93 research outputs found
ΠΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΠΠΎΠ³ΡΡΠ°Π½ΡΠΊΠΎΠ³ΠΎ Π²ΠΎΠ΄ΠΎΡ ΡΠ°Π½ΠΈΠ»ΠΈΡΠ°
ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ ΡΡΡΠ΅ΠΊΡΠ° ΠΠ°ΡΡΠΈΠ½Π³Π΅ΡΠ° Ρ ΡΠΎΡΡΠΎΠΌ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΏΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
ΠΠΎΡΠΌΠΎΡΡΡΡΠΊΡΡΡΠ½ΡΠ΅ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ Π·ΠΎΠ»ΠΎΡΠΎΡΡΠ΄Π½ΡΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π·Π°Π°Π½Π³Π°ΡΡΠΊΠΎΠΉ ΡΠ°ΡΡΠΈ ΠΠ½ΠΈΡΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΊΡΡΠΆΠ°
ΠΠ·ΡΡΠ΅Π½Ρ ΠΊΠΎΡΠΌΠΎΡΡΡΡΠΊΡΡΡΡ Π·Π°Π°Π½Π³Π°ΡΡΠΊΠΈΠΉ ΡΠ°ΡΡΠΈ ΠΠ½ΠΈΡΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΊΡΡΠΆΠ° ΠΏΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π°ΠΌ ΠΌΡΠ»ΡΡΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ Modis ΠΈ Landsat ETM+. ΠΡΠ΄Π΅Π»Π΅Π½Ρ ΡΠ΅ΡΡΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠ»ΡΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ°, ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΡΠ΅ΠΌΡΠ΅ ΠΊΠ°ΠΊ Π³Π»ΡΠ±ΠΈΠ½Π½ΡΠ΅ ΠΎΡΠ°Π³ΠΈ Π³ΡΠ°Π½ΠΈΡΠΈΠ·Π°ΡΠΈΠΈ. ΠΠΎΠΊΠ°Π·Π°Π½Ρ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π·ΠΎΠ»ΠΎΡΠΎΠ³ΠΎ ΠΎΡΡΠ΄Π΅Π½Π΅Π½ΠΈΡ Π² ΠΊΠΎΡΠΌΠΎΠ³Π΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΡΠΊΡΡΡΠ°Ρ
. ΠΡΠ΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΡΠ°Π·Π½ΠΎΡΠ°Π½Π³ΠΎΠ²ΡΠ΅ ΠΊΠΎΡΠΌΠΎΠ³Π΅ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡΡΡΠΊΡΡΡΡ Π½Π°Ρ
ΠΎΠ΄ΡΡ ΠΎΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π² Π°Π½ΠΎΠΌΠ°Π»ΡΠ½ΡΡ
ΡΡΡΡΠΊΡΡΡΠ°Ρ
Π³Π΅ΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ»Π΅ΠΉ
ΠΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΈΠ±ΡΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ , ΡΠ΅ΠΏΠ»ΠΎΠ²ΡΡ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΠΉ Π² ΡΡΡΠ±ΠΎΠΏΡΠΎΠ²ΠΎΠ΄Π΅, ΠΏΠΎΠ΄Π²Π΅ΡΠΆΠ΅Π½Π½ΠΎΠΌ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΊΠ»ΠΈΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΡΡΠ»ΠΎΠ²ΠΈΠΉ
Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology
Improved interpretation of t2 distributions from nmr relaxation measurements for a better prediction of low permeabilities
see Abstract Volum
Die Lithologie der kontinentalen Tiefbohrung KTB-HB : eine Interpretation geophysikalischer Logs (EFA-LOG 280 - 7140 m)
Die Lithologie der kontinentalen Tiefbohrung KTB-HB : eine Interpretation geophysikalischer Logs (EFA-LOG 280 - 7140 m)
Influence of depth, temperature, and structure of a crustal heat source on the geothermal reservoirs of Tuscany: numerical modelling and sensitivity study
Β© 2016, Ebigbo et al.Granitoid intrusions are the primary heat source of many deep geothermal reservoirs in Tuscany. The depth and shape of these plutons, characterised in this study by a prominent seismic reflector (the KΒ horizon), may vary significantly within the spatial scale of interest. In an exploration field, simulations reveal the mechanisms by which such a heat source influences temperature distribution. A simple analysis quantifies the sensitivity of potentially measurable indicators (i.e. vertical temperature profiles and surface heat flow) to variations in depth, temperature, and shape of the heat source within given ranges of uncertainty
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