Search

Parkinson, F.; Douglas, K.; Li, Z.; Meijer, A.; Stacey, C.D.; Kung, R., and Podhorodeski, A., 2024. A generalized semiautomated method for seabed geology classification using multibeam data and maximum likelihood classification. Journal of Coastal Research, 40(1), 1–16. Charlotte (North Carolina), ISSN 0749-0208. This paper presents a GIS-based model to perform semiautomated seabed classification that can act as a first-pass, pseudoclassified surficial geological map. The user can then edit the output into a finalized map in less time than by manual classification. The model uses maximum likelihood classification with unsupervised classification through iterative self-organizing clusters. This model is fully contained within the ArcGIS software suite as a ModelBuilder workflow composed of geoprocessing tools and Python script tools. Model inputs tested include different combinations of multibeam echosounder–derived data: slope, backscatter, and terrain ruggedness. Furthermore, to test the assumption of Gaussian distribution of input data required for maximum likelihood classification, Box–Cox power transformations were applied to slope and backscatter data and were used as model inputs. To illustrate the performance of the model, two locations are highlighted as case studies: Milbanke Sound and Spiller Channel, located on the central coast of British Columbia, Canada. Association between model outputs and ground-truth classes was generally weak to moderate when measured using Cramér's V association scores. Overall, the slope and backscatter parameter model had the highest scores of association. Results from an overlay analysis comparing model outputs with user-confirmed polygons show that the slope and backscatter model performs best in regions with distinct changes in the hardness of sediments but that in fjord regions dominated geologically by steeper bathymetric change, the slope parameter model may perform better. However, all model outputs had difficulty delineating bedrock units. The model has the flexibility to identify certain seabed habitat features as well, including glass sponge reefs—biologically active bioherms that have led to marine protected area designations in other areas of British Columbia. [ABSTRACT FROM AUTHOR]

Infestation by the mountain pine beetle, Dendroctonus ponderosae, decimated the forests of central British Columbia from 1999 to 2012, severely impacting the forest industry of the Nechako-Chilcotin plateau. In response, all levels of government recognized the value in developing other areas of economic activity, such as hydrocarbon and mineral exploitation, to support local economies. Exploration for resources beneath the Nechako-Chilcotin plateau has historically been constrained by Tertiary volcanic sequences and Quaternary glacial deposits that obscure the underlying geology and limit geophysical imaging. Thus, a coordinated program comprising additional geological mapping, borehole data analysis, and modern geophysical surveys of the area was initiated in 2006, with the objective of better defining the subsurface geology, solving problems of imaging through the complex near-surface, and developing improved regional geological and tectonic models. An initial set of papers arising from this fieldwork, which focused on issues relevant to mineral and hydrocarbon exploration, was published in June 2011 in a Special Issue of the Canadian Journal of Earth Sciences. This Introduction to the second 'Mountain Pine Beetle' Special Issue summarizes a set of scientific papers that focus on topics more related to hydrocarbon exploration and the large-scale structure of the crust. The papers deal with the development, thickness, and present distribution of the most prospective Cretaceous sedimentary rocks, as well as characterizing the physical properties of the near-surface volcanic units. [ABSTRACT FROM AUTHOR]

Mineral and petroleum exploration in the Cariboo-Chilcotin region of British Columbia is hampered by widespread and extensive cover of Pleistocene glacial deposits and Tertiary volcanic successions. Seeing through this geological cover is critical to reducing exploration risk and enhancing exploration activity. Also critical to exploration is effective community engagement, hopefully resulting in endorsement and support for exploration initiatives. The forests in the Cariboo-Chilcotin region have been extensively destroyed by an infestation of the mountain pine beetle, , disrupting established communities and greatly affecting economic activity in the region. Governments of all levels recognized that geological exploration activity could provide a ready and appreciable stimulus to economic activity, but only if local communities endorsed such programs. Relatively little oil and gas exploration and research has taken place in the region, and its effects have been poorly understood locally. Consequently, an extended effort was undertaken to establish an integrated geoscience program for the Cariboo-Chilcotin region, focused on mineral and petroleum exploration-related research and coupled with engagement with local communities to inform them of exploration benefits and risks. This Introduction to the 'Mountain Pine Beetle' Special Issue of Canadian Journal of Earth Sciences provides a brief overview of the scientific papers included in the issue and also a review of the community engagement process that was undertaken to establish working relationships with First Nations and other communities in the region. [ABSTRACT FROM AUTHOR]

In this paper we present the framework and open-source software code to train and apply Deep Learning Convolutional Neural Networks (CNNs) to the prediction of geological lineaments using topographic, magnetic, and gravity raster data. Many important applications relate to the recognition of linear geological structures from remote sensing data, such as thrust faults, bedrock fault and shear zones, lithological contacts, fractures and fold structures. The digitization of fault lineaments is conventionally performed by geologists or geophysicists with working knowledge of the relevant data, e.g., topographic Digital Elevation Model, magnetic data, and gravity data. Visual inspection and extraction is simple but subjective; the process is also time-expensive with efficiency and accuracy depending on the individual's knowledge, experience, and skill. For decades there has been interest in ways to automate this process. Our CNN approach is trained using publicly available lineament GIS data from the Quest BC project in British Columbia, Canada, and the Loch Lilly-Kars area of New South Wales, Australia. The datasets used to train the prediction models resulted in interesting predictions proximal to the training areas: some major lineaments are indicated, some are missed, and potential new (valid) lineaments are indicated. The results indicate potential for use as a semi-automated lineament detection solution. In contrast, but as anticipated, application of the model to the blind-test area of the Swayze greenstone belt, Ontario, produced poor lineament prediction results (as compared to publicly available interpretations). We interpret this result as related to insufficiently large training data inputs. However, it is inferred that results could be improved through feature engineering (e.g., use of topographic slope, rather than simply elevation) without the need to simply create larger training datasets. We hope that, by making the code open-source, the geoscience community will use this platform to gradually improve an open source fault prediction model. [Display omitted] • CNNs used to predict fault lineaments with remote sensing data. • Terrain models, magnetic and gravity data are used as input features. • Clustering and post-processing are the key elements for post-prediction processing. • We infer that improved feature engineering can improve future model generalization. [ABSTRACT FROM AUTHOR]

Although alkalic porphyry Au--Cu deposits are much less common than their calc-alkalic counterparts, they represent attractive exploration targets both due to their occurrence as, locally concealed, clusters and their high Au contents. In this paper, the geology of the Mt. Milligan Au--Cu deposit, Canada, and its geophysical footprint on magnetic and AEM data is used as a case-study for the exploration and discovery of concealed alkalic porphyry Au--Cu deposits. The Cole-Cole parameters in the AEM data are extracted using an improved Airborne Induced Polarization (AIP) mapping algorithm. We propose that the subtle electromagnetic signatures of disseminated sulfides around the intrusive porphyry stocks at Mt. Milligan could be mapped by the m τ product, where m is AIP apparent chargeability, and τ the Cole-Cole time-constant, provided that the depths of the alteration zone does not exceed 75 m. The product is called Tau-Scaled Chargeability or TSC. In order to improve the exploration success in the search for concealed alkalic porphyry Au--Cu deposits, we propose to: (1) concentrate the exploration on known and well-endowed mineral districts; and (2) apply innovative digital data-processing techniques capable of extracting weak and subtle signatures from airborne geophysical data and, subsequently, to utilize DCIP ground surveys in the follow-up of anomalies, ideally in combination with enzyme-leach soil sampling. • Alkalic porphyry Au-Cu deposits are attractive exploration targets due to their high Au contents. • They occur in clusters, typically above fractionating magma chambers in the upper crust. • AIP mapping can define pyrite halos around concealed intrusive stocks at Mt. Milligan. • AIP mapping can be used as a vectoring tool. [ABSTRACT FROM AUTHOR]

In geo-engineering projects dealing with rock formations, and especially with rocky outcrops, discontinuity orientation is one of the most important parameters that should be carefully estimated. Slope stability is significantly dependent on discontinuity geometry, and even slight changes in orientation may affect the factor of safety. For this reason, joint orientation measurement is the main component of the preliminary site investigation for slopes. The use of three dimensional models derived from LiDAR scans or photogrammetric models has seen substantial growth in the field of geosciences in the last decade, with many examples of useful application and analysis. Several authors have developed techniques and methodologies (Jaboyedoff et al. 2010; Kemeny et al. 2006; Lato at al. 2007; Riquelme et al. 2014) to semi-automatically extract geometric features including joint orientation by using LiDAR data. In this study, the Discontinuity Set Extractor (DSE) (Riquelme et al. 2014) methodology for plane detection and joint orientation extraction is examined.DSE provides a powerful toolset for plane detection and plane orientation assessment, and has been tested in several cases (Riquelme et al. 2017; Buyer, AA & Schubert, W. 2017; Farmakis, I. 2018). However, when evaluating the stability of complex rock masses, some difficulties and uncertainties may arise. The current study demonstrates an application of the DSE methodology on a complex rocky slope in British Columbia, comprising four different lithologies and complex tectonics. In this case, The methodology was found to be problematic, in part due to the available LiDAR data set, and in part due to the complexity of the surface exposure of the joint surfaces. The method was modified as are discussed in this paper so that realistic outputs can be extracted. All of the tests, together with the different approaches, are presented in detail, and the results are compared with manually measured orientations along the point cloud for the verification of their validity. [ABSTRACT FROM AUTHOR]