Mining hazard (not including coal)

Underground extraction of rocks and minerals has taken place in Britain for more than 5000 years. A broad range of raw materials has been extracted using underground mining methods, which can be categorised into seven commodity types; Bedded ores, Building stones, Chalk, Evaporites, Oil shale, Other commodities and Vein minerals.

The voids resulting from past underground mining activity pose a possible hazard. Former underground workings, particularly where shallow, may collapse and cause surface settlement. The mining hazard (not including coal) dataset indicates the potential for ground instability hazards due to subsidence associated with underground (non-coal) mining.

The mining hazard dataset draws together a diverse range of material derived from BGS geological mapping, which constrains distribution, supplemented mine abandonment plans, BGS databases of known working locations, mining literature reviews and expert knowledge to assemble, interpret and organise the information.

The information has been compiled into a digital format using GIS to apply a bespoke methodology for the seven commodity categories, each processed using tailored geological criteria reflecting distinct categories of mining. Each of these is the result of extensive research, specialist understanding and development and application of an appropriate method of data capture which are combined to deliver a single comprehensive mining hazard data layer. The data are presented in five susceptibility classes with an accompanying text description of the hazard class. Additionally, names of legacy working provided where known to support further research.

Mining of coal is specifically excluded from this dataset and enquiries on past coal mining should be directed to the Mining Remediation Authority.

The data is relevant to infrastructure asset owners for assessing susceptibility to ground instability, local and regional planners for assessing proposals, financial organisations such as insurers and lenders for reviewing the hazard exposure of portfolios and conveyancers when carrying out environmental reporting.

Mining hazards may lead to financial loss for anyone involved in the ownership or management of property, including developers, householders or local government. These costs could include increased insurance premiums, depressed house prices and, in some cases, engineering works to stabilise land or property. Armed with knowledge about potential hazards, preventative steps can be put in place to alleviate the impact of the hazard to people and property. The cost of such prevention may be very low, and is often many times lower than the repair bill following ground movement.

BGS Mining hazard (not including coal) Open

The BGS Mining Hazard (not including coal) GB Open is a generalised version of the Premium dataset and will be freely available under the BGS Non-commercial licence. This version of the dataset summarises susceptibility to ground instability information via spatial statistics (zonal statistics) on the basis of a regular hexagon grid distributed across the surface of Great Britain. The hexagon grid utilises the H3 discrete global grid system which provides a hierarchical geospatial indexing system that partitions the globe into hexagonal cells. This system is widely adopted by providers of open, geospatial data and therefore supports users in integrating BGS data with other H3 formatted datasets.

The Open data are derived by spatially summarising the information held in the Premium BGS Mining Hazard (not including coal) GB dataset. The original five classes have been rationalised to three ensuring that small scale mining features are not underestimated in the generalisation process. BGS Mining Hazard (not including coal) GB Open classes:

• Low: localised, small-scale mining may have occurred in the area
• Moderate: small-scale, underground mining may have occurred in the area
• Significant: underground mining is known or considered likely to have occurred in the area
• NA: no record of activity

Spatial statistics are used to determine the highest class (worst-case) present in the hexagon area. Each hexagon cell was then attributed with the highest (worse-case) class identified within the cell.