This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …

The Earth below ground is the subject of interest for many geophysical as well as geological
investigations. Even though most practitioners would agree that all available information …

The geo-structures embedded in the multiple variable strata could be significantly affected
by the geological uncertainty. The quantitative evaluation of geological uncertainty and its …

A random field is the representation of the joint probability distribution for a set of random
variables. Markov fields, in particular, have a long standing tradition as the theoretical …

The uncertainty involved in the interpreted geological model may be categorized as the
stratigraphic uncertainty and the properties uncertainty. Note that although the influence of …

The representation of subsurface structures is an essential aspect of a wide variety of
geoscientific investigations and applications, ranging from geofluid reservoir studies, over …

The story of statistics in geotechnical engineering can be traced to Lumb's classical
Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in …

If you scan a page from a soil report, this is called digitization. If you deploy digital
technologies, both software such as building information modeling and machine learning …

Consolidation analysis is a key task for reclamation design. Although consolidation is a long-
term process, acceleration of consolidation is often preferred for speeding up the …

An essential task in many geotechnical projects is delineation of subsurface soil stratigraphy
from scatter measurements. Geotechnical engineers often use their knowledge on local …