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LiDAR and its application to Surface Geological Mapping and Structural Interpretation within the Western Fold Belt of Papua New Guinea


Accurate mapping of surface geology and structures is the essential first step in understanding subsurface geology for oil & gas exploration. Traditionally this has been undertaken using remotely sensed satellite and aerial imagery paired with ground truthing and sampling. In remote, rugged environments this can be logistically challenging, especially in said environments where unbroken forest canopy with thick undergrowth covers the underlying geology and obscures outcrops. LiDAR is a newer remote sensing technology that has the ability to ‘see’ through the vegetation, and, when visualized within Geographic Information Systems (GIS), can effectively strip away the vegetation, allowing the virtual ground surface to be observed in high-resolution detail, and interpreted. LiDAR (Light Distance And Ranging) operates on the same principle as RADAR, utilizing the timed reflectance of emitted energy between source and target with trigonometry to calculate the target location in 3-dimensional (3-D) space. Whereas RADAR utilizes radio wave energy, LiDAR uses emitted light energy in the form of laser pulses for 3-D target location. Airborne LiDAR surveys pair a LiDAR sensor and navigation equipment that utilizes highly accurate base station GPS with either fixed-wing or rotary-wing aircraft, to rapidly survey swaths of land from which a ground surface model can be generated. LiDAR data yields a 3-D aware (XYZ) point cloud of all reflections observed by the sensor. LiDAR systems using proprietary algorithms automatically classify these reflections, once located in 3D space by determining the first and last reflections within a defined 3D bin of the point cloud. Through iteration these algorithms compute ground reflections (generally the last return) from others and classify accordingly. LiDAR analysts using manual supervised classification methods improve the pre-classified point cloud data into further classes such as non-ground; low, medium or high canopy; or buildings, depending on customer requirements. Point cloud data attributed as ground returns are then interpolated into raster grids or surfaces to yield a bare earth ground model. These bare earth models, when consumed within 2-D GIS using techniques such as hill shading, slope, and rugosity, allows the interpretation of geological features such as lithology contacts, bedding, folds and faults that would otherwise be hidden by the forest canopy. Furthermore, these features can be visualized, digitized and converted to 3-D aware spatial data, including surface dip. These spatial data are available for importation to industry-standard applications such as PETREL or LITHOTECT and, when combined with seismic sections, drill logs, and other data, enable geoscientists to build new subsurface models or improve existing models. While not replacing traditional geological field work, the use of LiDAR allows rapid mapping and assessment of surface geology, contemporaneously improving the efficiency of field campaigns by targeting resources to ambiguous or structurally complex areas. Methods described herein have proven to be very effective in the Western Fold Belt of Papua New Guinea, an environment of complex terrain, remote location and expansive dense forest canopy.