Albert Services

Albert uses its proprietary CARDS (Computer Aided Resources Detection System) to help mineral exploration professionals identify areas with a high statistical probability of similarity to known areas of mineralization. In combination with modern exploration techniques, CARDS is a useful tool to save both money and time by limiting target areas for exploration. In addition to data mining and target generation, Albert offers project management services.

A few words about CARDS

All 2D/3D tools and services developed by DIAGNOS Mining division in the past will be used by Albert Mining. We might have changed our name, but we haven’t changed who we are. We will maintain our service approach with existing and future customers. Albert Mining has continuously proved the methodology through projects & discoveries of our clients… while reducing its impact on the environment… much quicker than in the past.

You know what they say “the best place to find a mine is next to a mine”. This is exactly what Albert Mining will do to discover the next mine!

CARDS locates mineral deposits in 4 easy steps.

To understand the process of CARDS, please click on the following steps in order. 

During this comprehensive & complex process, through the help of CARDS, all available information about the modeling area is compiled.

All the data are entered into CARDS in the the form of geo-referenced data points. Each point in the database is linked to its own set of characteristics (variables) that are extracted from a variety of sources, for example:

  • Proximity to mineral occurrences / mineralized drill holes
  • Geophysical surveys: MAG, EM, IP, gravity, radiometry
  • Geochemical surveys: rock, soil, lake bottom, drill hole assays
  • Satellite imagery
  • Geological maps: rock type, alteration
  • Digital elevation models
  • Proximity to lithological contacts / specific intrusive suites
  • Proximity to interpreted lineaments / mapped faults and shear zones

We can then identify the positive points (drill holes and MDI’s) according to the established thresholds for each of the mineralizations sought. By using a moving window, we capture the neighbouring patterns around each point, expressed by new calculated variables for each primary exploration layer. In the analysis of each point in the database, the characteristics of all points within a specified distance (neighbourhood) are weighted into the evaluation of that point. Therefore, the combination of their limited characteristics and their proximity to points with other significant characteristics is similar to that of known positive points.

The models analysis is based, inter alia, on the variable importance and the models comparison. Variable importance is a difficult concept to define in general, because the importance of a variable may be due to its (possibly complex) interaction with other variables.

In order to study the accuracy of predictions and to validate results, several methods are used and compared on each data block.

  • Generate a signature of known positive occurrences using multiple models that discriminates between the positive and unknown points using all the existing information.
  • Aggregate the different rules of all models by getting a probability between 0 (unlike-positive) and 1 (like-positive) computed as the average of the different classification results. This probability represents the level of similarity of each point to the existing positive sites based on all variables used in the modeling.

The Data Mining technics find & mark the new patterns signatures created by CARDS through our scoring engine.

  • Classify each new unknown point based on the rules of classification already generated: a point is considered as positive if its probability is higher than a specified threshold level
  • Run a validation learning algorithm using the same input data of the prediction algorithm to ensure that the statistical process is working properly and that the results intuitively make sense.
  • Investigate visually by comparing target grids based on different models in order to get the targets relevance and priorities.
  • Targets generated by CARDS are evaluated in conjunction with all readily available geological data in the evaluation of the economic potential of a property as well as in the outlining of exploration targets.
+ STEP 1 :DATA GATHERING & PROCESS

During this comprehensive & complex process, through the help of CARDS, all available information about the modeling area is compiled.

All the data are entered into CARDS in the the form of geo-referenced data points. Each point in the database is linked to its own set of characteristics (variables) that are extracted from a variety of sources, for example:

  • Proximity to mineral occurrences / mineralized drill holes
  • Geophysical surveys: MAG, EM, IP, gravity, radiometry
  • Geochemical surveys: rock, soil, lake bottom, drill hole assays
  • Satellite imagery
  • Geological maps: rock type, alteration
  • Digital elevation models
  • Proximity to lithological contacts / specific intrusive suites
  • Proximity to interpreted lineaments / mapped faults and shear zones

We can then identify the positive points (drill holes and MDI’s) according to the established thresholds for each of the mineralizations sought. By using a moving window, we capture the neighbouring patterns around each point, expressed by new calculated variables for each primary exploration layer. In the analysis of each point in the database, the characteristics of all points within a specified distance (neighbourhood) are weighted into the evaluation of that point. Therefore, the combination of their limited characteristics and their proximity to points with other significant characteristics is similar to that of known positive points.

+ STEP 2: MODEL SET UP

The models analysis is based, inter alia, on the variable importance and the models comparison. Variable importance is a difficult concept to define in general, because the importance of a variable may be due to its (possibly complex) interaction with other variables.

In order to study the accuracy of predictions and to validate results, several methods are used and compared on each data block.

  • Generate a signature of known positive occurrences using multiple models that discriminates between the positive and unknown points using all the existing information.
  • Aggregate the different rules of all models by getting a probability between 0 (unlike-positive) and 1 (like-positive) computed as the average of the different classification results. This probability represents the level of similarity of each point to the existing positive sites based on all variables used in the modeling.
+ STEP 3: DATA MINING & PREDICTION

The Data Mining technics find & mark the new patterns signatures created by CARDS through our scoring engine.

  • Classify each new unknown point based on the rules of classification already generated: a point is considered as positive if its probability is higher than a specified threshold level
  • Run a validation learning algorithm using the same input data of the prediction algorithm to ensure that the statistical process is working properly and that the results intuitively make sense.
+ STEP 4: INTERPRETATION & REPORTING
  • Investigate visually by comparing target grids based on different models in order to get the targets relevance and priorities.
  • Targets generated by CARDS are evaluated in conjunction with all readily available geological data in the evaluation of the economic potential of a property as well as in the outlining of exploration targets.

Want to know more? Contact Albert.