Subsurface fluid flow is critically dependent on the 3D distribution of petrophysical properties in rocks. In sequences of sedimentary rocks these properties are strongly influenced by lithology and facies distribution that stem from the geologic processes that generated them.

When I first started conducting research in Mongolia, I found that I could make a room full of anthropologists jealous by telling them where I worked. To a discipline whose members pride themselves on working in exotic locations, Mongolia is a glamorous research site, for few Western and even fewer American anthropologists had worked there during the Cold War.1 Young anthropologists started to trickle in during the early 1990s: Norwegian, Danish, and French graduate students; Christopher Kaplonski, Peter Marsh, and Katherine Petrie from the United States; and, of course, the University of Cambridge research group (Mongolian and Inner Asian Studies Unit) headed by Caroline Humphrey and now David Sneath. So anthropology, my discipline, was becoming interested in Mongolia.

This report responds to a request by the U.S. Geological Survey (USGS) that the National Research Council (NRC) review its concept of The National Map. The National Map is envisioned by USGS as a database providing “public domain core geographic data about the United States and its territories that other agencies can extend, enhance, and reference as they concentrate on maintaining other data that are unique to their needs” (USGS, 2001).

In 1916, the first generation of geological graduates entered the China Geological Survey and opened a new era of geological survey in China. Over the past 100 years, generations of geologists have made outstanding contributions to the geological survey and prospecting for China’s prosperity. They measure the ground, search for treasures, explore the earth, and engrave the beautiful mountains and rivers.

Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia.

The onset and rapid spread of canals across the face of Britain in the late eighteenth century, closely followed by the building of the railway network in the early nineteenth century, were largely responsible for making the study of the strata, or stratigraphy, a subject of both practical and economic value. It is not surprising, therefore, that it was a land surveyor and canal engineer, William Smith (1769–1839), working initially in southern and eastern England, who first worked out that rock strata were not randomly disposed around the country but arranged in a definite order.

A review of exploration expenditure by exploration stage over 15 years at Laverton Western Australia has identified a poor conversion rate from the first stage (ground acquisition) to the second stage (drill testing). This poor conversion rate is primarily due to poor target selection. The use of a well described ore deposit model and regional geological and regional geophysical datasets is able to improve target selection. The Thermal Aureole Gold ore deposit model and the relevant geological features that can be detected in regional geological maps are described and some examples are shown for Morila, Sadiola and Muruntau gold deposits. <...>