Vector Field-based Simulation of Tree-Like Non-Stationary Geostatistical Models

12/25/2018
by   Viviana Lorena Vargas, et al.
0

In this work, a new non-stationary multiple point geostatistical algorithm called vector field-based simulation is proposed. The motivation behind this work is the modeling of a certain structures that exhibit directional features with branching, like a tree, as can be frequently found in fan deltas or turbidity channels. From an image construction approach, the main idea of this work is that instead of using the training image as a source of patterns, it may be used to create a new object called a training vector field (TVF). This object assigns a vector to each point in the reservoir within the training image. The vector represents the direction in which the reservoir develops. The TVF is defined as an approximation of the tangent line at each point in the contour curve of the reservoir. This vector field has a great potential to better capture the non-stationary nature of the training image since the vector not only gives information about the point where it was defined but naturally captures the local trend near that point.

READ FULL TEXT

page 3

page 5

page 11

page 14

page 15

page 17

page 18

page 19

research
09/18/2018

Non-Stationary Covariance Estimation using the Stochastic Score Approximation for Large Spatial Data

We introduce computational methods that allow for effective estimation o...
research
06/16/2018

Adaptive estimating function inference for non-stationary determinantal point processes

Estimating function inference is indispensable for many common point pro...
research
10/07/2019

Skeleton based simulation of turbidite channels system

A new approach to model turbidite channels using training images is pres...
research
11/09/2020

An application of an Embedded Model Estimator to a synthetic non-stationary reservoir model with multiple secondary variables

A method (Ember) for non-stationary spatial modelling with multiple seco...
research
11/21/2022

Spatio-temporal point processes with deep non-stationary kernels

Point process data are becoming ubiquitous in modern applications, such ...
research
06/18/2018

Evaluating and Characterizing Incremental Learning from Non-Stationary Data

Incremental learning from non-stationary data poses special challenges t...
research
11/10/2022

Multivariate compactly supported C^∞ functions by subdivision

This paper discusses the generation of multivariate C^∞ functions with c...

Please sign up or login with your details

Forgot password? Click here to reset