Abstract

Water quality data is collected throughout the mine project life cycle. During early stages, water quality is collected for baselining purposes. During feasibility, permitting, and mine planning, water quality data collected from material characterization tests is combined with baseline data to develop models predicting water quality associated with future mining activities. Prediction outcomes support material and water management strategies. During operations, water quality data is collected to monitor mine facility seepage, water treatment performance, and mine boundary compliance. At closure, water quality is collected to assess closure strategy effectiveness and employ adaptive management strategies. Water quality data is ubiquitous through the project life cycle and yet remains highly underutilized.

The abundance of this data is ideally suited to using machine learning approaches to maximize its value. It is typically collected at regular intervals throughout the mine life cycle. Furthermore, compliance and operational requirements dictate that water quality be collected at sufficient spatial density. Finally, water quality data is multi-dimensional, commonly including measurements for 20 to 40 (or more) different parameters. The large number of parameters makes for a “wide” dataset with considerable statistical variance, which facilitates application of innovative unsupervised machine learning methods.

Application of unsupervised methods to water quality data in four different parts of the mine project life cycle indicates that the multivariate approach is highly effective in identifying different water quality domains, breakthrough of MIW, mixing effects and various reactive processes that occur in water.

The four use cases have application and implications as follows:

• Baseline and compliance: machine learning methods are applied to separate pre-mining water quality in different hydrostratigraphic units and to detect vertical transmissivity between units.
• Feasibility and permitting: interpretation of a large laboratory humidity cell dataset to understand the formation and timing of acid conditions; this information can be applied at the field scale to predict acidic conditions in mine waste facilities and assess regional groundwater patterns in mineralized zones.
• Operations: forensic study to detect mine process water influence in both surface and groundwater, and to separate MIW from natural acid rock drainage.
• Closure planning: coupled unsupervised machine learning and isotope study is used to identify natural acid rock and neutral drainage and to separate these chemical fingerprints from groundwater impacted by pit and waste rock generated acid rock drainage; this work is all in support of development of the long-term site closure strategy.