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UBC Theses and Dissertations

Incorporating geophysics in the hydrogeological decision-making process Clayton, Edward Andrew

Abstract

A risk-based economic decision making framework for hydrogeology-related engineering problems was developed, and tested in a real-world case study, for incorporating geophysics in a probabilistic site conceptual model that is directly linked to a decision analysis model. The framework employs a flexible, practical geostatistical methodology that quantitatively accounts for uncertainty associated with measurement inaccuracy and spatial variability. Two types of decision analyses are performed: (1) all types of existing information are integrated in the site conceptual model and the most cost-effective engineering design is determined, accounting for risk costs associated with uncertainty, and (2) the expected future economic worth of different measurements, including geophysics, to the overall problem is evaluated. Site characterization is often one of the most important and expensive activities in hydrogeology-related engineering problems, having a large impact on the engineering solution and total cost. Thus, this activity should be carried out in the most effective way possible—providing the most worthwhile information at the lowest possible cost. The worth of information is defined by how much the overall economic value of the engineering solution is improved, including reduction in costs associated with the risk of engineering failure. Most conventional characterization techniques involve invasive sampling or testing of the subsurface, sampling only a small fraction of the overall site. Geophysics measurements are non-invasive, have a much larger spatial coverage, can be acquired at a much higher density, and are relatively inexpensive. However, geophysics measurements usually do not measure the properties of interest in these problems, and are often plagued by noise and other uncertainties that limit quantitative usage of the data. These measurements could be a very valuable site characterization technique if they could be quantitatively incorporated in the decision making process, in a way that accounts for their inherent uncertainty. The Markov-Bayes indicator geostatistics methodology [Alabert, 1987; Zhu and Journel, 1993] provides a versatile and straightforward approach for incorporating geophysics, and any other indirect or direct site characterization measurement of the property of interest, in a probabilistic spatial model of the property. The uncertainty associated with the indirect measurements is determined through a simple calibration between collocated indirect and direct measurements, and accounted for in the probabilistic model using indicator cokriging. The output of the model is either (1) a set of probability of class membership maps for defined interval classes of the property or (2) a set of equally-likely realizations of class membership, or actual values, of the property. The methodology was adapted and expanded to a comprehensive set of routines, optimized for incorporating geophysics data in the uncertainty model and easily linking the model to a decision analysis model. A real world case study involving soil contamination remediation was performed to test the integrity and practicality of the developed Markov-Bayes uncertainty framework, and its ability to incorporate actual geophysics measurements. The case study site requires excavation and selective treatment of soil contaminated above specified action levels, with a penalty cost for underclassifying or over-classifying soil contamination. A soil remediation design and data worth analysis decision model, closely linked to the uncertainty framework, was developed for the problem. The expected value optimal remediation design was determined based on the information provided by soil sample data. A sensitivity analysis was also performed—for a range of contamination underclassification unit costs and different real-time sampling alternatives. The optimal remediation designs require excavation of almost the entire site and range in total cost from $4.5 to $9 million Canadian dollars, the higher cost designs corresponding to the scenarios where real-time sampling is not an option and the underclassification costs are substantially higher than when contamination is correctly classified. When excavation and real-time batch sampling, followed by appropriate treatment, is an alternative, it is the optimal action for most of the site—since it eliminates risk costs. A data worth analysis was also performed to evaluate the worth of additional soil sampling versus geophysics surveys of different data quality levels. The results show that soil sampling—from 20 boreholes evenly spaced across the site—provides negligible worth to the site remediation. The same is true for geophysics, except for the scenario where the geophysics is of very good quality for delineating contamination (greater than 80% probability of correctly identifying the highest contamination levels) and the surveys cover most of the site. Even this scenario provides little value unless the underclassification cost is greater than two times the correct classification cost. Ground penetrating radar (GPR) and frequency domain electromagnetics (FDEM) surveys were acquired as an indirect measurement of hydrocarbon and metals contamination, and the processed data was incorporated in the decision model to evaluate the change in the optimized remediation design. Combining the results from both surveys in the Markov-Bayes uncertainty model produces a significant change in the probability of contamination maps in the region where the surveys were performed. However, the geophysics provides little improvement to the overall design and, consequently, little reduction in total cost. This result is anticipated from the data worth analysis, since the survey covered only a small portion of the overall site. The case study results illustrate that the developed Markov-Bayes uncertainty framework can be effectively employed in hydrogeology-related problems to (1) evaluate the economic worth of geophysics and (2) incorporate geophysics data in a risk-based decision model. While the geophysics acquired in this study produced little value to the decision making in this particular problem, the integration of the "soft" results from both the GPR and FDEM surveys with "hard" soil sample data had a significant impact on the contamination probability model in the region of the surveys. This suggests that using geophysics within the Markov-Bayes uncertainty framework could provide a promising technique for indirectly measuring hydrogeological properties—providing much greater spatial coverage than conventional sampling techniques.

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