Distance Maps

Shows how close each cell is to things like roads, faults, or boundaries.

Rasterizes vector features (points, lines, polygons) into distance-to-feature grids for spatial modelling.

Input: Vector data (.SHP)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Vector category, Desired category

Processing: Converts vector features into distance-to-feature grids. One grid per feature type if categories are specified.

Data Density Maps

Highlights areas where you have lots of information vs. little to none.

Creates a grid that reflects how many data layers are present per pixel, highlighting data-rich areas.

Input: Vector data (.SHP), Raw grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI

Processing: Counts the number of layers informing each pixel to highlight data density.

ML Prediction Maps

Fills in the gaps by predicting what values would be in places you haven’t sampled.

Predict target variables from point data and auxiliary rasters through supervised learning.

Input: Points (.SHP), Auxiliary grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Target variable, Auxiliary grids, Subsampling, Smoothing

Processing: Trains a supervised model on sample points and predicts values across a grid. Includes quality control and smoothing.

Rock Geochemical Maps

Estimates what the rock chemistry values are between sample locations.

Interpolate rock chemistry data from samples across a region using auxiliary inputs.

Input: Sampling points (.SHP), Auxiliary grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Target elements, Auxiliary grids, Subsampling, Smoothing

Processing: Interpolates chemical elements from rock samples using auxiliary data. Flags low-quality predictions.

Surficial Sediment Geochemical Maps

Predicts soil or sediment chemical composition between sampling points.

Model sediment geochemistry by combining sampling data with terrain, watershed context and other representative datasets.

Input: Sampling points (.SHP), DEM, Auxiliary grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Target elements, DEM, Watershed scale, Auxiliary grids, Subsampling, Smoothing

Processing: Combines terrain, watershed features and other data with geochemical samples to predict sediment chemistry across a grid.

Structural Field Maps

Creates a map of which way rock layers are tilted or folded underground.

Convert orientation measurements (strike/dip) into interpolated structural grids, vector fields.

Input: Measurement points (.SHP)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Structure types, Strike, Dip, Right hand rule

Processing: Converts structural measurements into interpolated strike field maps using transformation rules.

Fault Disturbance Maps

Maps how the volume of rock is affected by a fault.

Model spatial influence and disturbance zones around fault lines with customizable decay.

Input: Line vectors (.SHP)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Faults, Wavelength, Disturbance level, Decay

Processing: Models wave-based disturbance zones around faults with decay scaling away from fault trace.

Feature Extraction Maps

Pulls out hidden patterns in the data using image recognition techniques.

Apply deep learning filters (feature extraction) to raster inputs to extract high-level spatial features.

Input: Grid (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Number of filters

Processing: Applies deep learning (RESNET-50) and Haralick filters to extract spatial features from raster data.

Multivariate Anomaly Maps

Flags areas where the data looks strange or unexpected when layers are compared together.

Detect anomalies based on multi-layer raster analysis using an anomaly scoring algorithm.

Input: Data grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Target grids

Processing: Analyzes multiple grids to detect anomalies across all data, using a scoring algorithm across layers.

Lineament Extraction Maps

Traces straight-line features like fractures or boundaries that show up in the data.

Identify and extract linear features (lineaments) using edge detection and line-following algorithms.

Input: Data grid (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Edge detection sensitivity, Line length, Gaps, Orientation filters

Processing: Detects and traces linear features from raster data using edge and line detection techniques.

Geological Domain Probability Maps

Predicts what kind of rock is likely in each location based on patterns in the input data.

Use geochem, lithology, and auxiliary data to classify terrain into probabilistic geological domains.

Input: Lithology points (.SHP), Geochemical points (.SHP), Auxiliary grids (.ERS)

Output: Grid (.ERS)

Key Parameters: Grid resolution, AOI, Target lithologies, Geochemical elements, Auxiliary signal

Processing: Trains a deep learning model to predict probabilities of lithological domains across a region.