The Study of Time Series Using the DMA Methods and Geophysical Applications

Agayan, S. M.; Богоутдинов, Ш. Р.; Soloviev, Anatoly; Сидоров, Роман

doi:10.5334/dsj-2016-016

Cited by 26 publications

(26 citation statements)

References 14 publications

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“…The FDPS algorithm efficiently searches for the bases of these elevations, which is fundamental in data analysis, in particular in time series analysis (DRAS, FC ARS algorithms etc.) [25].…”

Section: Fdps Algorithmmentioning

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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Section: Fdps Algorithmmentioning

confidence: 99%

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

et al. 2021

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“…До 2018 г. МА широко использовалась для обнаружения аномальных сигналов различного происхождения в геофизических временных рядах (см., например, [Gvishiani et al, 2014(см., например, [Gvishiani et al, , 2016aAgayan et al, 2016]). Согласно в периоды пониженной магнитной активности мера аномальности не должна превышать значений 0.4-0.5.…”

Section: применение ма для распознавания пониженной магнитной активностиunclassified

Overview of Anomality Measure Application for Estimating Geomagnetic Activity

2020

Геофиз.Исслед.

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“…The geomagnetic data modeling and analysis problems are associated with their complicated irregular structure and the presence of noises of different origin (natural and anthropogenic) [22,[24][25][26]. The geomagnetic data structure and the anomalous feature occurrence times contain essential information on nonstationary processes in NES and characterize magnetic disturbance intensity at the place of data recording.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

2022

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Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration.

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The Study of Time Series Using the DMA Methods and Geophysical Applications

Cited by 26 publications

References 14 publications

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

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

Overview of Anomality Measure Application for Estimating Geomagnetic Activity

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

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