Spatial analysis of the frequency–magnitude distribution of aftershock activity of 2003 Bam earthquake: southeast Iran

The Bam earthquake (2003 December 26, M _W = 6.6) was one of the largest earthquakes that occurred in southeast of Iran during last century. It took place along an N–S trending right-lateral strike-slip fault, almost near the southern end of Nyband–Gowk fault. In this study, we mapped the frequency–magnitude distribution of aftershock events spatially across the Bam aftershock zone. The b-value varies between 0.6 and 1.1 across the Bam rupture zone. The overall depth distribution of b-value in Bam aftershock zone reveals two distinct increases in b-value: (1) at depths of 8–10 km and (2) shallower than 4 km beneath the Bam city. There is no correlation between high b- value anomalies found in this study and the region of largest slip, whereas the spatial correlation between high b-value anomalies and the zone of low V _s and high σ (in earlier tomography study) is obvious. This correlation reveals that material properties and increasing heterogeneity are more important in controlling b-value distribution in Bam earthquake rupture zone. The high b-value anomaly near the surface of northern part of rupture zone may be related to unconsolidated and water-rich quaternary alluvial sediments and probable low-strength rocks beneath them. The high b-value anomaly at depth range 8–10 km can be correlated with fractured and fluid-filled mass, which may result from the movement of magma during Eocene volcanism in the Bam area. In this study, the induced changes in pore fluid pressure due to main shock are suggested as a mechanism for aftershock generation.     

Shrinkage Properties of Soft Clay Treated with Cement and Geofibers

In this study, effects of two types of geofibers, namely polypropylene and recycled carpet, on three dimensional shrinkage properties of cement treated kaolinite and bentonite clays are investigated. Cement treated clay specimens were prepared with cement contents of 5, 10, and 15 % by weight of dry soil for kaolinite samples, and 30, 40 and 50 % for bentonite samples. To investigate and understand the influence of different fiber types and contents, three different percentages of fiber content (i.e. 0.1, 0.2 and 0.5 % polypropylene fibers; and 0.5, 0.75 and 1 % carpet fibers) were adopted. The results of shrinkage tests on 126 cylindrical samples of cement treated clay with various cement and fiber contents were analysed to understand the relationships between these parameters and the shrinkage percentage of treated soil. Results of this study indicate that combination of cement and fiber is effective in reducing the volume change of clayey soils undergoing drying process. In the applied ranges of cement and fiber contents, the influence of cement addition on the shrinkage reduction is more significant than the addition of fibers for the treated kaolinite. However, addition of fibers in curtailing the shrinkage of bentonite clay is more significant than the cement addition.     

Arsenic in the Muteh gold mining district, Isfahan, Iran

Following the appearance of symptoms of arsenic toxicity in the inhabitants of villages in the Muteh gold mining region, central Iran, the concentration of this element in various parts of biogeochemical cycle is investigated. For this purpose, rock, groundwater, soil, plant, livestock hair and wool, and human hair samples are collected and analysed. Total arsenic content ranges from 23 to 2,500?mg/kg in rock samples, 7?C1,061???g/l in water, 12?C232?mg/kg in soil, 0.5?C16?mg/kg in plant samples, 4.10?C5.69?mg/kg in livestock hair and wool, and 0.64?C5.82?mg/kg in human hair. Arsenic concentration in various parts of biogeochemical cycle near the gold deposit in a metamorphic complex, and also close to the gold-processing plant, is very high and decreases exponentially with increasing distance from them. Arsenic concentration in water from a well close to the Muteh gold mine is above 1?mg/L. Arsenic in hair samples taken from local inhabitants is above the recommended levels, and the control samples in Shahre-Kord city. Arsenic concentration is higher in male population and correlates positively with age. It is suggested that arsenic resulting from the decomposition of ore mineral such as orpiment (As₂S₃), realgar (As₂S₂) and arsenopyrite (FeAsS) is responsible for polluting natural resources and the human intake via drinking water and the food chain. Gold mining and processing has undoubtedly enhanced the release of arsenic and intensified the observed adverse effects in Muteh area.     

Extending a BIM-based data model to support 3D digital management of complex ownership spaces

Currently, 2D-based analogue building subdivision plans are used to represent the spatial extent of private, public and communal property ownership rights. These plans are recognized as posing a range of challenges in terms of communicating the spatial complexity of ownership spaces within multi-storey buildings. In response to these challenges, three-dimensional (3D) digital data environments are being investigated as a potential approach for managing complex, vertically stratified ownership arrangements. The argument presented in this article is that Building Information Modelling (BIM) can be adopted for 3D digital management of data related to complex ownership spaces. BIM provides a common and 3D digital data sharing space, underpinning a reliable basis for facilitating collaboration and decision-making over the lifecycle of buildings. However, ownership attributes and the spatial structure of ownership arrangements inside buildings are yet to be accommodated within the BIM data environment. In this article, we present a range of required data elements for managing complex ownership spaces, which have been elicited by investigating current practices pertaining to subdivision of ownership spaces within multi-storey buildings in Melbourne, Australia. An open data model in the BIM domain is extended with these data elements and a prototype model for a real multi-storey building is implemented to demonstrate the viability of the extended data model for 3D digital management and visualization of data related to complex ownership arrangements.     

Design of small GPS drifters for current measurements in the coastal zone

Field observations of flows in coastal zone are scarce, but important for understanding the spatial variability of currents. The design of small, low-cost GPS drifters for collecting accurate Lagrangian data in the coastal zone is described. The drifters are intended for using in nearshore environments, lakes and estuaries over timescales of a few minutes up to several hours and are a low-cost alternative for applications which do not require drifter’s sea-going capability. Two field tests of GPS drifters in the south coast of Caspian Sea in Anzali port, Iran, in November 2008 and July 2009 were successful.     

Fluoride hydrogeochemistry and bioavailability in groundwater and soil of an endemic fluorosis belt,central Iran

Presence of fluoride in groundwater is a public health problem in the so-called endemic fluorosis belt of the central Iran, where the groundwater is the major source of drinking water in most urban and rural areas. Therefore, an attempt has been made to determine the hydrogeochemical factors controlling fluoride enrichment in the groundwater resources at this belt. Fluoride concentrations ranged from 0.20 to 1.99 mg/L (1.02 ± 0.47) in groundwater samples. The presence of different F-bearing minerals and also clay minerals in the soils and aquifer materials was confirmed using XRD analysis. To identify probable sources of dissolved F^? and investigate groundwater quality, multivariate statistical analyses were carried out. Geochemical modeling indicated that all samples were undersaturated with respect to fluorite, halite, gypsum and anhydrite and mostly oversaturated with respect to calcite and dolomite. Contrary to most high-fluoride regions in the World, the high F^? content was dominated by Na–Cl- and Ca–SO₄-type groundwater in the study area. Besides, fluoride showed negative relationship with pH and HCO₃ ^? in groundwater. In order to assess the bioavailability of fluoride in soils, a two-step chemical fractionation method was applied. The results showed that fluoride in soils mostly accompanied with the residual and water-soluble fractions and was poorly associated with soil’s bonding sites. Calculated aqueous migration coefficient demonstrated that fluoride in the studied soils was mobile to easily leachable to the groundwater. Finally, the results demonstrated that combination of water–rock interaction and influence of clay minerals is geochemical mechanism responsible for controlling fluoride enrichment in groundwater.     

Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification

Conditioning complex subsurface flow models on nonlinear data is complicated by the need to preserve the expected geological connectivity patterns to maintain solution plausibility. Generative adversarial networks (GANs) have recently been proposed as a promising approach for low-dimensional representation of complex high-dimensional images. The method has also been adopted for low-rank parameterization of complex geologic models to facilitate uncertainty quantification workflows. A difficulty in adopting these methods for subsurface flow modeling is the complexity associated with nonlinear flow data conditioning. While conditional GAN (CGAN) can condition simulated images on labels, application to subsurface problems requires efficient conditioning workflows for nonlinear data, which is far more complex. We present two approaches for generating flow-conditioned models with complex spatial patterns using GAN. The first method is through conditional GAN, whereby a production response label is used as an auxiliary input during the training stage of GAN. The production label is derived from clustering of the flow responses of the prior model realizations (i.e., training data). The underlying assumption of this approach is that GAN can learn the association between the spatial features corresponding to the production responses within each cluster. An alternative method is to use a subset of samples from the training data that are within a certain distance from the observed flow responses and use them as training data within GAN to generate new model realizations. In this case, GAN is not required to learn the nonlinear relation between production responses and spatial patterns. Instead, it is tasked to learn the patterns in the selected realizations that provide a close match to the observed data. The conditional low-dimensional parameterization for complex geologic models with diverse spatial features (i.e., when multiple geologic scenarios are plausible) performed by GAN allows for exploring the spatial variability in the conditional realizations, which can be critical for decision-making. We present and discuss the important properties of GAN for data conditioning using several examples with increasing complexity.