71 research outputs found
The world's largest oil and gas hydrocarbon deposits: ROSA database and GIS project development
This article proposes the use of Big Data principles to support the future extraction of hydrocarbon resources. It starts out by assessing the possible energy-system transformations in order to shed some light on the future need for hydrocarbon resource extraction and corresponding drilling needs. The core contribution of this work is the development of a new database and the corresponding GIS (geographic information system) visualization project as basis for an analytical study of worldwide hydrocarbon occurrences and development of extraction methods. The historical period for the analytical study is from 1900 to 2000. A number of tasks had to be implemented to develop the database and include information about data collection, processing, and development of geospatial data on hydrocarbon deposits. Collecting relevant information made it possible to compile a list of hydrocarbon fields, which have served as the basis for the attribute database tables and its further filling. To develop an attribute table, the authors took into account that all accumulated data features on hydrocarbon deposits and divided them into two types: static and dynamic. Static data included the deposit parameters that do not change over time. On the other hand, dynamic data are constantly changing. Creation of a web service with advanced functionality based on the Esri Geoportal Server software platform included search by parameter presets, viewing and filtering of selected data layers using online mapping application, sorting of metadata, corresponding bibliographic information for each field and keywords accordingly. The collected and processed information by ROSA database and GIS visualization project includes more than 100 hydrocarbon fields across different countries
ΠΠ΅ΠΎΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΡ Π² ΠΡΠΊΡΠΈΠΊΠ΅ ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΈΡ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π½Π° ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ
The scientific research that has become the subject of consideration in this article is related to assessment of the influence of geophysical factors on sustainable functioning of transport systems and the system analysis of their impact on the transport infrastructure at the Arctic latitudes. The research is a new direction in the field of study of operational reliability of transport systems and scientific support for development of transport infrastructure in the Russian Arctic.The paper touches upon the issues of reliability and possible failures of technical equipment under the influence of space weather, and also discusses multifaceted problems of safety and efficiency of development of transport systems considering new data on the structure and properties of the lithosphere referring to thawing of permafrost and mineral deposits. A separate section is devoted to new information on seismic activity and seismic hazard assessment in areas of operation and promising development of the transport infrastructure of the Arctic zone of the Russian Federation (AZRF).Intellectual accounting and generalisation of the obtained interdisciplinary results together with their visualisation are provided by geoinformatics methods. The paper presents also the results of adoption of modern geodatabase management systems, of the application of modern technologies of geoportals and interactive spherical visualisations for qualitative presentation of new geophysical knowledge obtained in the course of research.ΠΠ°ΡΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΡΡΠ°Π²ΡΠΈΠ΅ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠΎΠΌ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΡ Π² ΡΡΠΎΠΉ ΡΡΠ°ΡΡΠ΅, ΡΠ²ΡΠ·Π°Π½Ρ Ρ ΠΎΡΠ΅Π½ΠΊΠΎΠΉ Π²Π»ΠΈΡΠ½ΠΈΡ Π³Π΅ΠΎΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π½Π° ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΌ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ ΠΈΡ
Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ Π½Π° ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ Π² Π°ΡΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΠΎΡΠ°Ρ
. ΠΠ½ΠΈ ΡΠ²Π»ΡΡΡΡΡ Π½ΠΎΠ²ΡΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π½Π°Π΄ΡΠΆΠ½ΠΎΡΡΠΈ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΠΡΠΊΡΠΈΠΊΠ΅.Π ΡΠ°Π±ΠΎΡΠ΅ Π·Π°ΡΡΠΎΠ½ΡΡΡ Π²ΠΎΠΏΡΠΎΡΡ Π½Π°Π΄ΡΠΆΠ½ΠΎΡΡΠΈ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΎΡΠΊΠ°Π·ΠΎΠ² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ΅Π΄ΡΡΠ² ΠΏΠΎΠ΄ Π²Π»ΠΈΡΠ½ΠΈΠ΅ΠΌ ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ³ΠΎΠ΄Ρ. Π’Π°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ Ρ ΡΡΡΡΠΎΠΌ Π½ΠΎΠ²ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎ ΡΡΡΠΎΠ΅Π½ΠΈΠΈ ΠΈ ΡΠ²ΠΎΠΉΡΡΠ²Π°Ρ
Π»ΠΈΡΠΎΡΡΠ΅ΡΡ, ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΡΠ°ΡΡΠ΅ΠΏΠ»Π΅Π½ΠΈΠ΅ΠΌ ΠΌΠ½ΠΎΠ³ΠΎΠ»Π΅ΡΠ½Π΅ΠΌΡΡΠ·Π»ΡΡ
ΠΏΠΎΡΠΎΠ΄ ΠΈ ΠΌΠ΅ΡΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΏΠΎΠ»Π΅Π·Π½ΡΡ
ΠΈΡΠΊΠΎΠΏΠ°Π΅ΠΌΡΡ
. ΠΡΠ΄Π΅Π»ΡΠ½ΡΠΉ ΡΠ°Π·Π΄Π΅Π» ΠΏΠΎΡΠ²ΡΡΡΠ½ Π½ΠΎΠ²ΡΠΌ ΡΠ²Π΅Π΄Π΅Π½ΠΈΡΠΌ ΠΎ ΡΠ΅ΠΉΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΠ΅ΠΉΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ Π² ΡΠ°ΠΉΠΎΠ½Π°Ρ
ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ ΠΡΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·ΠΎΠ½Ρ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ (ΠΠΠ Π€).ΠΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ ΡΡΡΡ, ΠΎΠ±ΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΡΡ
ΠΌΠ΅ΠΆΠ΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈ ΠΈΡ
Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ Π³Π΅ΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ. Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π±Π°Π·Π°ΠΌΠΈ Π³Π΅ΠΎΠ΄Π°Π½Π½ΡΡ
, ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π³Π΅ΠΎΠΏΠΎΡΡΠ°Π»ΠΎΠ² ΠΈ ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΠ²Π½ΡΡ
ΡΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΉ Π΄Π»Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π½ΠΎΠ²ΡΡ
Π³Π΅ΠΎΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π½Π°Π½ΠΈΠΉ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π² Ρ
ΠΎΠ΄Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ
CODATA and global challenges in data-driven science
This synthesis report presents the scientific results of the international conference "Global Challenges and Data-Driven Science" which took place in St. Petersburg, Russian Federation from 8 October to 13 October 2017. This event facilitated multidisciplinary scientific dialogue between leading scientists, data managers and experts, as well as Big Data researchers of various fields of knowledge. The St. Petersburg conference covered a wide range of topics related to data science. It featured discussions covering the collection and processing of large amounts of data, the implementation of system analysis methods into data science, machine learning, data mining, pattern recognition, decision-making robotics and algorithms of artificial intelligence. The conference was an outstanding event in the field of scientific diplomacy and brought together more than 150 participants from 35 countries. It's success ensured the effective data science dialog between nations and continents and established a new platform for future collaboration
Formalized clustering and significant earthquake-prone areas in the Crimean Peninsula and Northwest Caucasus
Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, {\itshape {M}} 6.0. Variable EPA method
Successive recognition of significant and strong earthquake-prone areas: The BaikalβTransbaikal region
FCaZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts
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