Mapping of landscape-ecological situation of the mining area within the Patom upland

2017 ◽  
Vol 928 (10) ◽  
pp. 10-18
Author(s):  
S.A. Sedykh
Keyword(s):  
Large Scale ◽  
Temporal Distribution ◽  
Mining Area ◽  
Distribution Analysis ◽  
Ecological Situation ◽  
Software Environment ◽  
Thematic Map ◽  
Main Layer ◽  
Geological Processes ◽  
Landscape Ecological

The article presents the results of a landscape-ecological study with mapping of the territory in the central part of the Patom upland. Research polygon is located within the Kropotkinsky mining unit of Baikal region, has an area of 600 km2. Technogenic disruption has a wide spatial and temporal distribution. Analysis of a complex ecological situation requires a rational assessment and adequate mapping. For this, modern geoinformation-cartographic, remote methods and a geosystemic approach were used. A large-scale thematic map was created, which includes 250 polygons in the main layer, which belong to 26 types of landscapes. A detailed description of the landscapes is given in the map legend. There are indicated the belonging to the landscape structures of regional and continental dimension, types and degree of disturbance. Additional vector layers are selected for displaying exogenetic geological processes, linear disturbance, geochemical anomalies. Mapsemiotic method was used for the visualization and formalization of the final thematic map. It allows to make full use of the integrated capabilities of the syntax of the map symbols and their semantic value in the software environment.

scholarly journals Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering

2020 ◽  
Vol 12 (18) ◽  
pp. 3069
Author(s):  
Yan Ma ◽  
Caihong Ma ◽  
Peng Liu ◽  
Jin Yang ◽  
Yuzhu Wang ◽  
...  
Keyword(s):  
United States ◽  
Time Series ◽  
Data Clustering ◽  
Large Scale ◽  
Temporal Distribution ◽  
The United States ◽  
Distribution Analysis ◽  
Heat Sources ◽  
Long Time Series ◽  
Long Time

Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO2 emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach.

The satellite radar monitoring for anthropogenic and natural geological hazards mapping within the Solotvyno mining area (Transcarpathia, Ukraine)

2021 ◽  
Author(s):  
Maxim Pakshin ◽  
Stella Shekhunova ◽  
Svitlana Stadnichenko ◽  
Ivan Liaska
Keyword(s):  
Land Surface ◽  
Mining Area ◽  
Salt Dome ◽  
Ecological Situation ◽  
Surface Deformations ◽  
Geological Processes ◽  
Vertical Displacements ◽  
Dome Structure ◽  
Satellite Radar ◽  
Radar Monitoring

<p>Satellite images have been interpreted to establish the basic patterns of land surface deformations and predict the development of hazardous geological processes within the Solotvyno salt dome structure and the adjacent territories (Transcarpathia, Ukraine). Solotvyno rock salt deposit is one of the largest in Ukraine. Geotechnical and hydro-geological problems at the deposit have started to accumulate since the mid-90s and have led to a dangerous environmental technogenic situation that was given the national emergency status in 2010. This multi-hazard geo-ecological situation is a result of overlapping both anthropogenic “post-mining” (karst, subsidence, sinkhole formation, ground surface collapses, mine flooding, slope mass movements) and natural (flooding, landslides, etc.) hazardous geological processes, in particular, the disturbance of land surface, which is the sign of the uncontrolled development of salt karst, flooding and could result in transboundary pollution of the Tysa River, etc.</p><p>To forecast the development of hazardous geological processes, monitoring was implemented by using innovative techniques for processing satellite radar data, such as “Persistent Scatterers (PS)”, “Small Baseline Subset (SBAS)”. Due to the use of long-time series of images obtained with a synthetic aperture radar (SAR), the errors of orbital data and the effects of atmospheric phenomena have been effectively suppressed. The results of processing are digital maps, with the accuracy of evaluating the average vertical displacement rate of the objects being 2–4 mm/year when using the “PS” technique and 6–15 mm/year if using the “SBAS”.</p><p>A highly accurate evaluation of the vertical displacements of objects and land surface has been carried out using interferometric processing of satellite radar monitoring data by means of new satellite constellations including Sentinel-1A and 1B (DInSAR analysis data for 2016–2020, SBAS approach, Copernicus EMSN-030, EMSN-064; PS+SBAS approach, Center of the Special Information Receiving and Processing and the Navigating Field Control, Ukraine). The research area was 33 sq. km. Information end products (raster and vector) have been created, which permitted the changes in spatial and temporal dimensions to be analyzed. The values and areas of concentrated land surface deformations have been determined within the zone of anthropogenic and natural karst development. The areas of land surface subsidence with the average rate of vertical displacements from -6 to -94 mm/year have been digitized using GIS tools.</p><p>The assessment of anthropogenic hazards for the Solotvyno salt dome structure and adjacent territories has been provided. It has been determined that mines No7, 8, and 9 pose an anthropogenic threat to the safety of Solotvyno community inhabitants.</p><p>The reconstruction of land surface vertical displacements in time has been carried out within the studies performed. In order to ensure life safety in Solotvyno, the results will be used in territory development and in setting up the system of monitoring. In view of the complicated geo-ecological situation, the development and functioning of a permanent geo-ecological monitoring system for the Solotvyno mining area and the adjacent territories is the top-priority objective.</p><p>The research has been carried out with the EU financial support: projects REVITAL 1 (HURSKOVA/1702/6.1/0072) and ImProDiReT (No. 783232).</p>

scholarly journals Density Distribution Maps: A Novel Tool for Subcellular Distribution Analysis and Quantitative Biomedical Imaging

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1009
Author(s):  
Ilaria De Santis ◽  
Michele Zanoni ◽  
Chiara Arienti ◽  
Alessandro Bevilacqua ◽  
Anna Tesei
Keyword(s):  
Density Distribution ◽  
Large Scale ◽  
Super Resolution ◽  
Spatial Location ◽  
Relative Displacement ◽  
Distribution Analysis ◽  
Laboratory Equipment ◽  
Imaging Device ◽  
Distribution Maps ◽  
Super Resolution Microscopy

Subcellular spatial location is an essential descriptor of molecules biological function. Presently, super-resolution microscopy techniques enable quantification of subcellular objects distribution in fluorescence images, but they rely on instrumentation, tools and expertise not constituting a default for most of laboratories. We propose a method that allows resolving subcellular structures location by reinforcing each single pixel position with the information from surroundings. Although designed for entry-level laboratory equipment with common resolution powers, our method is independent from imaging device resolution, and thus can benefit also super-resolution microscopy. The approach permits to generate density distribution maps (DDMs) informative of both objects’ absolute location and self-relative displacement, thus practically reducing location uncertainty and increasing the accuracy of signal mapping. This work proves the capability of the DDMs to: (a) improve the informativeness of spatial distributions; (b) empower subcellular molecules distributions analysis; (c) extend their applicability beyond mere spatial object mapping. Finally, the possibility of enhancing or even disclosing latent distributions can concretely speed-up routine, large-scale and follow-up experiments, besides representing a benefit for all spatial distribution studies, independently of the image acquisition resolution. DDMaker, a Software endowed with a user-friendly Graphical User Interface (GUI), is also provided to support users in DDMs creation.

Wavelet analysis of the temporal-spatial distribution in the Eurasia seismic belt

2017 ◽  
Vol 15 (03) ◽  
pp. 1750018 ◽  
Author(s):  
Wenfeng Zheng ◽  
Xiaolu Li ◽  
Lirong Yin ◽  
Zhengtong Yin ◽  
Bo Yang ◽  
...  
Keyword(s):  
Fault Zone ◽  
Compact Support ◽  
Large Scale ◽  
Temporal Distribution ◽  
Seismic Energy ◽  
Research Area ◽  
Earthquake Activity ◽  
Seismic Zone ◽  
Seismic Belt ◽  
Earthquake Fault

Due to the growing frequency of earthquakes, safeties of human lives and properties are facing serious threats. However, the research in the field of spatial-temporal distribution of earthquake is quite a few. In this paper, we use wavelet model to analyze the spatial-temporal distribution of earthquakes. Because the spatial-temporal distribution of earthquake activity is closely related to the distribution of the earthquake fault zone, we analyze large-scale earthquake clusters by selecting the Eurasia seismic belt and the surrounding region as the research area. From the perspective of the time domain, the results show that the seismic energy of the earthquake fault zone presences compact support or similar compact support distribution, suggesting that the seismic zone exists a relatively quiet period and active stage. This indicate that the seismic zone is periodical. The period of strong earthquakes above normal and less than normal is different by time changes. The cycles of earthquakes are different due to different regions and different geological and geographical environment.

scholarly journals Large Evaporite Provinces: Geothermal rather than Solar Origin?

2020 ◽  
Author(s):  
Zhanjie Qin ◽  
Chunan Tang ◽  
Xiying Zhang ◽  
Tiantian Chen ◽  
Xiangjun Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Climatic Factors ◽  
Temporal Distribution ◽  
Tectonic Activity ◽  
Driving Mechanism ◽  
Solar Origin ◽  
Tectonically Active ◽  
Spatio Temporal ◽  
Thermal Influence ◽  
The Masses

Abstract Large evaporite provinces (LEPs) represent prodigious volumes of evaporites widely developed from the Sinian to Neogene. The reasons why they often quickly develop on a large scale with large areas and thicknesses remain enigmatic. Possible causes range from warming from above to heating from below. The fact that the salt deposits in most salt-bearing basins occur mainly in the Sinian-Cambrian, Permian-Triassic, Jurassic-Cretaceous, and Miocene intervals favours a dominantly tectonic origin rather than a solar driving mechanism. Here, we analysed the spatio-temporal distribution of evaporites based on 138 evaporitic basins and found that throughout the Phanerozoiceon, LEPs occurred across the Earth’s surface in most salt-bearing basins, especially in areas with an evolutionary history of strong tectonic activity. The masses of evaporites, rates of evaporite formation, tectonic movements, and large igneous provinces (LIPs) synergistically developed in the Sinian-Cambrian, Permian, Jurassic-Cretaceous, and Miocene intervals, which are considered to be four of the warmest times since the Sinian. We realize that salt accumulation can proceed without solar energy and can generally be linked to geothermal changes in tectonically active zones. When climatic factors are involved, they may be manifestations of the thermal influence of the crust on the surface.

Analisys of mud inrush water sources into underground working of the Sokolovskoe ore deposit

2020 ◽  
pp. 56-67
Author(s):  
E. Yu. Efremov
Keyword(s):  
Hydraulic Conductivity ◽  
Large Scale ◽  
Underground Mining ◽  
Temporal Distribution ◽  
High Water ◽  
The North ◽  
Initial Block ◽  
Groundwater Supply ◽  
Spatio Temporal ◽  
Inrush Water

There is a serious threat of groundwater inrush from overlying sedimentary layers for underground mining. When ore is extracted using block caving method, the area of overburden collapse over ore zone disrupts the natural structure of high hydraulic-conductivity and low hydraulic-conductivity layers. This process creates conditions for the accumulation and transfer of groundwater to mine workings, which lead to accidents, up to disastrous proportions. The research aim is to determine the spatio-temporal distribution of mud inrushes, and to identify groundwater supply sources of inrushes to reduce the geotechnical risks of underground mining in Sokolovskaya mine. Research methods include localization, classification, and analysis of monitoring data, comparison of mud inrushes distribution with geostatistical parameters of the main aquifers.The majority of large-scale accidents caused by mud inrushes are confined to the central and northern area of caved rock zone. The most risky stage of the ore body extraction is the initial block at the lower extraction level. The sources of water supply for the majority of the mud inrushes are high water level areas of the Cretaceous aquifer to the north and west of the mine. Rational targeted drainage aimed at draining the identified areas of the aquifer is the best way to reduce the risk of accidents.

Cloud in Science

2014 ◽  
pp. 165-191
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Computing ◽  
Scientific Discovery ◽  
Software Environment ◽  
Access To Resources ◽  
On Demand ◽  
Service Models ◽  
New Models ◽  
The Impact

Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources as well as the ability to control the software environment. Scientific computing researchers and resource providers servicing these users are considering the impact of new models and technologies. SaaS solutions like Globus Online and IaaS solutions such as Nimbus Infrastructure and OpenNebula accelerate the discovery of science by helping scientists to conduct advanced and large-scale science. This chapter describes how cloud is helping researchers to accelerate scientific discovery by transforming manual and difficult tasks into the cloud.

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