Pattern of [formula omitted] concentration in a deep copper mine and its correlation with ventilation schedule.

• Long term data related to H 2 S concentration in mining corridor has been considered. • Novel methodology for environmental data is proposed. • Daily pattern has been discovered in highly random data. • Method has been validated for real data measured in the deep mine. The quality assessment of the air in a deep underground mine is a challenging issue. It is a time-varying process and it depends on several factors, mainly on technological processes such as blasting, air conditioning, ventilation, machines operations, as well as gas released by rock mass and humidity. The air quality should be monitored and analyzed to understand the process as much as possible in order to facilitate the miner's work and to improve its safety. One of the most critical parameters of the air quality in the considered mine is the hydrogen sulphide ( H 2 S) concentration. It is related to the geology of deposit thus one should expect the random nature of the gas concentrations. In this paper, we focused on H 2 S concentration analysis based on long term monitoring. Signal segmentation procedure for raw data has been proposed, the segmented data (daily patterns) have been visualized and finally statistically analyzed. It has been found that there are deterministic components in the H 2 S data variation, which strongly depend on ventilation operation regimes. This can be a basis for further data analysis and for controlling air quality in the mine in a more effective way. [ABSTRACT FROM AUTHOR]