Information Theory Based Probabilistic Machine Learning And Wireline Inversion: Surat Basin Case Study

Information Theory and Machine Learning

Machine learning techniques are becoming increasingly useful primarily with the rapid development of Internet. A variety of machine learning methods have drawn inspirations or borrowed ideas from information theory. In this paper, we present a survey of such interactions between machine learning and information theory. Four important areas of machine learning are examined from the perspective o...

Application of Machine Learning Approaches in Rainfall-Runoff Modeling (Case Study: Zayandeh_Rood Basin in Iran)

Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model tree...

Case-Based Reasoning for Explaining Probabilistic Machine Learning

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As ba...

Comparing Geostatistical Seismic Inversion Based on Spectral Simulation with Deterministic Inversion: A Case Study

Seismic inversion is a method that extracts acoustic impedance data from the seismic traces. Source wavelets are band-limited, and thus seismic traces do not contain low and high frequency information. Therefore, there is a serious problem when the deterministic seismic inversion is applied to real data and the result of deterministic inversion is smooth. Low frequency component is obtained fro...

Probabilistic machine learning