Combining geostatistics and physically-based simulations to characterize contaminated soils

<p>Characterization of contamination in soils resulting from nuclear or industrial activities is a crucial issue for site remediation. A classical approach consists in delineating the contaminated zones based on a geostatistical estimation calibrated from measured activities, but it results in high uncertainties when the number of measurements is low and/or the spatial variability of the studied variable is governed by complex processes. In order to reduce these uncertainties, a novel approach, called Kriging with Numerical Variogram (KNV), is developed: the variogram is computed from a set of physically-based flow-and-transport simulations rather than from the measurements.</p><p>The KNV approach is assessed on a two-dimensional synthetic reference test case reproducing the migration of a tritium plume within an unsaturated soil with hydraulic properties highly variable in space. The results show that the mean absolute error in estimated activities is 50% to 75% lower with KNV compared to classical geostatistical approaches, depending on the sampling scenario. Moreover, KNV leads to a significant reduction of the empirical error standard deviation, which reflects uncertainties on the estimated activities. The performance of KNV regarding the classification into contaminated or not-contaminated zones is yet sensitive to the contamination threshold.</p><p>The KNV approach could thus help to better estimate volumes of soils to be decontaminated in the context of remediation of nuclear or industrial sites. This approach can be transposed to other scales of heterogeneities, such as systems with several geological units, or other pollutants with a more complex chemical behavior, as soon as a numerical code that simulates the phenomenon under study is available.</p><p><em>This study is part of Kri-Terres project, supported by the French National Radioactive Waste Management Agency (Andra) under the “Investments for the Future” national program.</em></p>

Application of geostatistics to complete uranium resources estimation of Rabau Hulu Sector, Kalan, West Kalimantan

Kalan is one of the focus areas for uranium exploration in West Borneo that conducted by BATAN. Situated in the central part of Kalan, previous works in Rabau Hulu Sector consisted of surface geology and radiometric anomaly mapping, trenching, drilling, logging, and conventional uranium resource estimation. Nevertheless, the complete resource estimation of the previous work was still using 2D modeling, and the latest one using 3D modeling is a method-application case study in one orebody. To increase the confidence level and completing the uranium resource estimation of all orebodies in this sector, a geostatistical estimation with 3D orebody modeling using SURPAC mine planning software was conducted in this paper. Gamma-ray log data from 32 drill holes were collected and then interpreted to obtain uranium grade-thickness data. Based on the correlation of grade-thickness data according to surface orebody orientation, the orebody 3D modeling was done. It resulted in 26 orebodies with one control system of lithology as the mineralization only taken place in the quartzite unit. This 3D model then used as a constraint for block model with 4x4x2 m block size and 0.25x0.25x0.125 m minimum block size. Block model calculation was performed using ordinary kriging which generated the kriging efficiency attribute for the determination of the resource category. Within 25 meters searching radius, the calculation resulted in 408, 480 tons of ore, while total uranium resource was 268 tons of uranium with 677 ppm average grade. There were 214 tons of uranium (79%) categorized as measured while the other 54 tons of uranium (21%) categorized as indicated.

Geostatistical Estimation of Multi-Domain Deposits with Transitional Boundaries: A Sensitivity Study for the Sechahun Iron Mine

In mineral resource estimation, identification of the geological domains to be used for modeling, and the type of boundaries dividing them, is a major concern. Generally, the variables within a domain are estimated with an assumption of the hard boundaries (sharp contact). However, in many cases, the geologic structures that generate a deposit are transitional (overlapping of several geologic domains). Consequently, boundary identification of the geological domains is essential for an accurate estimate of resources. This paper considers a real application to examine whether the addition of geologic information benefits grade estimation in the presence of transitional boundaries. Results proved that the accuracy of the grade estimation can be improved by adding geological information and there is a significant sensitivity in grade estimation results in the existence of transitional boundaries.