A Spatially Enabling Relational Geologic Database of Java Island,Western Sunda Arc of Indonesia ù A Step to the Geoscience and Mining Data Model

A relational database in which its complex contents can be navigated through a map interface (ie spatially enabling) is relatively a new concept yet attractive for geoscience fields. This concept is made possible using the new breed of geographic information system (GIS) that utilises component object model and relational database management system. Such sophisticated yet open and highly interoperable platform makes possible the integration of data management, analysis, and modeling processes into single workflow. Taking benefit of this technology trend, several initiatives emerge in geoscience to establish digital data models, such as the petroleum industry, the North America digital geologic map data model, and the ESRI geology data model. The mining industry unfortunately has not followed same paths due to the lack of data model and standards in its history. This paper presents an effort on establishing a digital data model for mining-related geoscience databases. The aim is to promote several standards by proposing a preliminary data model and demonstrating that the model is working for the mining business datasets. The case study is the island of Java, the western part of the Sunda island arc of Indonesia. Many types of datasets, such as geologic maps, topography, mineral deposits, structures, geochemical and geophysical data were dealt with and were transformed relational geologic databases of Java using the new Geodatabase geographic data model. Intelligent GIS tools were investigated for geologic applications, ranging from generic to specific purposes. Subtypes and domain were used to set up constraints, while topology rules and relationship classes were used to create links among data layers. The linear referencing system was used to model drillhole data. The current results show that the generic GIS and relational database can handle typical tasks related with mining business datasets. A preliminary data model is finally suggested.