A rudimentary treatise on coal and coal-mining,

Mode of access: Internet.

An elementary text-book of coal mining... /

Mode of access: Internet.

The principles and practice of coal mining /

Mode of access: Internet.

A text-book of coal-mining. For the use of colliery managers and others.

Short bibliography at end of each chapter.

History of the manufacture of iron in all ages, and particularly in the United States from colonial time to 1891. Also a short history of early coal mining in the United States ...

Mode of access: Internet.

A Guide to Surface Features Related to Underground Coal Mining

The purpose of this guide is to assist investigators conducting geologic hazard assessments with the understanding, detection, and characterization of surface features related to subsidence from underground coal mining. Subsidence related to underground coal mining can present serious problems to new and/or existing infrastructure, utilities, and facilities. For example, heavy equipment driving over the ground surface during construction processes may punch into voids created by sinkholes or cracks, resulting in injury to persons and property. Abandoned underground mines also may be full of water, and if punctured, can flood nearby areas. Furthermore, the integrity of rigid structures such as buildings, dams and bridges may be compromised if mining subsidence results in differential movement at the ground surface. Subsidence of the ground surface is a phenomenon associated with the removal of material at depth, and may occur coincident with mining, gradually over time, or sometimes suddenly, long after mining operations have ceased (Gray and Bruhn, 1984). The spatial limits of underground coal mines may extend for great distances beyond the surface operations of a mine, in some cases more than 10 miles for an individual mine. When conducting geologic hazard assessments, several remote investigation methods can be used to observe surface features related to underground mining subsidence. LiDAR-derived DEMs are generally the most useful method available for identifying these features because the bare earth surface can be viewed. However, due to limitations in the availability of LiDAR data, other methods often need to be considered when investigating surface features related to underground coal mining subsidence, such as Google Earth and aerial imagery. Mine maps, when available, can be viewed in tandem with these datasets, potentially improving the confidence of any possible mining subsidence-related features observed remotely. However, maps for both active and abandoned mines may be incomplete or unavailable. Therefore, it is important to be able to recognize possible surface features related to underground mining subsidence. This guide provides examples of surface subsidence features related to the two principal underground coal mining methods used in the United States: longwall mining and room and pillar mining. The depth and type of mining, geologic conditions, hydrologic conditions, and time are all factors that may influence the type of features that manifest at the surface. This guide provides investigators a basic understanding about the size, character and conditions of various surface features that occur as a result of underground mining subsidence.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry