Geological Rock Types Research Articles

Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks

The integration of image analysis through deep learning (DL) into rock classification represents a significant leap forward in geological research. While traditional methods remain invaluable for their expertise and historical context, DL offers a powerful complement by enhancing the speed, objectivity, and precision of the classification process. This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks (CNNs) for geological image analysis, particularly in the classification of igneous, metamorphic, and sedimentary rock types from rock thin section (RTS) images. This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision. Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities, achieving an F1-Score of 0.9869 for igneous rocks, 0.9884 for metamorphic rocks, and 0.9929 for sedimentary rocks, representing improvements compared to the baseline original results. Moreover, the weighted average F1-Score across all classes and techniques is 0.9886, indicating an enhancement. Conversely, methods like Distort lead to decreased accuracy and F1-Score, with an F1-Score of 0.949 for igneous rocks, 0.954 for metamorphic rocks, and 0.9416 for sedimentary rocks, exacerbating the performance compared to the baseline. The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results. The findings of this study can benefit various fields, including remote sensing, mineral exploration, and environmental monitoring, by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Modeling Permeability in Different Carbonate Rock Types

In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to estimate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to provide a workflow to improve the use of existing models (e.g., Kozeny, Lucia, and Winland) to predict permeability in carbonate reservoirs. More than 1,000 core plugs were studied from seven different carbonate reservoirs across the Middle East: mainly Cretaceous reservoirs. The plugs were carefully selected to represent a wide range of properties within the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs, and high-pressure mercury injection. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy, and mixed. Special attention was given to the diagenesis effects, mainly compaction, cementation, and dissolution. The texture information was plotted in the porosity-permeability domain and was found to produce three distinct porosity-permeability relationships. Each texture gave a unique porosity-permeability trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozeny, Winland, and Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. Both Kozeny and Lucia models honored the geology of the samples and showed similar trends to the porosity-permeability relationships, whereas the Winland model gave different slopes to the experimental data. The prediction of the permeability was improved by using different model parameters per RRT within each texture. This work presents a systematic approach to construct correlations between porosity and permeability in complex carbonate reservoirs. Model parameters (i.e., FZI, RFN, and r35) were suggested within different geological rock types to estimate permeability. Based on the workflow presented in the paper, the predicted permeability was improved to less than a factor of 2 compared to the measured values. Moreover, the same workflow was applied using the data from seven different reservoirs, and the same rock typing scheme was applicable to all the reservoirs. Such work is not abundant in the literature and would serve to improve permeability prediction in heterogeneous carbonate reservoirs, which is one of the main uncertainties in modeling carbonates.

DETERMINATION OF GROUNDWATER POTENTIAL AREAS IN KADUNA METROPOLIS, KADUNA, NIGERIA

Groundwater is one of the major resources important for sustainable growth and development. Proper management of the resources is therefore important to meet all the water requirements for human existence to continue. In this study, an investigation is made to explore for groundwater potential areas of Kaduna Metropolis, Kaduna State using remote sensing and GIS technique. The data used for the study are as follows; Landsat 8 (OLI) 30m, Aster data (30m), Geologic map, Soil map and Topographic map was used to generate the factors such as; Land use/cover, Slope, Geomorphology, Geologic rock types, soil textures and drainage density. All the factors and its attributes were weighted and rated respectively and were classified according to their importance to groundwater occurrence using the modified DRASTIC model and the weighted overlay technique was used to create a groundwater potential map of the metropolis. Groundwater potential map were classified into four categories that best describes the potential of each area. These classes are; Poor (21%), Moderate (23%), Good (26%) and Very Good (30.2%) groundwater potential which was found to be concentrated in the central part of the metropolis. This result was verified against existing borehole data and field observations to validate the accuracy and was found to be 76% accurate. The study demonstrated the effectiveness of Remote Sensing and GIS as an effective tool in delineation and identification of groundwater potential areas.

Development of data-driven models for estimating the probability of high-concentration occurrence of naturally occurring radioactive materials in groundwater

High-concentration occurrence probability estimation models was developed to estimate 238U and 222Rn in groundwater using in-situ monitoring data (i.e., geological rock types, well depth, T, pH, Eh, EC, DO, and HCO3). The estimation models are based on a non-linear data-driven method to improve their effectiveness for applications in different estimation cases using various in-situ monitoring data. When developing the models, most sensitive in-situ monitoring data are selectively utilized to train the models, where various statistical and correlation analyses are applied to overcome challenges, including model overfitting during training, a highly nonlinear correlation between input and target variables, and poor training due to low-quality monitoring data. Based on statistical analysis results, all input variables, except for Eh and well depth, are used for developing the 238U and 222Rn estimation models, respectively. Actual data collected from groundwater quality monitoring networks of South Korea from 2007 to 2019 are used to validate the developed models. Although it is difficult to characterize 222Rn occurrence using the geochemical conditions of groundwater because the gaseous phase behavior of 222Rn highly depends on structural geology, the 222Rn estimation model achieves reasonable performance with more than 70% accuracy. In addition, compared to the 222Rn estimation model, the 238U estimation model achieves higher classification accuracy with approximately 80%. Consequently, we can confirm that the developed estimation models can be effectively used to estimate the probability of high-concentration risk of 238U and 222Rn in groundwater. Conclusively, the practical applicability of the developed models is wide, as the models have been developed using direct observable and real-time data.

Rock classification in petrographic thin section images based on concatenated convolutional neural networks

Rock classification plays an important role in rock mechanics, petrology, mining engineering, magmatic processes, and numerous other fields pertaining to geosciences. This study proposes a concatenated convolutional neural network (Con-CNN) method for classifying geologic rock types based on petrographic thin sections. Plane polarized light (PPL) and crossed polarized light (XPL) were used to acquire thin section images as the fundamental data. After conducting the necessary pre-processing, the PPL and XPL images as well as their comprehensive image developed by principal component analysis were sliced into small patches and were put into three CNNs, comprising the same structure for achieving a preliminary classification. Subsequently, these patches classification results of the CNNs were concatenated by using the maximum likelihood method to obtain a comprehensive classification result. Finally, a statistical revision was applied to fix the misclassification due to the proportion differences of minerals that were similar in appearance. In this study, there were 92 rock samples of 13 types giving 106 petrographic thin sections and 2208 petrographic thin section images, and finally 238,464 sliced image patches were used for the training and validation of the Con-CNN method. The 5-folds cross validation showed that the proposed method provides an overall accuracy of 89.97% and a kappa coefficient of 0.86, which facilitates the automation of rock classification in petrographic thin section images.

Analysis of Hyperspectral Remote Sensing Images for Extraction Geological Rock Types Maps by Geospatial Techniques

Hyperspectral images involve a wealth of data, but interpreting them needs an understanding of exactly what properties of ground materials that it is trying to measure and how to relate to the measurements made by the hyperspectral sensor. The purpose of the study is to use hyperspectral remote sensing data for finding of geological locations and the creation of geological rock types map. In this research, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image has been used and analyzed using geospatial techniques such as ENVI software. These techniques include: enhancement, de-correlation stretch, band ratio and classification. All these techniques have been performed to produce geological rock kinds map. Also, 3D surface for ASTER data can be displayed using ENVI software.

A GIS-based linear regression modeling approach to assess the impact of geologic rock types on groundwater recharge and its hydrological implication

In this study, the efficacy of the linear regression modeling technique in the company of recharge rate-induced geophysical parameters was implemented through geographical information system to establish the essentiality of geology factor in recharge rate estimate and prediction. The study empirically assessed the in situ and the spatiotemporal variability in the recharge rate in response to the multi-faceted geologic settings in the Perak province, Malaysia. The used data sets were 2D resistivity data, climate data and the ancillary data (surface geologic rock types; [quaternary (QUA); Devonian (DEV); Silurian (SIL); igneous(ING)]. The acquired 2D data were interpreted for delineating vadose zone units having varying geoelectrical parameters consisting of resistivity (ρ) and its thickness (D) (depth to aquifer top). The combined GIS and linear regression techniques were applied to relate the vadose zone geoelectrical parameters with the spatially modeled rainfall–recharge estimate. The obtained results were processed in R statistical software environment to develop GIS linear regression (GLR)-based recharge models for the area underlain geologic rock types. The developed GIS linear regression (GLR)-based recharge models were validated using the models’ parameter significance evaluation and models’ forecasting accuracy assessment approaches. The results of the applied GLR-based recharge models at each 2D location were processed to produce groundwater recharge maps for the rock types. Applying the concept of GIS-based geoelectrical parameters recharge estimate and rainfall–recharge rate–subsurface media estimate approaches, the output of the GLR-based recharge models was quantitatively evaluated to give expected recharge estimates of 242 mm/yr, 248.mm/yr, 268 mm/yr and 224 mm/y and total recharge water values of 377.9 × 106 m3/year, 319 × 106 m3/year, 161.6 × 106 m3/year, 227.3 × 106 m3/year, for the area underlain rock types, namely QUA, DEV, SIL and ING, respectively. Based on these results, the impact of geologic rocks’ factor on the area recharge rate estimates is established. Thus, the consideration of the geology factor is very important in accurate estimation and prediction of the groundwater recharge rate. The developed GLR-based recharge models and the produced groundwater recharge maps are viable decision tools for efficient management of groundwater resources in the study area and any area of similar geologic settings.

Grid density overlapping hierarchical algorithm for clustering of carbonate reservoir rock types: A case from Mishrif Formation of West Qurna-1 oilfield, Iraq

The primary goal of this study is to solve a major challenge of reservoir rock typing in quantitatively understanding the relationship between geological and petrophysical properties of carbonate reservoir. This paper first presents an improved overlapping index based on the grid density to define the overlapping relationship between different clusters and a grid density overlapping hierarchical algorithm (GDOH) for clustering of carbonate reservoir rock types. GDOH clustering algorithm is a hybrid type which integrates the gird, density, overlapping index and the hierarchical clustering method using the number of grid divisions and the threshold value of the overlapping index as the key parameters. Taking the Mishrif Formation of West Qurna Oilfield-1 as a case, the GDOH clustering algorithm is employed for clustering of carbonate reservoir rock types. As a result, ten geological rock types are identified according to the lithological type and diagenetic type using the thin section data. We finally got four reservoir rock types using the GDOH clustering algorithm with the overlapping index of core porosity and the permeability data as the metric. These reservoir rock types are different in reservoir qualities and porosity-permeability relationships. Each reservoir rock type also shows a similar set of capillary pressure curves and a typical and unique set of pore throat size distribution. The clustering result gives us some clues to understand the control of depositional and diagenesis features on the variation of reservoir rock types in reservoir quality and porosity-permeability relationship. Compared with the model-based petrophysical rock-typing techniques such as Winland r35, FZI and FZI-Star (FZI*), GDOH algorithm shows some apparent advantages in the evaluation of reservoir quality and prediction of permeability in three dimensions based on geologic principles in addition to understanding the relationship between reservoir rock type and petrophysical static and dynamic rock type.

Reservoir rock typing using integrating geological and petrophysical properties for the Asmari Formation in the Gachsaran oil field, Zagros basin

The Gachsaran oilfield is one of the largest oilfields in the Zagros basin and the Asmari Formation is a primary reservoir rock in it. In this study, the reservoir rock typing has been carried out by a combination of microscopic (microfacies and diagenesis) and petrophysical (electrofacies and hydraulic flow units) studies. Microscopic studies of 478 thin sections from core samples led to identification of 11 microfacies belonging to 4 facies belts including tidal flat, lagoon, shoal, and open marine as well as diagenetic phenomena such as micritization, bioturbation, neomorphism, compaction, dissolution, cementation, dolomitization, and anhydrzation. In order to determine the rock types, the effects of diagenesis on distribution of porosity-permeability data in the petrophysical diagram were investigated and eventually 12 geological rock types introduced. Concepts of electrofacies and hydraulic flow units have been used to determine the petrophysical rock types and then their propagation in non-cored depths and wells. In this context, by means of the well logging data cluster analysis in the Paradigm ™ Geolog 6.7.1 software, 4 electrofacies were determined and modeled. The validity of these electrofacies was examined through their correlation with 12 geological rock types, core porosity-permeability data, and capillary pressure curves. Furthermore, by using core porosity-permeability data, the flow zone indicators were calculated and accordingly 6 flow units identified in the framework of discrete rock types. Subsequently we tried to validate these flow units by using estimation of permeability for each of them and their comparison with core permeability, and also correlating flow units with capillary pressure curves. Finally, in order to predict the flow zone indicators and propagation of the flow units in the non-cored depths and wells, modeling of flow zone indicators was carried out by establishing a quantitative relation between calculated flow zone indicators and well logging data in the software. The results of this research can be useful for hydrocarbon development and production in the future of this oilfield and similar fields.

Solvent diffusion and dispersion in partially saturated porous media: An experimental and numerical pore-level study

Recent research on unlocking the solvent dispersion as a physical blending process in porous media has been mainly focused on core-scale observations. Pore-level studies of multiphase displacements can help to develop models that correlate rock macro-scale characteristics with its small-scale features, particularly for unsaturated rocks where the intricate fluid arrangements cause dynamic events to be localized in preferential pathways. In this work, to draw new insights into the physics of pore-level interfaces and crystallize the role of small-scale phenomena on the efficiency of solvent-aided bitumen recovery processes, numerical simulations coupled with experiments are conducted in pore-level domains. We simulate fluid flow and transport phenomena through millimeter-sized three-dimensional slabs of consolidated and unconsolidated packings of grains representing the geological rock types of the McMurray formation. We propose a robust numerical workflow for simulation of miscible-floods in unsaturated porous media and investigate the impact of matrix heterogeneity, connate water, cementation, and injection velocity on the longitudinal dispersion coefficient. In particular, primary drainage is simulated at low capillary numbers resulting in two-phase fluid occupancies through pore space domains. Finite element simulations are then carried out in order to solve the mixing advection-diffusion equations within the water-free pore space, and lastly, the effluent history is analyzed to predict the dispersion coefficient in both fully- and partially-saturated conditions and evaluate the efficiency of miscible displacements in the presence of microheterogeneities. A new analytical model for calculation of dispersivity, together with the numerical simulation results, is utilized to adjust a general model for the prediction of longitudinal dispersion coefficient in unsaturated sandy porous media of either uniform or non-uniform grains. Moreover, miscible-flood experiments at low to high injection velocities are conducted in a transparent glass micromodel. According to the results, rock micro-heterogeneities and immobile water both increase the longitudinal dispersion coefficient, and two-phase equilibria control the velocity field by creating connected regions of brine and low resistance oil-filled channels and consequently influence the solute transport and mixing processes. The effect of viscosity contrast on the longitudinal dispersion coefficient is also noteworthy, as the viscous fingering at unfavorable viscosity ratios widens the mixing zone.

A review for genesis of continental arc magmas: U, Th, K and radiogenic heat production data from the Gümüşhane Pluton in the Eastern Pontides (NE Türkiye)

In this study, in situ radioelemental (238U, 232Th and 40K) measurements and heat production rate evaluation have been conducted at 132 sites in the Gümüşhane Pluton of the Eastern Pontides Orogenic Belt (EPOG) of Northern Türkiye. The average K, U and Th values for granites are 4.35±0.71%, 5.85±2.74ppm, 22.13±5.55ppm, respectively; microgranites 4.33±1.37%, 7.12±2.23ppm, 24.11±9.59ppm; metagranite 3.93±0.68%, 6.24±2.75ppm, 12.87±3.92ppm; andesite/basalt 1.75±0.74%, 3.22±1.07ppm, 9.01±5.14ppm. Based on the abundances of K, U and Th, the radiogenic heat production (RHP) values, which is a reflection of the geological rock types, are estimated as 3.52±1.03μWm−3, 3.86±1.31μWm−3, 2.93±0.89μWm−3, and 1.68±0.63μWm−3 for granites, microgranites, metagranites and andesite/basalt, respectively. The average heat production value for the study area is estimated as 2.83±1.39μWm−3, which is closer to the upper continental crustal value; contrary to the highest heat production value of granitic basement and the lowest heat production values of the sedimentary rocks.The study region is depicted with very high level of Th/U value ranging from 0.11 to 23.19 with mean value of 3.53±2.5, which is comparatively close to the upper continental crustal estimate of 3.8. The numerical results verify that mantle-derived magmas are subjected to contamination with both middle and lower crustal material constituted by fragments of southerly dipping subduction of Tethys Ocean during the late Mesozoic–Cenozoic.

Shifting effects of rock roughness across a benthic food web

Habitat heterogeneity affects the spatial pattern of stream organisms, but it is unclear how broadly heterogeneity affects the distribution of organisms within a food web. Specifically, rougher rocks have greater algal biomass than smoother rocks, and we hypothesized bottom-up food web control of food web structure, in which rougher rocks would also have higher grazer and predator abundance. We surveyed algal biomass and macroinvertebrates on rocks of differing roughness. We also conducted a field experiment to separately examine rock roughness and algal biomass effects by manipulating algal biomass by raking or scrubbing rocks within created rock clusters. Neither the survey nor the experiment strongly supported a bottom-up scenario. Algal biomass increased with rock roughness. Grazing mayfly abundance was distributed evenly among geologic rock types, except for a higher abundance of baetids on rocks with large cavities, where predatory stoneflies were also abundant. In the rock cluster experiment, the moderate raking disturbance produced higher grazer abundance and reduced algal biomass relative to unmanipulated controls. We concluded that fine-scale roughness directly promoted algal biomass, whereas larger-grain roughness (crevices) affected the distribution of the food web components by forming clumped distributions of grazing baetid mayflies and predatory stoneflies.

Geological modeling of rock type domains in the Balya (Turkey) lead-zinc deposit using plurigaussian simulation

AbstractMineral resource evaluation requires defining geological rock-type domains. The traditional simulation methods have serious limitations for applications to large numbers of domains, which have complex contact relations. Plurigaussian simulation is an effective method which can be applied, in a simple way, to any number of domains, using both local and global geological information to infer the distributions of rock types. This work not only presents the application of the plurigaussian simulation method to the Balya lead-zinc deposit, but also assesses the spatially varying rock type proportions, and accounts for uncertainties between them. These parameters are extremely important for mining deposits, since the mineralizations of interest generally occur only in certain rock types. Furthermore, being able to model the different geological rock types is vital to good mine operations, production planning, and management. The results indicate that the plurigaussian method correctly reproduces the different orientations of the individual rock types, as seen in drill holes, and the proportion of each rock type, even if this varies in space.

Annual emissions of mercury to the atmosphere from natural sources in Nevada and California

The impact of natural source emissions on atmospheric mercury concentrations and the biogeochemical cycle of mercury is not known. To begin to assess this impact, mercury emissions to the atmosphere were scaled up for three areas naturally enriched in mercury: the Steamboat Springs geothermal area, Nevada, the New Idria mercury mining district, California, and the Medicine Lake volcano, California. Data used to scale up area emissions included mercury fluxes, measured in-situ using field flux chambers, from undisturbed and disturbed geologic substrates, and relationships between mercury emissions and geologic rock types, soil mercury concentrations, and surface heat flux. At select locations mercury fluxes were measured for 24 h and the data were used to adjust fluxes measured at different times of the day to give an average daily flux. This adjustment minimized daily temporal variability, which is observed for mercury flux because of light and temperature effects. Area emissions were scaled spatially and temporally with GIS software. Measured fluxes ranged from 0.3 to approximately 50 ng m–2 h–1 at undisturbed sites devoid of mercury mineralization, and to greater than 10,000 ng m–2 h–1 from substrates that were in areas of mercury mining. Area-averaged fluxes calculated for bare soil at Steamboat Springs, New Idria, and Medicine Lake of 181, 9.2, and 2 ng m–2 h–1, respectively, are greater than fluxes previously ascribed to natural non-point sources, indicating that these sources may be more significant contributors of mercury to the atmosphere than previously realized.

A New Method for Characterizing Surface Roughness and Available Space in Biological Systems

1. Surface roughness and available space for organisms inhabiting heterogeneous habitats have important effects on measurements of species diversity, population abundance and dispersal. Quantifying habitat complexity is important but very difficult to achieve, especially at small scales. 2. A method of quantifying topographic roughness is described, which produces estimates of space potentially available to organisms in different size classes rather than giving general descriptors. The method uses samples of profiles across the surface of interest that are taken over any spatial scale and that are then analysed by a low-cost, image-processing technique. 3. A case study shows that the method allows quantification of the effects of surface texture on abundance of stream algae. Stream stones differing in geological rock type differed in roughness at very small spatial scales (crevices < 0.4 mm in size). Chlorophyll a concentrations were correlated positively with roughness measured at the smallest spatial scale (0.2 mm). A colonization experiment, in which bricks were sandblasted to produce rough surfaces, showed that rough substrata attain higher abundances of stream algae than smooth. The imaging technique permitted the quantification of the exact differences in texture between smooth and rough surfaces while demonstrating that both rough and smooth bricks still fell within the natural range of texture shown by stream stones

Using Soil Gas Radon and Geology to Estimate Regional Radon Potential

Two important parameters have been identified in order to estimate the radon potential of a region. They are the soil gas radon concentration and the geological rock type from which soils are derived. A simple soil gas collection and analytical technique has been developed to provide information on soil gas radon concentrations. The application of these techniques has demonstrated a clear relationship between the estimate of the radon potential and indoor radon measurements. This information is particularly important when evaluating the radon potential of areas that will be subject to population expansion in the future. Other factors, such as gamma radiation measurements and soil permeability can be included to improve the estimate of radon potential, but geology and soil gas measurements are the most important factors. Although this approach is useful for regional estimates, it can also be used for site-specific evaluations.

Using Soil Gas Radon and Geology to Estimate Regional Radon Potential

Two important parameters have been identified in order to estimate the radon potential of a region. They are the soil gas radon concentration and the geological rock type from which soils are derived. A simple soil gas collection and analytical technique has been developed to provide information on soil gas radon concentrations. The application of these techniques has demonstrated a clear relationship between the estimate of the radon potential and indoor radon measurements. This information is particularly important when evaluating the radon potential of areas that will be subject to population expansion in the future. Other factors, such as gamma radiation measurements and soil permeability can be included to improve the estimate of radon potential, but geology and soil gas measurements are the most important factors. Although this approach is useful for regional estimates, it can also be used for site-specific evaluations.

Mineral resources versus geologic diversity in small areas

There is a simple linear relationship between geologic diversity ( = the number of rock types — 1) and the mineral-resource diversity ( = the number of commodities produced — 1) which may be used to predict the number of commodities produced from a given number of geologic rock types in a region. In addition it is shown that the geologic and mineral-resource diversities are related positively to size (expressed as log A km 2) of sampling unit. It is of some interest to determine whether these relationships hold for sample units the size of counties. 182 counties from six states, Maine (16), New Hamshire (10), Vermont (14), Pennsylvania (67), Nevada (17), and California (58) are used to investigate these relationships. The regression of geologic diversity ( s g − 1) on size (log A) is positive, linear, and about r 2 = 41.0% determining. Similarly, the regression of mineral-resource diversity ( s m − 1) on size (log A) is positive, linear, and r 2 = 39.6% determining. The regression of mineral resource on geologic diversity also is similarly linear and positive with r 2 = 54% determining. The regression of geologic diversity on size for a larger global population ( n = 413; where sample units are countries and states) is similar to that for the 182 counties with r 2 = 48% determining. Evidently, the relationships hold for sample units the size of counties with a similar slope but a smaller intercept. It then is shown that for sample units the size of states and countries (i.e. log A from 3.0 to 6.5) the intercept is about 12. In other words, given a sample unit the size of states or countries with a geologic diversity of zero, the region is geologically homogeneous, one would expect it to produce some 12 commodities; on the other hand if the sample unit is the size of counties with the same value of zero for geologic diversity, then it would likely produce from 3 to 5 commodities.

Lithological studies of western foothills of Himalaya from Remotely Sensed data: A computer image analysis approach

Digital sensing systems, on board remote sensing satelites, have provided a very powerful tool for conducting earth resources studies using digital computers. This paper mainly describes various digital techniques that are applied on Remotely Sensed data to extract various landuse features and to identify, broadly, geological rock types using Dipix Image Processing System. A variety of image enhancement algorithms are used to delineate four major Geological groups and several landuse features such as drainage, river, canal, vegetational pattern, railways, road etc. Some of the landuse features have been of help in identifying and marking of four major geological groups namely Lower pre-Cambrian rocks, Upper and Lower Siwaliks and recent alluvial deposits.