Geological Variables Research Articles

Machine learning approach for core permeability prediction from well logs in Sandstone Reservoir, Mediterranean Sea, Egypt

Precise reservoir description is crucial for the proper evaluation of reservoirs. The ability to predict permeability is key to successful reservoir characterization. Although obtaining permeability values through a reservoir is crucial, this is not a simple task. This requires a significant amount of time and money. Numerous methods use the core correlations documented in the literature. Well-log information that offers ways to determine permeability. In this study, we describe a machine learning-based permeability prediction method. With log and core (data), Generalized Additive Models (GAMs) allow for the expansion of the forecasts for(uncored)wells.It also considers the characteristics of the field to ensure a better comprehension of reservoirs, reservoir rock characteristics, and geological variations. We demonstrate the utility of generalized additive models (GAMs), a non-parametric regression-based technique, to account for nonlinear trends in seven wells located in the (WDDM) concession where data analysis is performed on the collected information to evaluate the model's performance. Both the linear and nonlinear functions were used to train the data. The findings of the analysis demonstrate that GAMs outperform segmented linear regression models when the trend is nonlinear, but they also demonstrate their effectiveness when the trend is linear. The GAMs used five wells for training and two blind wells for testing. The GAMs with all five wells used as input, were found to perform best in predicting permeability for the shaly sandstone, with coefficients of determination (Pseudo-R2) of approximately 0.98 and 0.82 for the training and blind data sets, respectively.

Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments.

Numerical investigation of a district scale groundwater heat pump system: A case study from Colchester, UK

Nearly 40% of Europe’s total energy consumption is dedicated to buildings and heating/cooling make a significant part of this consumption. Groundwater heat pumps (GWHP) are highly efficient, and low-carbon technology that can supply heating/cooling to buildings on small or large scales. Thus, they contribute to achieving European targets of net-zero greenhouse gas emissions by 2050. In the literature, studies on the utilisation of GWHP at a district scale, particularly in chalk aquifers, are relatively rare. The implementation of district-scale geothermal heat pump (GWHP) systems poses several challenges, including dealing with the scale and complexity of the systems, addressing geological variability, managing high initial investments, balancing energy demand and supply, ensuring proper maintenance and monitoring, and mitigating potential environmental impacts. These challenges require careful consideration and strategic planning to ensure the successful deployment and sustainable operation of these systems., This study numerically investigates a district-scale GWHP system and analyses the thermal plume development created due to the heating operation, offering insights into system performance. A good match was found between field results and simulation results for water level increase and drawdown. However, there is a difference of approximately 11% in system efficiency between field tests and simulations due to the lower abstraction temperature detected in the simulation. The simulation results show that cooler water injection into the fractured chalk aquifer creates a thermal plume radially spanning out to 50 m. The thermal plume has no effect on the abstraction temperature and system performance. This result can be attributed to the large distance between injection and abstraction wells and the low hydraulic gradient.

Deep learning prediction of waterflooding-based alteration of reservoir hydraulic flow unit

The hydraulic flow unit (HFU) is a comprehensive representation of reservoir quality, and the type of reservoir HFU alters dramatically due to the impact of waterflooding. The accurate prediction of HFU characteristic alterations based-waterflooding is crucial but difficult because the alterations are influenced by many factors like geological conditions and production history. In this study, we propose a deep learning-based method using both static geological data and dynamic production history data to predict HFU characteristic alterations waterflooding-based. We classify six HFU types using the k-means clustering of geological variables, including the flow zone indicator (FZI), permeability, and shale volume. The deep-learning model uses the initial static geological variables, including FZI, permeability, and shale volume, and dynamic production data as inputs, whereas the HFU type after waterflooding is used as the target. Convolutional neural networks (CNNs) are adopted for the prediction of HFU characteristic alterations and also compared with support vector machines (SVM) and back propagation neural networks (BPNNs). The prediction accuracy of the CNN model is 93.3% and 87.1% for the validation and testing datasets, respectively, which performs much better than other machine learning methods. In addition, we explore the factors affecting the performance of different models. CNN model can extract useful information from complicated production history data more effectively than other machine learning methods. Finally, the trained CNN model is used to predict the spatial distribution of HFU at different development stages in the study area. In summary, the proposed method can accurately predict dynamic alterations in the reservoir HFU type after waterflooding, which is very useful for the adjustment of oilfield production measures.

The composition and provenance of Late Bronze Age vitreous materials from Kefalonia, Greece

In this study, we present the results of the analysis of Late Bronze Age (LBA) vitreous materials from three cemeteries on the island of Kefalonia, Greece (Mazarakata, Metaxata, and Kokkolata-Menegata). A multianalytical approach (EPMA, LA-ICP-MS, MC-ICP-MS [Sr-Nd]) was used to determine the composition and provenance of a group of 14 glass beads, a glass fragment, and a faience bead, all dating to the 14th-11th centuries BCE. Most of the beads were produced using a plant ash alkali with one having a mixed alkali composition. Identification of the colorants revealed that the blue glasses were colored using Cu, Co-Cu, or Co. Cobaltiferous alum from the Western Desert of Egypt imparted the blue color to the cobalt containing glasses. Trace element and isotopic analysis showed that all the relief plaques were made using imported Egyptian glass. The isotopic signature of one relief plaque, a triple rosette motif unique to Kefalonia (K13-4), suggests that there may be greater geological variation among the currently identified LBA Egyptian silica and alkali sources or possibly the use of new raw material sources in the region. Silica and plant ashes from different regions were exploited for the manufacture of two beads of Near Eastern origin. One bead (M18-3) was made from an alkali derived from plants likely growing in river valleys in zones MP-1 (northern Iraq) or MP-3 (southern Iraq) and silica from zone MP-2 (deserts in western Iraq/eastern Syria). The other bead was unique in composition characterized by high chromium, silica from northern Iraq (zone MP-1 N), and plant ash from the desert (zone MP-2). Similarities to raw materials used to produce glass at Alalakh may tentatively suggest a similar origin. A Mycenaean Group B faience bead (Co-Cu blue) included in the study was manufactured with an alkali and a source of cobalt that did not come from the Western Desert. Further identification of the alkali type and the source of the raw materials for this faience bead was not possible but similarities were found to Group B beads from Psara and Mesara in Greece.

Geology constrains biomineralization expression and functional trait distribution in the Mountainsnails

AbstractAimGeographic variation in metabolic resources necessary for functional trait expression can set limits on species distributions. For species that need to produce and maintain biomineralized traits for survival, spatial variation in mineral macronutrients may constrain species distributions by limiting the expression of biomineralized traits. Here, we examine whether Oreohelix land snails that express heavily biomineralized shell ornaments are restricted to CaCO3 rock regions, if they incorporate greater amounts of CaCO3 rock carbon in their shell than less biomineralized smooth forms, and if ornamentation increases shell strength.LocationWestern United States.TaxonOreohelix land snails.MethodsWe used random forest classification models at multiple spatial resolutions to evaluate the contribution of topographic, vegetation, climate, and geologic variables in predicting the presence of heavily biomineralized shell ornaments across the range of Oreohelix. We then measured and compared shell biometric variables, 14C/12C ratios, and peak force for fracture for ornamented and smooth forms from calcareous and non‐calcareous substrates.ResultsDistance to CaCO3 rock was the most important variable in all models and closer proximity to CaCO3 rock was associated with greater probability of local ornamentation classification. Pairwise comparisons of 14C/12C ratios in closely occurring ornamented vs. smooth population pairs revealed ornamented forms incorporate greater CaCO3 rock carbon than smooth forms. Ornamented types measured in this study were generally heavier and required greater peak force for fracture than smooth forms, except when comparing ornamented forms to smooth forms sampled from CaCO3 rock.Main ConclusionsBiomineralization expression, species distribution, and trait function appear to be constrained by mineral supply in a highly diverse group of land snails. This trait‐environment relationship provides a basis for future investigations of CaCO3 macronutrient constraints on shell form and species distribution in other terrestrial molluscs and has a direct impact on the management of Oreohelix species.

Snowpack and groundwater recharge in the Atlas mountains: New evidence and key drivers

Study regionThe Atlas Mountains of Morocco, specifically the High Oum Er-rbiaa (HOER) and Ourika catchments. Study focusto identify the recharge processes within the semi-arid watersheds, in the Atlas Mountains, through monthly monitoring of snow, rainfall, surface water, and groundwater isotope signal, but also the usage of remote sensing data. New hydrological insights for the regionThe spatial-temporal analysis of groundwater and precipitation isotopes reveals significant spatial heterogeneity, primarily influenced by the geological variations in each aquifer. Temporal variations indicate that direct recharge occurs in response to winter precipitation, whereas a delayed response is observed during the summer when snow replenishes groundwater towards the end of the melting season. The findings are further supported by the "Gravity Recovery and Climate Experiment" (GRACE) dataset, which demonstrates that high values of Total Water Storage (TWS) align with groundwater isotopes. This highlights the substantial groundwater abstraction rate between March and June to compensate for the lack of precipitation during this period. The analysis of isotope data indicates that 50% of groundwater recharge in the upstream Jurassic aquifer and 80% in the downstream Triassic-Paleozoic aquifers in the HOER catchment is sourced from snowmelt. Similarly, in the Ourika catchment, snowmelt contributes 30% and 50% of groundwater recharge in the upstream and downstream portions of the catchments respectively. This disparity is due to different melting rates across altitudinal ranges and variations in the lithology of each catchment.

Effect of geologic variability on occurrence of collapse and natural vegetation recovery on cutting slopes of a forestry-exclusive road

Effect of geologic variability on occurrence of collapse and natural vegetation recovery on cutting slopes of a forestry-exclusive road

Natural vs. anthropogenic metals for reconstructing the source-to-sink pathway (Naples Bay, eastern Tyrrhenian Sea)

The grain size, nutrients, and metals contents of sediments are important tracers for reconstructing the origin of the contaminants and the pathway of the sediments from the source to the sink. This is particularly important in areas with high geological variability and a high demographic pressure. The origin and distribution of contaminants and nutrients allow us to identify the source-to-sink pathway of the sediments. In particular, the Sarno River is one of the main contributing sources of anthropogenic contaminants due to the outflow from the large surrounding large industrial area. Geochemical and physical parameters of sediments were analyzed along a transect from the coastline to a water depth of 112 m, with the objective of exploring the spatial variability of sediment contamination in the continental shelf of Naples Bay. The latter is characterized by a complex geological area and is subject to river inflow from a highly industrialized area. The data have been analyzed using principal component analysis (PCA), hierarchical correlation analysis (CA), and analysis of variance (ANOVA). To distinguish between a natural and anthropogenic origin of contaminants and to construct the source-to-sink pathway, the relationship between geochemical and physical data and their distribution was examined, and the results were compared with published data collected onshore. Four distinct sectors with different associations of metals, grain size, and total organic carbon have been recognized: I) offshore Vesuvius volcano, the occurrence of coarse-grained sediments, associated with As, Fe, Mn, and low TOC values, suggests a natural origin of the contaminant due to the volcanic nature of the substrate; II) offshore the Sarno River, the presence of Cr, Cd, Cu, Pb and Zn, associated with the high TOC content, suggests an anthropogenic origin for the contaminants; III) offshore Sorrento Peninsula, the presence of As, Fe, Mn, and some gravel made up of pumice and bioclasts indicate a predominance of contaminants of natural origin; and IV) finally, the distal part is not subjected to a primary terrestrial input. Metals contamination of anthropogenic origin and the organic matter in sediments decreased rapidly with distance from the coast highlighting the main deposition area close to the river discharge.

Machine learning-based Sr isoscape of southern Sardinia: A tool for bio-geographic studies at the Phoenician-Punic site of Nora.

Since prehistoric times, the island of Sardinia-in the western Mediterranean-has played a leading role in the dynamics of human population and mobility, in the circulation of raw materials and artefacts, idioms and customs, of technologies and ideas that have enriched the biological, linguistic and cultural heritage of local groups. For the Phoenician and Punic periods (from the 9th to the 3rd centuries BCE), the ancient site of Nora-in southern Sardinia-represents an emblematic case in the study of migratory phenomena that occurred on the Island from the Iron Age until the Roman conquest. Despite the importance of exploring (and characterising) such movements from a wider bio-cultural perspective, the application of bio-geochemical tools for geographical provenance to the ancient skeletal populations of Sardinia is yet scarce. The present work is the first step towards filling this gap with the development of the first isoscape of southern Sardinia using new bioavailable Sr isotope data and a machine-learning approach. From a geolithological point of view, Sardinia is rather heterogeneous and requires detailed studies to correctly assess the distribution of the isotopic signature of bioavailable Sr. The random forest model employed here to construct the Sr isoscape uses several external environmental and geological variables. The most important predictors are related to age and bedrock type, with additional input from local soil properties. A 10-fold cross-validation gives a mean square error of 0.0008 and an R-squared of 0.81, so the model correctly predicts the 87Sr/86Sr ratio of unknown areas. By using a Bayesian provenance assignment workflow, we tested the isoscape here produced to determine the geographic origin and the mobility of archaeological and modern fauna collected from the Phoenician-Punic site of Nora and the surrounding Pula Plain. Our results indicate that archaeological sheep and goats (87Sr/86Sr < 0.7090) are compatible with areas close to Nora and Pula Plain, in agreement with archaeological evidence of pastoralism in those areas. Modern wild and domesticated fauna (87Sr/86Sr > 0.7090) show compatibility with several natural and anthropogenic locations in southern Sardinia, as expected based on modern species distribution data. Finally, we discuss the large Sr isotopic variability of the Nora baseline, where human mobility studies of human cremated and inhumed individuals are currently underway.

Carbon sequestration potential of process‐based river restoration

AbstractFloodplain restoration can enhance the capacity for carbon sequestration by facilitating higher water tables, deposition of fine sediment, and increased input and residence time of organic matter. We measured floodplain soil organic carbon stocks in nine stream restoration projects across the western United States and compared them to nearby degraded and reference condition floodplains. Degraded floodplains had the lowest soil mean carbon stocks in the majority of floodplains measured (range 161–894 Mg C/ha), and reference stocks had the highest stocks (range 391–904 Mg C/ha) of those with statistically significant differences between the three categories. Across all sites measured, stream restoration sites, referred to as treatment sites, had stocks (range 203–1028 Mg C/ha) similar to degraded condition floodplains but the largest range. When modeled under degraded conditions, four out of nine of the treatment sites had significantly higher OC stocks than predicted. Climate and geologic variables are most influential in predicting carbon stocks, and floodplains in the interior western USA have the highest carbon stocks. As the demand for carbon sequestration increases due to climate change, ecologically responsible floodplain restoration provides a significant opportunity for carbon storage. However, despite the statistically significant relationships we observed in this dataset, the variations within the data in relation to degraded/treatment/reference categories illustrate the uncertainties in quantifying the effects of restoration on floodplain carbon stocks.

High‐Resolution Geological Information from Crosshole Ground Penetrating Radar in Clayey Tills

AbstractHeterogeneous glacial deposits dominate large parts of the Northern Hemisphere. In these landscapes, high‐resolution characterization of the geology is crucial for understanding contaminant transport. Geological information is mostly obtained from multiple boreholes drilled during a site investigation, but such point‐based data alone do not always provide the required resolution to map small‐scale heterogeneity between boreholes. Crosshole ground penetrating radar (GPR) is suggested as a tool for adding credible geological information between boreholes at contaminated site investigations in industrial sites where infrastructure, such as electrical installations, can pose a challenge to other geophysical methods. GPR data are sensitive to the dielectric permittivity and the bulk electrical conductivity, which can be related to the distribution of water content and sand/clay occurrences. Here we present a detailed crosshole GPR dataset collected at an industrial contaminated site in a clay till setting. The data are processed using a novel inversion approach where information on changes in the velocity and attenuation of the radar signal are obtained independently. The GPR results are compared to borehole logs, grain size analyses, and relative permeability data from the site. The GPR data analysis provided valuable information on the understanding of the lateral geological variability. A silt layer with a thickness of a few decimeters, likely important for flow characterization, was confirmed and resolved by GPR data. Our findings suggest that crosshole GPR has the potential for contributing with high‐resolution geological information by filling the data gap between boreholes, thereby becoming a relevant tool in contaminated site investigations.

An Algorithm to Predict Real Estate Price using Machine Learning

Real Estate Pricing has always been a varying from time to time keeping a buyer confuse on how to get exact price of the property at certain point of time. A machine learning algorithm is used to predict the exact price of the property. Predicting the accurate amount of the real estate is very much a matter of concern as people are investing too much now a days in property where a property dealer could easily charge more amount if the buyer is not knowing the market price of the property. The major focus of this research paper is to predict the accurate price of the property without a hassle. Apart from that it also focuses on increasing the accuracy of the already existing system. There is various machine learning algorithm available for prediction such as Naive Bayes, Logistic Regression, Classification, Random Forest KNN, Support Vector Machine, Lasso, Linear Regression etc. The aim of this research is to predict the market value of real estate properties based on geological location. By analyzing previous market patterns, value ranges, and upcoming developments, we can determine a starting price for a property based on geological variables. Our study is applicable to any location, and we have utilized three machine learning algorithms to make predictions. Our findings indicate that linear regression provides the most accurate predictions, with an accuracy rate of 85%. This system eliminates the need for clients to rely on brokers and provides them with the confidence to invest in real estate. The accuracy of our system surpasses that of previous methods used in the industry.

Geostatistical simulations of the spatial variability of hydraulic conductivity in an alluvial-marine sedimentary system in Beihai City, China

Sustainable aquifer management in coastal cities is becoming critically important due to the challenges of climate variability amidst population growth. Study of the aquifer heterogeneity is effective in highlighting the aquifer behavior. In this study, we took the case of Beihai to explore the heterogeneity of its subsurface system. 8629 samples from 111 boreholes were divided into four hydrofacies units as discrete geological variables according to their hydraulic conductivities (K) and used as input datasets for the models. Sequential Indicator Simulation (SISIM) and Transition Probability Geostatistical Simulation (T-PROGS) were employed to construct 3D hydrofacies stochastic models. The simulated results realistically represent horizontal river-driven sediment distribution and vertical deposition processes of K in the aquifer, where the northeastern part is mainly composed of low K and the southern part forms the main multi-layered aquifer system with progressively increasing medium and high K. With a comparative analysis on the ability of capturing geological characteristics using both SISIM and T-PROGS, it highlighted that SISIM reduces initial data requirements without the need of Gaussian transformation and is more broadly applicable, while T-PROGS prefers considering the internal correlation of hydrofacies units and is applicable in environments where lateral accumulation is prevalent. This study demonstrates the credibility of stochastic simulation methods in recreating complex costal sedimentary environments, which provide environment for further groundwater modeling and exploration. Multi-interleaved source data and more accurate algorithms will be integrated in aquifer heterogeneity as subsequent research priorities.

Impact of urban geology on model simulations of shallow groundwater levels and flow paths

Abstract. This study examines the impact of urban geology and spatial discretization on the simulation of shallow groundwater levels and flow paths at the city scale. The study uses an integrated hydrological model based on the MIKE SHE code that couples surface water and 3D groundwater simulations with a leaky sewer system. The effect of the geological configuration was analyzed by applying three geological models to an otherwise identical hydrological model. The effect of spatial discretization was examined by using two different horizontal discretizations for the hydrological models of 50 and 10 m, respectively. The impact of the geological configuration and spatial discretization was analyzed based on model calibration, simulations of high water levels, and particle tracking. The results show that a representation of the subsurface infrastructure, and near-terrain soil types, in the geological model impacts the simulation of the high water levels when the hydrological model is simulated in a 10 m discretization. This was detectable even though the difference between the geological models only occurs in 7 % of the volume of the geological models. When the hydrological model was run in a 50 m horizontal discretization, the impact of the urban geology on the high water levels was smoothed out. Results from particle tracking show that representing the subsurface infrastructure in the hydrological model changed the particles' flow paths and travel time to sinks in both the 50 and 10 m horizontal discretization of the hydrological model. It caused less recharge to deeper aquifers and increased the percentage of particles flowing to saturated-zone drains and leaky sewer pipes. In conclusion, the results indicate that even though the subsurface infrastructure and fill material only occupy a small fraction of the shallow geology, it affects the simulation of local water levels and substantially alters the flow paths. The comparison of the spatial discretization demonstrates that, to simulate this effect, the spatial discretization needs to be of a scale that represents the local variability in the shallow urban geology.

Unravelling the genetic diversity and population structure of common walnut in the Iranian Plateau

BackgroundCommon walnut (Juglans regia L.) has a long cultivation history, given its highly valuable wood and rich nutritious nuts. The Iranian Plateau has been considered as one of the last glaciation refugia and a centre of origin and domestication for the common walnut. However, a prerequisite to conserve or utilize the genetic resources of J. regia in the plateau is a comprehensive evaluation of the genetic diversity that is conspicuously lacking. In this regard, we used 31 polymorphic simple sequence repeat (SSR) markers to delineate the genetic variation and population structure of 508 J. regia individuals among 27 populations from the Iranian Plateau.ResultsThe SSR markers expressed a high level of genetic diversity (HO = 0.438, and HE = 0.437). Genetic differentiation among the populations was moderate (FST = 0.124), and genetic variation within the populations (79%) significantly surpassed among populations (21%). The gene flow (Nm = 1.840) may have remarkably influenced the population genetic structure of J. regia, which can be attributed to anthropological activities and wind dispersal of pollen. The STRUCTURE analysis divided the 27 populations into two main clusters. Comparing the neighbor-joining and principal coordinate analysis dendrograms and the Bayesian STRUCTURE analysis revealed the general agreement between the population subdivisions and the genetic relationships among the populations. However, a few geographically close populations dispersed into different clusters. Further, the low genetic diversity of the Sulaymaniyah (SMR) population of Iraq necessitates urgent conservation by propagation and seedling management or tissue culture methods; additionally, we recommend the indispensable preservation of the Gonabad (RGR) and Arak (AKR) populations in Iran.ConclusionsThese results reflected consistent high geographical affinity of the accession across the plateau. Our findings suggest that gene flow is a driving factor influencing the genetic structure of J. regia populations, whereas ecological and geological variables did not act as strong barriers. Moreover, the data reported herein provide new insights into the population structure of J. regia germplasm, which will help conserve genetic resources for the future, hence improving walnut breeding programs’ efficiency.

Investigating groundwater and surface water interactions using remote sensing, hydrochemistry, and stable isotopes in the Barotse Floodplain, Zambia

ABSTRACT Groundwater and surface water are inseparably linked, thus the understanding of their interactions is indispensable toward developing effective conjunctive water resource management strategies, particularly for ecosystems, such as wetlands, which provide numerous ecosystem services. This study aimed to identify potential areas of Groundwater-surface water (GW-SW) interactions and mechanisms that facilitate them using remote sensing, hydrochemistry, and stable isotopes. The results of remote sensing analysis using Normalised Difference Vegetation Index (NDVI) show that the vegetation is sensitive to the dynamics of groundwater level, with shallower levels (<10 m) in the lower reaches compared to deeper levels (>10 m) in the upper catchment. The study postulates that the near surface groundwater level, maybe due to the near proximity river system (Zambezi River) losing water to recharge the aquifer system, compared to upstream which may be discharging to floodplains (gaining system). Using Hierarchical Cluster Analysis (HCA) of the hydrochemical data, surface, and groundwater showed three similarity clusters of hydrochemistry consisting of Ca-Mg-HCO3, Na-HCO3, and Na-Cl. These zones are further investigated and likely represent geological variability, aquifer confinement, and the degree of GW–SW interactions. The outcomes of this research provide critical input for integrated protection and conservation of ecosystem services in floodplain and aquifer systems.

Influence of Measurement Uncertainty in the Determination of Selected Rock Parameters-A Realistic Approach.

To determine and compare the measurement uncertainty of different geological-geotechnical testing methods, numerous test locations were selected in a hard rock quarry. Measurements were carried out along two vertical measurement lines perpendicular to the mining levels of an existing exploration. Along these lines, the rock quality basically varies due to weathering (decreasing influence with increasing distance from the original ground surface), but also due to the influence of the geological-tectonic conditions on site. The mining conditions (blasting) are identical over the considered area. The rock quality was examined as follows: as field tests, the rock compressive strength was determined by means of point load test and rebound hammer, as laboratory method the Los Angeles test (standard laboratory test for the determination of the mechanical rock quality) was used to identify the impact abrasion resistance. The statistical evaluation and comparison of the results allowed conclusions to be drawn about the contribution of the individual test methods to the measurement uncertainty whereas, in practice, a priori information can be applied complementarily. It shows that the influence on the combined measurement uncertainty u of the different methods due to the geological variability in horizontal direction reaches values between 17 and 32%, whereby the rebound hammer method shows the highest values. However, the highest influences on the measurement uncertainties are a result of the vertical direction due to weathering phenomena with percentages of 55 to 70%. For the point load test, the vertical direction shows the highest significance with an influence of approximately 70%. This leads to the conclusion that a higher weathering degree of the rock mass shows an increasing effect on the measurement uncertainty which needs to be considered using a priori information in measurements.

Ethnobotanical study on the medicinal plants in the Manyas province (Balıkesir, Turkey)

The variation in geology, land forms and climate in Anatolia has led to huge plant diversity. This study performed to provide a comprehensive documentation on ethnomedicinal herbs and traditional knowledge in Manyas (Balıkesir) province. Ethnobotanical records were documented using structured and semi-structured interviews (n=115) and focus group discussions (n=17) with key informants. The informant consensus factor (IFC) and use value (UV) were calculated to see the relative importance of species known. Seventy-five medicinal herbs belonging to 69 genera and 40 family used as treatment of human diseases were recorded. Among these taxa, 61 species were wild and 14 species were recorded as cultivated plant. It is revealed that the most frequently used ethnomedicinal plant families were Lamiaceae (>13%), Asteraceae (>12%), Rosaceae (>8%); the most frequently used preparations methods were infusion and decoction. A total of 122 ethnomedicinal practices were recorded. The traditional herbs have been commonly used for the cure of respiratory system disorders (18%), gastro-intestinal complaints such as ulcers and stomachache abdominal pain (13%), wounds and cut (11%), diabetes (7%), hemorrhoids (5%), heart and vascular disorders (4%). The present study showed that the area was rich in medicinal plant knowledge. The traditional knowledge reported by the indigenous people of Manyas region is valuable for further research and protected ethnobotanical heritage.

COASTAL VULNERABILITY ANALYSIS IN DUMAI BARAT DISTRICT

The Coastal area is an area that is very vulnerable to the phenomenon of natural change. The coastal area's vulnerability level is influenced by geological variables, namely coastal geomorphology, sea level rise, shoreline changes, tides, and coastal elevations. This research was conducted in October 2021. This study aims to determine the level of coastal vulnerability in the Dumai Barat Sub-District, Riau Province with a quantitative descriptive method. Based on the result of the study, the coastal vulnerability index can be classified into five categories: very not vulnerable, not vulnerable, medium, high, and very high. The coastal area of Dumai Barat District is categorized as low vulnerability