Spatial agents for geological surface modelling

Abstract

Abstract. Increased availability and use of 3D-rendered geological models have provided society with predictive capabilities, supporting natural resource assessments, hazard awareness, and infrastructure development. The Geological Survey of Canada, along with other such institutions, has 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 for creating 3D geological models involve developing surface components that represent spatial geological features, horizons, faults, and folds, and then assembling them into a framework model as context for downstream property modelling applications (e.g. geophysical inversions, thermo-mechanical simulations, and fracture density models). The current challenge is to develop geologically reasonable starting framework models from regions with sparser data when we have more complicated geology. This study explores the problem of geological data sparsity and presents a new approach that may be useful to open up the logjam 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 3D 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, and local or regional trends) from geologic feature orientations (planar and linear) is the key to more accurate geologic modelling and is 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/, last access: 1 October 2021​​​​​​​) 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 as well as on-contact control points used in LoopStructural (https://www.github.com/Loop3d/LoopStructural, last access: 1 October 2021) and Map2Loop (https://doi.org/10.5281/zenodo.4288476, de Rose et al., 2020). 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 that they are embedded with behaviours 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 rotation 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.

Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru

“…The construction of three-dimensional (3D) geologic models is essential for the management, visual representation, amalgamation, and deduction of findings from geologic studies [6][7][8][9]. Its core lies in the generation of 3D representations from geological measurements and logs, with the aim of understanding the spatial arrangement, depositional connections, and nature of the stony subsurface [10][11][12][13]. These 3D models are vital in sectors such as geological context analysis, deep mining prospecting, geological disaster mitigation, and urban subway area planning [14][15][16][17][18][19].…”

GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships

“…To study structural landforms such as mountains, valleys, basins, and other types of geomorphology, it is essential to begin with the definition of structural geomorphology. This involves abstracting the structural and geomorphological elements into a unified framework, constructing spatial structural and geomorphological objects manifested by structural phenomena, such as folds, faults, and rock masses formed by tectonics [46]. The relationships between various elements in structural geomorphology are mainly explored through spatial relationships between geological phenomena, such as folds, faults, and strata, as well as the tectonic deformation and geomorphic parameters (surface relief) of folds and faults [47,48].…”