Most of the geochemical datasets include missing data with different portions and this may cause a significant problem in geostatistical modeling or multivariate analysis of the data. Therefore, it is common to impute the missing data in most of geochemical studies. In this study, three approaches called half detection (HD), multiple imputation (MI), and the cosimulation based on Markov model 2...

Introduction to Gaussian Random Function Simulation The so-called Gaussian Random Function simulation (GRFS) differs substantially from the Sequential Gaussian simulation (SGS) from GSLIB. GRFS more accurately reproduces distributions. It is typically faster than SGS, with additional efficiencies due to its parallel architecture. GRFS also has an option to run a fast collocated cosimulation – f...

Abstract Accurate pore pressure models in wells are essential for ensuring the lowest cost and operational safety during exploration/development projects. This modeling requires integration of several sources information such as well data, formation tests, geophysical logs, mud weight, geological models, seismic geothermal sedimentation rate modeling. An empirical relationship between overpress...

 In this paper, we present a hardware-software cosimulation environment for heterogeneous systems. To be an efficient and convenient verification environment for the rapid prototyping of heterogeneous systems consisting of hardware and software components, the environment supports i) modular cosimulation, ii) cosimulation acceleration, and iii) integrated user interface and internal representa...

The aim is to explain the current issues of HW/SW cosimulation and to introduce a new challenge of HW/SW cosimulation for multiprocessor SoC (MPSoC). Most of the current issues are related to raising abstraction levels of HW/SW cosimulation. Mixed-level cosimulation is explained in a unified manner using a concept of ‘HW/SW interface’. First, abstraction levels in HW/SW cosimulation are explain...

This study introduces a Bayesian Markov chain random field (MCRF) cosimulation approach for improving land-use/land-cover (LULC) classification accuracy through integrating expert-interpreted data and pre-classified image data. The expert-interpreted data are used as conditioning sample data in cosimulation, and may be interpreted from various sources. The pre-classification can be performed us...

To improve the performance of geographically distributed cosimulation, we propose a concept called hierarchically grouped message. The concept improves cosimulation performance, preserving the cosimulation accuracy, by hierarchically grouping messages transferred between simulators in a short period of simulated time into a single physical message, thereby reducing the number of physical messag...

This paper defines a suite of requirements for future hybrid cosimulation standards, and specifically provides guidance for development of a hybrid cosimulation version of the Functional Mockup Interface (FMI) standard. A cosimulation standard defines interfaces that enable diverse simulation tools to interoperate. Specifically, one tool defines a component that forms part of a simulation model...

In this paper, we present two approaches to improving the performance of single-processor timed cosim-ulation. One of the approaches is optimistic timed cosimulation and the other is non-IPC (interprocess communication) timed cosimulation. The optimistic timed cosimulation algorithm optimistically estimates the time for synchronization between HW simulator and SW simulator and runs simulation t...