Self Organising Maps - A Case Study of Broken Hill

Abstract

The principle aim of the research was to overcome the challenges faced by modern geophysical data analysts, particularly those working with large multivariate datasets using Self Organising Maps (SOM). SOM is an unsupervised learning technique for multivariate data, which works by taking multiple geophysical datasets for an area of interest, and integrating them to illustrate trends. Once developed, our method drastically lowered the time required for an analyst to examine and identify trends and relations across a broad range of geophysical, geochemical and other data layers. It also revealed hidden relations and distinct populations within correlated layers. Our study shows that SOM continues to be a powerful tool in accelerating the interpretation process. This includes the separation of features into distinct geological units, even without any preliminary map inputs to the SOM process. It also highlights SOM’s ability to highlight variation in cover, which has been identified as a key aspect moving forward in Australia’s mining future, when considering the vast expanses of Australia covered in sub cropping rock. In the future as data continue to grow and overlap, SOM will play an important role in highlighting these relations in soil cover and outcrop geology.