FCAZ-recognition based on declustered earthquake catalogs

Дзебоев, Б. А.; Karapetyan, Jon; Aronov, G.; Dzeranov, Boris V.; Kudin, Dmitry; Karapetyan, Roza; Vavilin, E. V.

doi:10.2205/2020es000754

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…DPS-series algorithms are actively applied in many geological and geophysical studies (analysis of seismic catalogs, search for signals on geophysical records, in the problem of radioactive waste disposal, etc.) [1,4,5,7,[11][12][13]. It seems that the new version of DPS developed in the present paper will make it possible to improve this application.…”

Section: Discussionmentioning

confidence: 90%

“…Despite generally good results and efficient applications [4,5,[11][12][13], the DPS-algorithm in its current version has drawbacks: at the first stage, the cutting X ⇒ X(α) is not always thorough and of high quality; at the second stage, the partitioning of X(α) into r-connectivity components is too small and detailed due to the locality of the radius r. A proper partition of X(α) must come from the global view of X(α) induced by the entire space X.…”

Section: Discussionmentioning

confidence: 99%

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DPS Clustering: New Results

et al. 2022

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The results presented in this paper are obtained as part of the continued development and research of clustering algorithms based on the discrete mathematical analysis. The article briefly describes the theory of Discrete Perfect Sets (DPS-sets) that is the basis for the construction of DPS-clustering algorithms. The main task of the previously constructed DPS-algorithms is to search for clusters in multidimensional arrays with noise. DPS-algorithms have two stages: the first stage is the recognition of the maximum perfect set of a given density level from the initial array, the second stage is the partitioning of the result of the first stage into connected components, which are considered to be clusters. Study of qualities of DPS-algorithms showed that, in a number of situations in the first stage, the result does not include all clusters which have practical sense. In the second stage, partitioning into connected components can lead to unnecessarily small clusters. Simple variation of parameters in DPS-algorithms does not allow for eliminating these drawbacks. The present paper is devoted to the construction on the basis of DPS-algorithms of their new versions, more free from these drawbacks.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

DPS Clustering: New Results

et al. 2022

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“…The feature of a study object is the result of measuring or modeling some individual property of the object, and their aggregate reflects the model of the object. The solution to urgent problems in assessing natural and man-made risks, such as searching for anomalies in geophysical fields [1][2][3], recognizing strong earthquake-prone areas [4][5][6][7][8], geodynamic zoning [9,10], etc., requires the creation of effective methods for the formalized analysis of a complex of geological and geophysical features. When analyzing the data, the features are synthesized using mathematical modeling methods [11][12][13] and may contain complex mathematical constructions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste

et al. 2022

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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.

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“…The available geospatial data arrays are almost always insufficient, uncertain and distorted due to the noise, which dictates the need for developing effective analysis and interpretation algorithms [3][4][5]. This issue is resolved in the article within the framework of discrete mathematical analysis (DMA), an original data analysis approach developed at the Geophysical Center of the Russian Academy of Sciences.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Framework for Determination of Linear Structures in Multidimensional Geodynamic Data Arrays

et al. 2021

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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.

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FCAZ-recognition based on declustered earthquake catalogs

Cited by 11 publications

References 25 publications

DPS Clustering: New Results

DPS Clustering: New Results

Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste

Theoretical Framework for Determination of Linear Structures in Multidimensional Geodynamic Data Arrays

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