scholarly journals Comparison of Ordinary and Universal Kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Šandrovac Field

2016 ◽  
Vol 31 (2) ◽  
pp. 41-58
Author(s):  
Ivana Mesić Kiš
Keyword(s):  
Input Data ◽  
Universal Kriging ◽  
Kriging Interpolation ◽  
Geological Data ◽  
Geological Structures ◽  
Spatial Trend ◽  
Common Trend ◽  
Cardinal Direction ◽  
Surface Spread

Universal Kriging has not until now been used for mapping of geological data in Croatia. However, it is one of the most frequently used methods of Kriging, probably the most adequate in cases when the input data is marked by a common trend. That exact feature is often an attribute of deep geological data, and thereby that of structural maps. Mapped surfaces in a row of examples have a structural trend towards one cardinal direction, or a sequence of geological structures, like anticlinorium, is a part of a structural unit of a higher order such as regional monocline. An example is given of geographical trend recognition in e-log Z’ surface spread in Šandrovac Field as well as successful mapping of that marker depth variable by using Universal Kriging.

scholarly journals Application of the Bootstrap Method in Data Analysis of Upper Miocene Sandstone Reservoirs of the Western Part of the Sava Depression

2020 ◽  
Author(s):  
Josip Ivšinović ◽  
Tomislav Malvić ◽  
Dubravka Pleše
Keyword(s):  
Input Data ◽  
Bootstrap Method ◽  
Reliable Data ◽  
Formation Water ◽  
Geological Data ◽  
Exploration And Production ◽  
Small Set ◽  
Case Data ◽  
The Bootstrap Method

In deep geological analysis of data, these are input data that are few and include a small set of data. In a small set of case data, it is necessary to obtain reliable data of individual geological variables from this type of data. The paper analyzes the possibility of applying the bootstrap method on variables that are important in the exploration and production of hydrocarbons. The variables analyzed were the following: porosity and total costs of disposal formation water. The case study was made on the data of reservoir "K", field "B" located in the western part of the Sava Depression. The analysis of the results showed the possibility of applying the bootstrap method in the analysis of deep geological data with the application of three different sizes of resampling dataset.

The Artwork without Cardinal Direction

2019 ◽  
pp. 131-143
Author(s):  
Gerhard Richter
Keyword(s):  
Cognitive Maps ◽  
Critical Practice ◽  
Work Of Art ◽  
Cardinal Direction

Chapter 6 marks a transition from the uncoercive gaze as it finds expression in other aspects of Adorno’s work to the problem of orientation, understood both as an intellectual phenomenon and as a problem to be considered in relation to the work of art. This chapter adds another case study to the examination of Adorno’s critical practice of the uncoercive gaze by complicating the concept of orientation and supposed “cognitive maps” provided by the artwork and by theoretical discourse. Tracing Adorno’s abiding engagement with the problem of orientation back to Kant’s essay on what it might mean to orient oneself in thinking, the chapter interrogates how Adorno’s engagement with the problem of orientation and the attendant specter of disorientation inflects a broader set of concerns that traverse his writing throughout its various periods.

scholarly journals The extreme runoff index for flood early warning in Europe

2014 ◽  
Vol 14 (6) ◽  
pp. 1505-1515 ◽  
Author(s):  
L. Alfieri ◽  
F. Pappenberger ◽  
F. Wetterhall
Keyword(s):  
Early Warning ◽  
Input Data ◽  
Weather Forecasting ◽  
Spatial Inhomogeneity ◽  
Optimal Timing ◽  
Numerical Predictions ◽  
In Situ Observations

Abstract. Systems for the early detection of floods over continental and global domains have a key role in providing a quick overview of areas at risk, raise the awareness and prompt higher detail analyses as the events approach. However, the reliability of these systems is prone to spatial inhomogeneity, depending on the quality of the underlying input data and local calibration. This work proposes a simple approach for flood early warning based on ensemble numerical predictions of surface runoff provided by weather forecasting centers. The system is based on a novel indicator, referred to as an extreme runoff index (ERI), which is calculated from the input data through a statistical analysis. It is designed for use in large or poorly gauged domains, as no local knowledge or in situ observations are needed for its setup. Daily runs over 32 months are evaluated against calibrated hydrological simulations for all of Europe. Results show skillful flood early warning capabilities up to a 10-day lead time. A dedicated analysis is performed to investigate the optimal timing of forecasts to maximize the detection of extreme events. A case study for the central European floods of June 2013 is presented and forecasts are compared to the output of a hydro-meteorological ensemble model.

scholarly journals Calibrating Partial Factors – Methodology, Input Data and Case Study of Steel Structures

2019 ◽  
Author(s):  
Vitali Nadolski ◽  
Árpád Rózsás ◽  
Miroslav Sýkora
Keyword(s):  
Input Data ◽  
Structural Reliability ◽  
Construction Materials ◽  
Steel Structures ◽  
Structural Performance ◽  
Limit States ◽  
Different Types ◽  
Load Effects ◽  
Previous Design

Partial factors are commonly based on expert judgements and on calibration to previous design formats. This inevitably results in unbalanced structural reliability for different types of construction materials, loads and limit states. Probabilistic calibration makes it possible to account for plentiful requirements on structural performance, environmental conditions, production and execution quality etc. In the light of ongoing revisions of Eurocodes and the development of National Annexes, the study overviews the methodology of probabilistic calibration, provides input data for models of basic variables and illustrates the application by a case study. It appears that the partial factors recommended in the current standards provide for a lower reliability level than that indicated in EN 1990. Different values should be considered for the partial factors for imposed, wind and snow loads, appreciating the distinct nature of uncertainties in their load effects.

scholarly journals A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 516
Author(s):  
Jesús Peral ◽  
David Gil ◽  
Sayna Rotbei ◽  
Sandra Amador ◽  
Marga Guerrero ◽  
...  
Keyword(s):  
Input Data ◽  
Developmental Disorder ◽  
Distributed Processing ◽  
Repetitive Behavior ◽  
Developed Countries ◽  
Cognitive Disorder ◽  
Hidden Correlations ◽  
Restricted And Repetitive Behavior ◽  
Diverse Data

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

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