Mineral resource modelling using an unequal sampling pattern: An improved practice based on factorization techniques

This work addresses the problem of geostatistical simulation of cross-correlated variables by factorization approaches in the case when the sampling pattern is unequal. A solution is presented, based on a Co-Gibbs sampler algorithm, by which the missing values can be imputed. In this algorithm, a heterotopic simple cokriging approach is introduced to take into account the cross-dependency of the undersampled variable with the secondary variable that is more available over the entire region. A real gold deposit is employed to test the algorithm. The imputation results are compared with other Gibbs sampler techniques for which simple cokriging and simple kriging are used. The results show that heterotopic simple cokriging outperforms the other two techniques. The imputed values are then employed for the purpose of resource estimation by using principal component analysis (PCA) as a factorization technique, and the output compared with traditional factorization approaches where the heterotopic part of the data is removed. Comparison of the results of these two techniques shows that the latter leads to substantial losses of important information in the case of an unequal sampling pattern, while the former is capable of reproducing better recovery functions.

1055 Performance of Gastric Bypass vs. Sleeve Gastrectomy in Hypertension Remission

Aim Bariatric surgery is gradually becoming a surgical field of paramount importance to global health. Our aim is to assess the performance of RYGB and SG in achieving remission of hypertension in bariatric patients. Secondarily, we aim to assess how age, gender, referral BMI, severity of hypertension, and association with T2DM, affects hypertension remission rates. Method In this observational, retrospective cohort study, we included 475 out of 505 total bariatric patients operated at the Worcestershire Royal Hospital between 2012 and 2019. Overall, 193 patients (40.6%) where taking anti-hypertensive medications pre-operatively. Hypertensive patients were divided into three categories: Mild (1 anti-hypertensive medication) 44%, moderate (2 anti-hypertensive medications) 39%, and severe (3 or more anti-hypertensive medications) 17%. All patients underwent either a RYGB 52% (101/193) or a SG 48% (92/193). We assessed hypertension remission after 1 and after 2 years. Results Hypertension remission rates post-RYGB where 40.0% after 1 year (38/95), and 43.0% (34/79) after 2 years. Rates post-SG where 40.8% after 1 year (31/76) and 43.1% (22/51) after 2 years. There was no statistically significant difference in hypertension remission rates between RYGB and SG, nor with any of secondary variable, including gender, age, BMI, severity of hypertension and association with T2DM. Conclusions Our data showed no significant difference between RYGB and SG in hypertension remission rates after 1- and 2-years post-procedure. This provides novel insights into the risk-benefit assessment of the bariatric patient, and helps define the SG as a much simpler, cheaper and safer surgical option for bariatric patients with hypertension as their major co-morbidity.

Cokriging Prediction Using as Secondary Variable a Functional Random Field with Application in Environmental Pollution

Cokriging is a geostatistical technique that is used for spatial prediction when realizations of a random field are available. If a secondary variable is cross-correlated with the primary variable, both variables may be employed for prediction by means of cokriging. In this work, we propose a predictive model that is based on cokriging when the secondary variable is functional. As in the ordinary cokriging, a co-regionalized linear model is needed in order to estimate the corresponding auto-correlations and cross-correlations. The proposed model is utilized for predicting the environmental pollution of particulate matter when considering wind speed curves as functional secondary variable.

Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling

SUMMARY Bayesian sequential simulation (BSS) is a geostastistical technique, which uses a secondary variable to guide the stochastic simulation of a primary variable. As such, BSS has proven significant promise for the integration of disparate hydrogeophysical data sets characterized by vastly differing spatial coverage and resolution of the primary and secondary variables. An inherent limitation of BSS is its tendency to underestimate the variance of the simulated fields due to the smooth nature of the secondary variable. Indeed, in its classical form, the method is unable to account for this smoothness because it assumes independence of the secondary variable with regard to neighbouring values of the primary variable. To overcome this limitation, we have modified the Bayesian updating with a log-linear pooling approach, which allows us to account for the inherent interdependence between the primary and the secondary variables by adding exponential weights to the corresponding probabilities. The proposed method is tested on a pertinent synthetic hydrogeophysical data set consisting of surface-based electrical resistivity tomography (ERT) data and local borehole measurements of the hydraulic conductivity. Our results show that, compared to classical BSS, the proposed log-linear pooling method using equal constant weights for the primary and secondary variables enhances the reproduction of the spatial statistics of the stochastic realizations, while maintaining a faithful correspondence with the geophysical data. Significant additional improvements can be achieved by optimizing the choice of these constant weights. We also explore a dynamic adaptation of the weights during the course of the simulation process, which provides valuable insights into the optimal parametrization of the proposed log-linear pooling approach. The results corroborate the strategy of selectively emphasizing the probabilities of the secondary and primary variables at the very beginning and for the remainder of the simulation process, respectively.

The first version of the Pan-European Indoor Radon Map

A hypothetical Pan-European Indoor Radon Map has been developed using summary statistics estimated from 1.2 million indoor radon samples. In this study we have used the arithmetic mean (AM) over grid cells of 10 km × 10 km to predict a mean indoor radon concentration at ground-floor level of buildings in the grid cells where no or few data (N<30) are available. Four interpolation techniques have been tested: inverse distance weighting (IDW), ordinary kriging (OK), collocated cokriging with uranium concentration as a secondary variable (CCK), and regression kriging with topsoil geochemistry and bedrock geology as secondary variables (RK). Cross-validation exercises have been carried out to assess the uncertainties associated with each method. Of the four methods tested, RK has proven to be the best one for predicting mean indoor radon concentrations; and by combining the RK predictions with the AM of the grids with 30 or more measurements, a Pan-European Indoor Radon Map has been produced. This map represents a first step towards a European radon exposure map and, in the future, a radon dose map.

Analysis of Strategic Variables for Ecotourism Development; an Application of Micmac

This study aims to determine strategic factors and its relationship in developing ecotourism areas. This research uses prospective structural approach. The analytical method uses Micmac to identify the most influential variables and its relation to Kedung Ombo ecotourism development. The results of analysis which realize the typology of strategic variables based on the strength of influence found 6 classifications of variable, namely: 1. The dominant variable consists of: regulation, and governance; 2. Key variable (Relay variable) consists of: institutional coordination, apparatus role, tourism marketing and tourism promotions. 3. Autonomous variable consists of: natural beauty, accessibility, potential of market tourism, local awareness of tourism. 4. The output variable consists of: funds for community, preservation of local wisdom, preservation of forest sustainability, conservation of reservoir function. 5. Regulators variable consists of: special permit policy for investment, retribution policy, tax policy, allowance policy, tourist attractions. 6. Secondary variable consists of: availability of tourism infrastructure, tourist interest of ecotourism, involvement of local community. The results of this study show a variety of information sources for policy makers in Kedung Ombo ecotourism development in a sustainable manner.

First steps towards an All-European Indoor Radon Map

A hypothetical All-European Indoor Radon Map has been developed using summary statistics estimated from 1.2 million indoor radon samples. In this study we have used the arithmetic mean (AM) over grid cells of 10 km × 10 km to predict a mean indoor radon concentration at ground floor level in the grid cells where no or few data are available (N < 30). Four interpolation techniques have been tested: inverse distance weighted (IDW); ordinary kriging (OK); collocated cokriging with uranium concentration as secondary variable (CoCK); and regression kriging with topsoil geochemistry and bedrock geology as secondary variables (RK). Cross-validation exercises have been carried out to assess the uncertainties associated with each method. Of the four methods tested, RK has proved to be the best one for predicting mean indoor radon concentrations, and by combining the RK predictions with the AM of the grids with 30 or more measurements, an All-European Indoor Radon Map has been produced. This map represents the first step towards a European radon exposure, and further on a radon dose map.

GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.