| Abstract | Increased availability and use of 3D rendered geological models has provided society with predictive capabilities, supporting natural resource assessments, hazards awareness and infrastructure
development. The Geological Survey of Canada, along with other such institutions, have been trying to standardize and operationalize this modelling practice. Knowing what is in the subsurface, however is not an easy exercise, especially when it is
difficult or impossible to sample at greater depths. Existing approaches to creating 3D geological models involves development of surface components that represent spatial geological features, horizons, faults and folds, and then assembling them into
a framework model as context for down-stream property modelling applications (geophysical inversions, thermo-mechanical simulations, fracture density models etc.). The current challenge is to develop reasonable starting framework geological models
from sparser data regions, when we have more complicated geology. This study explores this problem of geological data sparsity and presents a new approach that may be useful to open up the log jam in modelling the more challenging terrains using an
agent-based approach. Semi-autonomous software entities called spatial agents can be programmed to perform spatial and property interrogation functions, estimations and construction operations for simple graphical objects, that may be usable in
building three-dimensional geological surfaces. These surfaces form the building blocks from which full geological and topological models are built and may be useful in sparse data environments, where ancillary or a-priori information is available.
Critical in developing natural domain models is the use of gradient information. Increasing the density of spatial gradient information (fabric dips, fold plunges, local or regional trends) from geologic feature orientations (planar and linear) is
key to more accurate geologic modelling, and core to the functions of spatial agents presented herein. This study, for the first time, examines the potential use of spatial agents to increase gradient constraints in the context of the Loop project
(https://loop3d.github.io/) in which new complementary methods are being developed for modelling complex geology for regional applications. The Spatial Agent codes presented may act to densify and supplement gradient, and on-contact control points,
used in LoopStructural (www.github.com/Loop3d/LoopStructural) and Map2Loop (https://doi.org/10.5281/zenodo.4288476). Spatial agents are used to represent common geological data constraints such as interface locations and gradient geometry, and simple
but topologically consistent triangulated meshes. Spatial agents can potentially be used to develop surfaces that conform to reasonable geological patterns of interest, provided they are embedded with behaviors that are reflective of the knowledge of
their geological environment. Initially this would involve detecting simple geological constraints; locations, trajectories and trends of geological interfaces. Local and global eigenvectors enable spatial continuity estimates, which can reflect
geological trends, with rotational bias, using a quaternion implementation. Spatial interpolation of structural geology orientation data with spatial agents employs a range of simple nearest neighbour to inverse distance weighted (IDW) and quaternion
based spherical linear interpolation (SLERP) schemes. This simulation environment implemented in NetLogo 3D is potentially useful for complex geology - sparse data environments where extension, projection and propagation functions are needed to
create more realistic geological forms. |