Development of fragility curves by incorporating new spectral shape indicators and a weighted damage index: case study of steel braced frames in the city of Mashhad,Iran

In this study, strong ground motion record(SGMR) selection based on Eta(η) as a spectral shape indicator has been investigated as applied to steel braced frame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon(ε) and the target Eta(η) values at different hazard levels is presented, taking into account appropriately selected SGMR's. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter η is a more robust predictor of damage than searching for records with appropriate ε values.     

Effect of seismic waves on the hydro-mechanical properties of fractured rock masses

The transmission of seismic waves in a particular region may influence the hydraulic properties of a rock mass, including permeability, which is one of the most important. To determine the effect of a seismic wave on the hydraulic behavior of a fractured rock mass, systematic numerical modeling was conducted. A number of discrete fracture network(DFN) models with a size of 20 m × 20 m were used as geometrical bases, and a discrete element method(DEM) was employed as a numerical simulation tool. Three different boundary conditions without(Type Ⅰ) and with static(Type Ⅱ) and dynamic(Type Ⅲ) loading were performed on the models, and then their permeability was calculated. The results showed that permeability in Type Ⅲ models was respectively 62.7% and 44.2% higher than in Type I and Type Ⅱ models. This study indicates that seismic waves can affect deep earth, and, according to the results, seismic waves increase the permeability and change the flow rate patterns in a fractured rock mass.     

Seismic depth imaging of iron-oxide deposits and their host rocks in the Ludvika mining area of central Sweden

The development of cost-effective and environmentally acceptable geophysical methods for the exploration of mineral resources is a challenging task. Seismic methods have the potential to delineate the mineral deposits at greater depths with sufficiently high resolution. In hardrock environments, which typically host the majority of metallic mineral deposits, seismic depth-imaging workflows are challenged by steeply dipping structures, strong heterogeneity and the related wavefield scattering in the overburden as well as the often limited signal-to-noise ratio of the acquired data. In this study, we have developed a workflow for imaging a major iron-oxide deposit at its accurate position in depth domain while simultaneously characterizing the near-surface glacial overburden including surrounding structures like crossing faults at high resolution. Our workflow has successfully been showcased on a 2D surface seismic legacy data set from the Ludvika mining area in central Sweden acquired in 2016. We applied focusing prestack depth-imaging techniques to obtain a clear and well-resolved image of the mineralization down to over 1000 m depth. In order to account for the shallow low-velocity layer within the depth-imaging algorithm, we carefully derived a migration velocity model through an integrative approach. This comprised the incorporation of the tomographic near-surface model, the extension of the velocities down to the main reflectors based on borehole information and conventional semblance analysis. In the final step, the evaluation and update of the velocities by investigation of common image gathers for the main target reflectors were used. Although for our data set the reflections from the mineralization show a strong coherency and continuity in the seismic section, reflective structures in a hardrock environment are typically less continuous. In order to image the internal structure of the mineralization and decipher the surrounding structures, we applied the concept of reflection image spectroscopy to the data, which allows the imaging of wavelength-specific characteristics within the reflective body. As a result, conjugate crossing faults around the mineralization can directly be imaged in a low-frequency band while the internal structure was obtained within the high-frequency bands.     

Improved structural interpretation of legacy 3D seismic data from Karee platinum mine (South Africa) through the application of novel seismic attributes

Seismic detection of faults, dykes, potholes and iron-rich ultramafic pegmatitic bodies is of great importance to the platinum mining industry, as these structures affect safety and efficiency. The application of conventional seismic attributes (such as instantaneous amplitude, phase and frequency) in the hard-rock environment is more challenging than in soft-rock settings because the geology is often complex, reflections disrupted and the seismic energy strongly scattered. We have developed new seismic attributes that sharpen seismic reflections, enabling additional structural information to be extracted from hard-rock seismic data. The symmetry attribute is based on the invariance of an object with respect to transformations such as rotation and reflection; it is independent of the trace reflection amplitude, and hence a better indicator of the lateral continuity of thin and weak reflections. The reflection-continuity detector attribute is based on the Hilbert transform; it enhances the visibility of the peaks and troughs of the seismic traces, and hence the continuity of weak reflections. We demonstrate the effectiveness of these new seismic attributes by applying them to a legacy 3D seismic data set from the Bushveld Complex in South Africa. These seismic attributes show good detection of deep-seated thin (∼1.5 m thick) platinum ore bodies and their associated complex geological structures (faults, dykes, potholes and iron-rich ultramafic pegmatites). They provide a fast, cost-effective and efficient interpretation tool that, when coupled with horizon-based seismic attributes, can reveal structures not seen in conventional interpretations.     

Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance

Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.

     

Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Strange quark star in dilaton gravity

In this work, we first obtain the hydrostatic equilibrium equation in dilaton gravity. Then, we examine some of the structural characteristics of a strange quark star in dilaton gravity in the context of Einstein gravity. We show that the variations of dilaton parameter do not affect the maximum mass, but variations in the cosmological constant lead to changes in the structural characteristics of the quark star. We investigate the stability of strange quark stars by applying the MIT bag model with dilaton gravity. We also provide limiting values for the dilaton field parameter and cosmological constant. We also study the effects of dilaton gravity on the other properties of a quark star such as the mean density and gravitational redshift.We conclude that the last reported value for the cosmological constant does not affect the maximum mass of a strange quark star.