Kevin is an Associate Technical Partner in ERM’s Asset Management team, with a focus on the sustainability of mining projects. Dr Leahy joined ERM in 2003 and is a Chartered Geologist with 30 years’ experience and a PhD on kimberlite geochemistry in Canada. He has a diverse earth science background including geochemistry, sedimentology, structural geology, soil science, hydrogeology and exploration field work. Formerly, he was a consultant in both mineral and hydrocarbon exploration industries and has worked in the field globally. At ERM, he has worked or led on numerous projects including hydrogeochemical exploration, environmental and social impact assessment, mine feasibility and NI 43-101 reporting as a QP, ESG transaction due diligence, closure and life cycle planning, site investigation and remediation, compliance auditing and resource exploration. Kevin has presented at numerous conferences on diverse topics such as mine closure, assessment and financial provision of environmental social issues in mine transaction due diligence, contaminant fate and transport and exploration hydrogeochemistry. He is currently collaborating with iCRAG on the application of exploration hydrogeochemical techniques developed at ERM on datasets across Ireland.
Towards an All-Ireland Regional Mineral Prospectivity using CSET
ERM Catchment Scale Exploration tool combines hydrogeochemistry and hydrogeology to give vectors on saturation-index anomalies in stream and groundwater: finding blind deposits, helping to rank targets, and de-risking licences. The approach has been confirmed on a porphyry Cu-Au target under 300 m cover with a measurable signal over 2.5 km; and an IOCG body under 100 m of cover in much flatter terrain with a signal 7 km downgradient, and has been used across several exploration projects. Ireland offers ~10,000 TELLUS samples plus several databases of Midlands groundwater that could be used for regional CSET prospectivity mapping. ERM has partnered with iCRAG for research and development of the approach using machine learning and a streamlined hydrogeochemical interpretation.