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Estimating Subsurface Lithology Distribution of Pannonian Sediments in Eastern Part of Drava Depression by Geomathematical Methods (CROSBI ID 685465)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | domaća recenzija

Kamenski, Ana ; Cvetković, Marko Estimating Subsurface Lithology Distribution of Pannonian Sediments in Eastern Part of Drava Depression by Geomathematical Methods // Knjiga sažetaka / Abstracts Book / Horvat, Marija ; Matoš, Bojan ; Wacha, Lara (ur.). Zagreb: Hrvatski geološki institut, 2019. str. 96-97

Podaci o odgovornosti

Kamenski, Ana ; Cvetković, Marko

engleski

Estimating Subsurface Lithology Distribution of Pannonian Sediments in Eastern Part of Drava Depression by Geomathematical Methods

One of the key elements in regional geological subsurface exploration is a valid estimate of lithology distribution. The conventional way is to use different mapping algorithms to determine the properties in the interwell area based solely on well data or using exploration seismics (attribute analysis ; RADOVICH & OLIVEROS, 1998) to reduce uncertainty. Lithological properties of rocks in the subsurface are commonly estimated based on well data using either conventional deterministic approach or stochastic algorithms with previously expressed variograms, respecting the already established contacts between the lithostratigraphic units. Since the uncertainty, spatial and temporal variability cannot be avoided, the aim of this research is to estimate the lithological composition of the rocks in the area between wells, as realistically as possible. Variogram, as a starting point, is a basic term of geomathematical analysis which represents random field structure and it can only depend on the distance between the measured points and on the difference in values between them (ANDRIČEVIĆ et al., 2006). In addition to the well data interpretation, an important role in determining the lithological composition has also recently been given to the application of seismic attribute analysis (KOSON et al., 2014, PIGOT et al., 2013). Artificial neural networks are also used in evaluating the lithological composition (BRCKOVIĆ et al., 2017). In this relatively new method of data processing an algorithm is expected to learn from a set of available data and adapting to new conditions, functioning in the way the biological neural networks do (RUMELHART et al., 1986). The main characteristics of artificial neural networks are exploiting unclear and incomplete data, good nonlinear evaluation of sample relationships, using a large number of different parameters and acquiring new knowledge through the learning process from previous experiences. By using the mentioned geomathematical tools, analysis was performed on well data (Figure 1) from Pannonian sediments in eastern part of Drava Depression. These data includes categorical (lithology categories) and continuous variables (seismic attributes). Since all of this is aimed at better reconstruction of subsurface geology, the main goal of the research is to develop a methodology that will eventually merge well and seismic data with analyses made by artificial neural networks, in order to obtain a more realistic characterization of the lithological composition of clastic sediments in the area between wells, to enable regionally extensive reconstructions.

lithological composition, stochastic approach, variogram, artificial neural networks

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Podaci o prilogu

96-97.

2019.

objavljeno

Podaci o matičnoj publikaciji

Knjiga sažetaka / Abstracts Book

Horvat, Marija ; Matoš, Bojan ; Wacha, Lara

Zagreb: Hrvatski geološki institut

1849-7713

Podaci o skupu

6. hrvatski geološki kongres s međunarodnim sudjelovanjem

poster

06.10.2019-12.10.2019

Zagreb, Hrvatska

Povezanost rada

Matematika, Geologija

Poveznice