitting on top of the subducting slab. By summarizing the GNSS-based evidence from our previous studies concerning the transient motions associated with the 2006–2007 Simushir earthquakes, 2010 Maule earthquake, and 2011 Tohoku earthquake, we demonstrate that those data support the keyboard model and reveal specific effect of the postseismic oceanward motion. However, since the seismogenic blocks in subduction systems are mostly located offshore, the direct analysis of GNSS-measured displacements and velocities is hardly possible in terms of the original keyboard model. Hence, the generalized two-segment keyboard model is introduced, containing both frontal offshore blocks and rear onshore blocks, which allows for direct interpretation of the onshore-collected GNSS data. We present a numerical computation scheme and a series of simulated data, which exhibits the consistency with measured motions and enables estimating the seismic cycle characteristics, important for the long-term earthquake forecasting. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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Article
Deep Contrast Learning Approach for Address Semantic Matching
Appl. Sci. 2021, 11(16), 7608; https://doi.org/10.3390/app11167608 - 19 Aug 2021
Cited by 3 | Viewed by 870
Abstract
Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a [...] Read more.
Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines. Full article
(This article belongs to the Special Issue Geoinformatics and Data Mining in Earth Sciences)
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