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Session
Rock Mass Characterization and Data Uncertainty IV, Finger Rock Room
Abstract Summary
The Large Open Pit project (LOP3) commissioned the development of a white paper that provides structural geologists and geotechnical engineers working in open pit mines with guidance on the creation and evaluation of 3-D structural geology models. 3-D structural geology models often guide the input parameters or provide direct input such as contact lines or wireframes for use in slope design analytical software used by engineers. The creation of 3-D lithological and alteration models are also addressed, particularly in the context of the most reliable model being the product of an integrated structural, lithological, and alteration model. This paper is intended to supplement the related chapters in Guidelines for Open Pit Slope Design (Read and Stacey, 2009).
Geology requires a strong interpretative component, involving the evaluation of geological observational data. This subjectivity influences all downstream analytical and interpretive processes. However, the geologists creating the models seldom understand how the models influence geotechnical design decisions and the engineers seldom appreciate the complexity of the geology and the simplifications, assumptions, and errors in the 3-D models.
This paper discusses the objectives, challenges, and solutions for building appropriate 3-D structural and geological models from data, including factors that are important to geotechnical engineers. The paper also addresses the concept of reliability of the models in practical open pit engineering terms. The concepts of uncertainty are explored with specific reference to all the parameters and factors that are included in a structural and/or geological interpretation, with the aim of highlighting the key factors that should be more closely reviewed and measured, if possible. A method to create a Reliability Index appropriate for case study open pits is also explored. Three open pit case studies are presented, for each of which a model reliability index is calculated.
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