24-9 Petrochronological Characterization of Sediment Sources Using Tensor analysis of Multivariate Detrital Zircon Data
Session: Advances and Applications in Geochronology for Interpreting Stratigraphic and Basin Records, Part I
Presenting Author:
Joel SaylorAuthors:
Saylor, Joel1, Richardson, Nicholas2, Graham, Naomi3, Lee, Robert G4, Friedlander, Michael P5(1) Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada, (2) Department of Mathematics, University of British Columbia, Vancouver, BC, Canada, (3) Department of Computer Science, University of British Columbia, Vancouver, BC, Canada, (4) Mineral Deposit Research Unit, University of British Columbia, Vancouver, BC, Canada; BHP Ltd, Tucson, AZ, USA, (5) Department of Mathematics, University of British Columbia, Vancouver, BC, Canada; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada,
Abstract:
Recognizing that non-unique univariate detrital datasets present a challenge to identifying sediment sources or source proportions, some studies have introduced secondary, petrochronological, parameters to discriminate between otherwise similar sources. Whereas the ability to acquire petrochronological data from detrital minerals has exploded, development of tools to analyse and interpret the multivariate datasets has not kept pace. One promising method recently developed is a multivariate variant of non-negative matrix factorization, known as Tucker-1 decomposition of 3-way tensors. Despite its numerical success, the Tucker-1 method has not yet been shown to yield geologically meaningful or reasonable results.
Herein we present a case study which applies the Tucker-1 decomposition method to a petrochronological detrital zircon dataset from till samples collected above the Cu-bearing Guichon Creek Batholith in southern British Columbia, Canada. This locus was chosen because it presents a well-constrained geological setting, with sources of predictable compositions allowing articulation and testing of detailed predictions. We consider a dataset of zircon composition comprising 11 variables, including age, Ce anomaly, CeN/NdN, DyN/YbN, ΔFMQ, Eu anomaly, ΣHREE/ΣMREE, Hf, Th/U, Ti temperature, and YbN/GdN, from 12 till samples. Variables chosen are expected to fall into one of three categories; showing no, minor, or significant variation associated with Cu mineralization. The Tucker-1 approach successfully deconvolves the multivariate data set into two endmembers which are consistent with derivation either from low Cu-ore potential (Source 1) or potential Cu-ore bearing (Source 2) igneous rocks. The distributions of variables in Source 1 and 2 match empirical data from either non-mineralized or mineralized basement igneous rocks, respectively. This result confirms that decomposition of the petrochronological dataset yields sources whose characteristics can be used to determine the conditions of formation of the transported zircons. Furthermore, we demonstrate that the proportions of the potential Cu-bearing source (Source 2) decrease with increasing distance from the ore bodies, as expected due to down-ice or off-axis zircon mixing and dilution. This suggests that the estimated proportions matrix captures geologically meaningful sediment transport and dilution processes. We conclude that the Tucker-1 decomposition approach provides a method of characterizing sources’ petrochronology as well as sediment transport processes even when sources are unknown. It thus provides a basis for future petrochronological interpretations with applied (i.e., Cu exploration) and pure (i.e., tectonic reconstructions) geoscience applications.
Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025
© Copyright 2025 The Geological Society of America (GSA), all rights reserved.
Petrochronological Characterization of Sediment Sources Using Tensor analysis of Multivariate Detrital Zircon Data
Category
Topical Sessions
Description
Session Format: Oral
Presentation Date: 10/19/2025
Presentation Start Time: 10:25 AM
Presentation Room: HGCC, 304C
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