Issue link: http://resourceworld.uberflip.com/i/355430
50 www.resourceworld.com A U G U S T / S E P T E M B E R 2 0 1 4 MININGWORLD Diagnos Resources Inc. [ADK-TSXV] uses a computerized modeling system that inte- grates data sets to help resource companies locate new exploration targets, confirm identified drill targets and identify the most prospective areas on their property. The system has the ability to learn and is not a rule based model generator. Michel Fontaine, Vice President of Diagnos, spent some time with me, to explain how it works. The system helps explorers develop an unbiased understanding of the spatial distribution of any given deposit type. It can be used to identify satellite deposits at a mine by comparing data to different areas on the property and data from other famous deposits. The system has been suc- cessfully utilized in base metal rich areas such as the Ring of Fire and is also a tool applicable to gold, uranium and diamond exploration sectors. Diagnos uses its proprietary, state- of-the-art, CARDS (Computer Aided Resources Detection System) to identify areas with a high statistical probability of similarity to known areas of mineraliza- tion. This saves companies precious time and money by limiting the number of exploration targets by prioritizing the most promising ones. CARDS uses the latest artificial intel- ligence (AI) and pattern recognition algorithms to analyze very large, digital data sets of compiled geo-referenced his- torical exploration data. In their typical data driven approach, which lets the data speaks for itself, the input variables consist of geochemical and geophysical data sets. The system also proved to be successful in more knowledge driven approaches by using other data such as structural, geo- logical, topographical and satellite imagery data sets. "Data is entered into CARDS in the form of a geo-referenced database." The ProCeSS hAS Four STePS: Step 1– Data Gathering and Processing All available data are examined and com- piled by professional geologists and geophysicists to assure quality and suit- ability of all the input data sets. The system combines all layers of information available for the analysis which will create a grid of the area of interest. Step 2 – Model Set Up In this step, the model learns the signature of positive training points provided by drill hole, grab sample, channel and trench sampling results by querying all the lay- ered grids. Step 3 – Prediction (Data Mining) Using a scoring system, based on the posi- tive signatures, the system checks each pixel of the grid to look for similarities. The system identifies prospective areas where the signature of unknown pixels matches the signature of the pixels where positive assay results were discovered. The system doesn't interpret or extrapolate from the data nor does it invent data. Areas of high potential are identified in this step using algorithms to analyze each layer of informa- tion to find patterns – some of which are naked to the human eye. "Millions of spa- tial data points and their relationships are analyzed – in a rather short period of time – something the human brain cannot do when dealing with enormous multi-dimen- sional data sets…" Step 4 – Presentation At this point a map is created of potential ore zones which will be validated, inter- preted and prioritized by the geologists and geophysicists working for Diagnos. Fontaine presented such a map to Metanor Resources Inc. [MTO-TSXV]. The map revealed a promising gold sig- nature just 2.5 miles west of the Bachelor Mine (Zone 3) on Metanor's Bachelor Lake property located in the Eastern Abitibi Greenstone Belt. On May 19, 2011, Metanor announced encouraging results of its 2010 diamond drilling program on Zone 3. Diagnos: mining data for mineral targets by Kathrine Moore