Investigation of Surface Exploration Programs for Hydrological, Hydrogeological and Hydrogeochemical Issues in the Site Selection Procedure

The project “Investigation of Surface Exploration Programmes for Hydrological, Hydrogeological and Hydrogeochemical Issues in the Site Selection Procedure” summarizes the measurement methods that can be used for surface exploration of site regions and compares them with the measurement parameters as given in the Repository Site Selection Act (StandAG). Based on this, an orientation framework was developed that can support the evaluation of actual exploration programs. This project is limited to those measurement parameters that result from §§ 22–24 in conjunction with the Annexes 1–11 of the StandAG for the surface exploration of hydrological, hydrogeological and hydrogeochemical characteristics of site regions. In a first step, definitions of terms which are subject to interpretation were identified in the StandAG and advice was provided on how to deal with them and, in some cases, justified proposals for interpretation within the framework of this project were given. As a result, hydrological, hydrogeological and hydrogeochemical parameters were presented that were derived directly from the StandAG, as well as those that are not explicitly mentioned in the StandAG but are necessary for a detailed characterization of the rock formations in a site region. The next step was to identify measurement methods to be used for surface exploration of hydrological, hydrogeological and hydrogeochemical parameters. In particular, hydrogeologic and (borehole) geophysical methods were considered in the context of field measurements and laboratory investigations as well as field and laboratory tests for the determination of hydrogeochemical parameters. These measuring methods were described with respect to their measuring principle, the respective limits, the technical effort as well as the advantages and disadvantages of their application. Possible combinations with other measuring methods as well as a potential need for additional research and development for use in surface exploration programs were also presented. Considering the boundary conditions and dependencies for different rock types and necessary exploration depths, the measuring methods were assigned to the relevant parameters with respect to their applicability. In most cases, several methods are suitable for the determination of a specific parameter. Combining different physically independent methods can limit the range of variation of the measured variables and significantly increase the reliability of the results. The resulting compilation of measurement methods was used in the last step to establish an orientation framework. This framework contains the essential requirements for a complete documentation and quality assurance of the measurements and sampling and derives fundamental factors influencing the quality and quantity of the parameters. The orientation framework also refers to various factors that may influence the selection of suitable measuring methods for the surface exploration of the parameters as well as the determination of appropriate measuring network densities and measurement intervals. The measurement network densities selected at the beginning of the exploration are usually adjusted based on increasing knowledge of the site-specific geological conditions. This step-by-step procedure, which has proven successful in geological exploration programs, was also emphasized in the orientation framework, as it gradually increases the level of knowledge, the level of detail and thus the reliability of the measurement results.

Using an Extreme Gradient Boosting Learner for Mapping Hydrogeochemical Parameters in Germany

<p>Information on the spatial distribution of hydrogeochemical parameters is crucial for decision making. Machine learning based methods for the mapping of hydrogeochemical parameter concentrations have been already studied for many years to evolve from deterministic and geostatistical interpolation methods. However, the reflection of all relevant processes that the target variable depends on is often difficult to achieve, because of the mostly insufficient determination and/or availability of features. This is especially true if you limit yourself to freely accessible data.</p><p>In this study, we apply an extreme gradient boosting learner (XGB) to map major ion concentrations across Germany. The training data consist of water samples from approximately 50K observation wells across Germany and a wide range of environmental data as predictors. The water samples were collected between the 1950s and 2005 at anthropogenically undisturbed locations.</p><p>The environmental data includes hydrogeological units and parameters, soil type, lithology, digital elevation model (DEM) and DEM derived parameters etc. The values of these features at the respective water sample location were extracted on the basis of a polygon, approximately representing the area that has an impact on the target variable (ion concentration). For a comparison, different polygon shapes are used.</p><p>The model was set up as chained multioutput regression, meaning that the prediction of the previous model in a linear sequence of single-output models is used as input for the subsequent model.</p><p>The results are planned to serve for a comparison with state-of-the-art deep learning architectures.</p>

Sources, trends, and fate of methane in shallow aquifers of Alberta, Canada

<p>Due to concerns regarding potential impacts of the development of natural gas from unconventional hydrocarbon resources on groundwater systems in North America and elsewhere, it has been crucial to improve methods of Environmental Baseline Assessment (EBA). Any subsequent deviations from the EBA could indicate migration of natural gas into the monitored groundwater systems. In collaboration with Alberta Environment and Parks, over 800 groundwater samples have been collected from dedicated monitoring wells since 2006 resulting in an extensive high-quality database of aqueous and gaseous geochemical and isotopic compositions. Because methane is the main component of natural gas, it had been the principal target of our groundwater studies. Our objectives were a) to assess the occurrence of methane in groundwater throughout the province of Alberta (Canada), b) to use isotope techniques to track the predominant sources of methane, c) to use a combination of chemical and multi-isotopic techniques and models to assess the fate of methane in groundwater, and d) to use probability for predicting the presence of methane in groundwater based on hydrogeochemical parameters in regions where no gas data exist.</p><p>Methane was found to be ubiquitous in groundwater samples throughout the province of Alberta with concentrations varying from 2.9 10<sup>-4</sup> to >2.4 mmol/l. The highest methane concentrations were found in Na-HCO<sub>3</sub> and Na-Cl water-types where the sulfate concentrations were <1 mmol/l. Analyses of the isotopic compositions of sulfate, dissolved inorganic carbon (DIC) and methane revealed that in some groundwater systems bacterial sulfate reduction occurred (δ<sup>34</sup>S<sub>SO4</sub> >+10‰ associated with lowest sulfate concentrations) and evidence for methane oxidation was also detected (highest δ<sup>13</sup>C<sub>CH4</sub> values > ‑55‰ associated with lowest methane concentrations). Moreover, some δ<sup>13</sup>C<sub>DIC</sub> values were as high as +13.8‰ associated with the highest methane concentrations. A geochemical and multi-isotope model using long-term monitoring data was developed and revealed two different sources of methane: 1) microbial methane resulting from in-situ methanogenesis within the aquifer for a subset of the samples; 2) migration of microbial methane into aquifers characterized by various redox conditions, followed by methane oxidation potentially coupled with bacterial sulfate reduction within sulfate-rich zones causing a pseudo-thermogenic carbon isotopic fingerprint for the remaining methane. So far, no evidence of unambiguously thermogenic methane in the groundwater samples collected from dedicated monitoring wells has been found. Efforts to assess the probability of regional occurrence of methane in groundwater systems in Alberta have then focused on a model for methane prediction model based on logistic regression (LR) for regions of Alberta where no gas data exist. Using basic hydrogeochemical parameters such as occurrence of electron donors, well depth and total dissolved solids of groundwater, the LR approach shows excellent performance metrics e.g. model sensitivity, specificity >80% regarding the prediction of methane occurrence in groundwater of Alberta.</p>

Seawater intrusion due to pumping mitigated by natural freshwater flux: a case study in Władysławowo, northern Poland

The paper presents a case study of seawater intrusion into a coastal aquifer, caused by a groundwater intake located close to the seashore in Władysławowo, northern Poland. Evolution of the basic hydrogeochemical parameters for the 50-year period from 1964 to 2014 indicates progressing encroachment of saline seawater into the aquifer. However, the spatial pattern of salinity was influenced by the variability of hydraulic gradient in fresh water discharging from aquifer to the sea. As a consequence, significant changes in salinity occurred in the directions both perpendicular and parallel to the coastline.

CHEMICAL COMPOSITION OF GROUNDWATER OF THE COLTOGOR- TOLKINSKAYA ZONE AND ADJACENT TECTONIC ELEMENTS

The comparison of hydrogeochemical conditions of Jurassic-Cretaceous sediments of Koltogor- Tolkinskaya suture zone and the adjacent tectonic structures was based on the results of the chemical composition analysis of almost 1,500 groundwater samples. The average values of the overall salinity of subsurface water and their content of basic components of ion-salt and microcomponent composition were compared for Aptian-Albian-Senomanian, Neocomian and Jurassic aquifer complexes of Koltogorsky, Tolkinsky, Yuzhno-Tolkinsky megatroughs and Tagrinsky, Bakhilovsky, Alexandrovsky, Srednevasyugansky megaswells, as well as Nizhnevartovsky, Kaymysovsky arches. Using Sulin’s classification and cluster analysis the type of groundwater was characterized by the hydrogeochemical parameters set. It was defined that the suture zone presence does not directly affect the hydrogeochemical conditions of deep horizons of this zone and adjacent areas.

Evaluation of Groundwater for Arsenic Contamination Using Hydrogeochemical Properties and Multivariate Statistical Methods in Saudi Arabia

The aim of this research is to evaluate arsenic distribution and associated hydrogeochemical parameters in 27 randomly selected boreholes representing aquifers in the Al-Kharj geothermal fields of Saudi Arabia. Arsenic was detected at all sites, with 92.5% of boreholes yielding concentrations above the WHO permissible limit of 10 μg/L. The maximum concentration recorded was 122 μg/L (SD = 29 μg/L skewness = 1.87). The groundwater types were mainly Ca+2-Mg+2-SO4-2-Cl−and Na+-Cl−-SO4-2, accounting for 67% of the total composition. Principal component analysis (PCA) showed that the main source of arsenic release was geothermal in nature and was linked to processes similar to those involved in the release of boron. The PCA yielded five components, which accounted for 44.1%, 17.0%, 10.1%, 08.4%, and 06.5% of the total variance. The first component had positive loadings for arsenic and boron along with other hydrogeochemical parameters, indicating the primary sources of As mobilization are derived from regional geothermal systems and weathering of minerals. The remaining principal components indicated reductive dissolution of iron oxyhydroxides as a possible mechanism. Spatial evaluation of the PCA results indicated that this secondary mechanism of arsenic mobilization may be active and correlates positively with total organic carbon. The aquifers were found to be contaminated to a high degree with organic carbon ranging from 0.57 mg/L to 21.42 mg/L and showed high concentrations ofNO3-ranging from 8.05 mg/L to 248.2 mg/L.