Reprint
Geoinformatics and Data Mining in Earth Sciences
Edited by
August 2024
468 pages
- ISBN978-3-7258-1867-9 (Hardback)
- ISBN978-3-7258-1868-6 (PDF)
This is a Reprint of the Special Issue Geoinformatics and Data Mining in Earth Sciences that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
This reprint presents a collection of articles on recent advances in data mining, systems analysis, geoinformatics, Big Data theory and practice, and GIS systems with applications for a wide range of Earth Science disciplines. It brings together state-of-the-art results in scientific areas such as seismic hazard and seismic risk, geodynamics and tectonics, Earth gravity, geoecology, GNSS applications, climate change, radioactive waste disposal, spatial disparities, mining surveys, GIS logistics, and other Earth science disciplines, including interdisciplinary ones.
Format
- Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
address matching; smart city; contrast learning; neural networks; data augmentation; keyboard model; seismic cycle; subduction zone; fault-block structure; geomechanical model; earthquake generation; healthcare centers; 2SFCA method; spatial accessibility; travel time threshold; GIS; OD cost matrix; finite metric space; density; solidity; clusters; discrete perfect sets; linear structures; system analysis; DMA algorithms; dynamic activity index; structural tectonic block; geodynamic data; safety; complex stress environment; open-pit coal mine; failure mechanism; coal petrography; coal petrography; surrounding rock; MV/LV network; GIS-based planning; spatial/network analysis; 3D virtual city; Web/3D Web GIS; ecology; environmental pollution; mining; spatial databases; GIS technologies; natural hazards; landslides; mudflows; avalanches; zonation; geophysical data; geomorphological conditions; microstructural analysis; GIS technology; image analysis; image filtering; microfracture mapping; porosity; permeability; Primorsky Fault; tectonite; Baikal Rift Zone; merging catalogs; earthquake; clustering algorithm; Arctic region; magnitude unification; duplicate events; spatial data mining; fuzzy co-location pattern; interval type-2 fuzzy set; clique; GPS tremor; entropy; spectral index; probability density; correlations; big data; source observations; seismological data; data management; synthetics seismograms; regional data; semantic address matching; deep transfer learning; pretraining model; fine tuning; Arctic zone of the Russian Federation; earthquake-prone areas; system-analytical method; FCAZ; pattern recognition; clustering; integrated earthquake catalogs; high seismicity zones; Russian Arctic; railway network development; Northern Latitudinal Railway; climate change; MERRA-2 reanalysis; digital atlas; geoinformatics; merging catalogs; earthquake; Russian Arctic; magnitude unification; duplicate events; seismic networks; algorithms; data adjustment; data integration; integrated geodesy; structural monitoring; GPS tremor singular points; entropy; spectral slope; probability density; principal components; Arctic; Gakkel Ridge; Knipovich Ridge; Svalbard Archipelago; merging earthquake catalogs; magnitude unification; level of registration; rapid visual screening; machine learning; SHAP; recalibration; seismic vulnerability