Effect of soil cohesion and friction angles on reverse faults

Severe faults have caused many earthquakes around the world throughout history.More recently,earthquakes have occurred in Taiwan,China(Chi-Chi fault),and elsewhere,causing loss of lives and destroying many buildings and structures.These tectonic movements have gained attention from engineers,and in the past 15 years,the focus has been on faulting mechanisms.In this study,a physical model(1 g)was fabricated and used to evaluate the impact of a reverse fault in a field with a tunnel.In the 1 g model,researchers installed additional gauges on the tunnel,so that all the displacements could be adjusted,and all the responses could be monitored during faulting.An experimental study of various soil properties(cohesion and friction angles)in reverse faults on the tunnel lining were carried out and are described herein.A comparison of results for different levels of soil cohesion revealed that it can dramatically reduce the displacement by as much as 40%,and that friction angles of 27ocan record approximately 60%more displacements than at 37o.Furthermore,a comparison of fault angles of 30oand 60oindicates that the displacements can be different by more than 43%in cohesionless soil and about 64%for a friction angle of 27o.     

Large‐scale regional delineation of riparian vegetation in the arid and semi‐arid Pilbara region,WA

Multiscene Landsat 5 TM imagery, Principal Component Analysis, and the Normalized Difference Vegetation Index were used to produce the first region‐scale map of riparian vegetation for the Pilbara (230,000 km²), Western Australia. Riparian vegetation is an environmentally important habitat in the arid and desert climate of the Pilbara. These habitats are supported by infrequent flow events and in some locations by groundwater discharge. Our analysis suggests that riparian vegetation covers less than 4% of the Pilbara region, whereas almost 10.5% of this area is composed of groundwater dependent vegetation (GDV). GDV is often associated with open water (river pools), providing refugia for a variety of species. GDV has an extremely high ecological value and are often important Indigenous sites. This paper demonstrates how Landsat data calibrated to Top of Atmosphere reflectance can be used to delineate riparian vegetation across 16 Landsat scenes and two Universal Transverse Mercator spatial zones. The proposed method is able to delineate riparian vegetation and GDV, without the need for Bidirectional Reflectance Distribution Function correction. Results were validated using ground truth data from local and regional scale vegetation surveys.     

Analysis of Spatial Autocorrelation Patterns of Heavy and Super-Heavy Rainfall in Iran

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord Gistatistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran,have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.     

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.     

Paleoenvironmental conditions of Early Miocene corals,western Makran,Iran

Miocene coral-bearing limestones, distributed in the western Makran Basin, are attributed to Aquitanian and Burdigalian. This investigation is focused on three Early Miocene coral-bearing limestone sections in western Makran. Remarkably, well-preserved scleractinian corals and other components could be very definitive to reconstruct paleoenvironmental conditions. Lithologically, there are some differences between the studied sections. Furthermore, zooxanthellate corals are diverse and abundant in all sections. Based on different components and richness of zooxanthellate corals, it seems that the studied carbonate corals are precipitated in a subtropical condition, between 19 and 20 °C in all sections. In light of the dominancy of corals, the presence of symbiont-bearing larger benthic foraminifera (LBF), lithological features, bioeroding features, and encrusting organisms, the oligotrophic to slightly mesotrophic conditions are considered for Tejek section and the mesotrophic condition had been prevailed in Kermestan and Irer sections. According to light intensity in water column and coral morphotypes, Tejek section is considered to precipitate under euphotic to slightly mesophotic condition, while Kermestan and Irer sections were deposited under mesophotic to euphotic light conditions. In transparent water, photic zones continue to deeper depths, while in less transparent water, these zones are limited to shallower parts. A defined depth in the photic zone may represent euphotic, mesophotic, or oligophotic zone. Based on the water transparency, a taxon in a defined photic zone can occur in various depths. On the basis of trophic-light intensity-depth chart, the estimated depth ranges are 12–85 m for Tejek section and 5–62 m for Kermestan and Irer sections. Water energy as another important factor in environmental condition is acquired from coral morphotypes. Accordingly, Tejek section is precipitated under moderate-high energy and Kermestan and Irer sections are deposited in low to moderate-high energy. The presence of encrusting coralline algae, corals, and other constituents is indicative of different substrates in the studied areas. Corals favorably develop in normal salinity waters. The existence of colonial corals and occurrence of benthic foraminifera with hyaline wall indicate normal seawater conditions.     

Poisson distribution of the Iranian declustered earthquake catalog

Two main goals are considered in this paper: (1) modification and computation of the local coefficients of the space-time windows in the well-known declustering algorithm introduced by Gardner and Knopoff (1974) and (2) checking the independence of the Iranian mainshocks obtained from applying the new modified model. First, 21 of the well-documented earthquake sequences of Iran in the time period of 1972 to 2008 with the mainshock magnitude ranged from M _w = 5.4–7.1 were used to define the new local space-time windows of declustering. Generally, using these Iranian earthquake sequences led to introduce bigger space-time windows for the new model in comparison to the Gardner and Knopoff’s (1974) windows. In the next step, to control the independence of Iranian mainshocks, the events of the Iranian earthquake catalog in the time span of 1964–2010 with moment magnitude of M _w = 3.5–7.4 were used. In this respect, dependent events corresponding to the seven seismotectonic zones of Iran were removed using the new modified space-time windows. After declustering, the mainshock catalog was examined by the Kolmogorov–Smirnov goodness-of-fit test, and it was found to follow a Poisson distribution in all the studied seismotectonic zones of Iran. The same test on times between successive declustered events shows that the inter-event times of all catalogs follow an exponential distribution.     

Delineation of riparian vegetation from Landsat multi‐temporal imagery using PCA

A deficiency in crucial digital data, such as vegetation cover, in remote regions is a challenging issue for water management and planning, especially for areas undergoing rapid development, such as mining in the Pilbara, Western Australia. This is particularly relevant to riparian vegetation, which provides important ecological services and, as such, requires regional protection. The objective of this research was to develop an approach to riparian vegetation mapping at a regional scale using remotely sensed data. The proposed method was based on principal component analysis applied to multi‐temporal Normalized Difference Vegetation Index datasets derived from Landsat TM 5 imagery. To delimit the spatial extent of riparian vegetation, a thresholding method was required and various thresholding algorithms were tested. The accuracy of results was estimated for various Normalized Difference Vegetation Index multi‐temporal datasets using available ground‐truth data. The combination of a 14‐dry‐date dataset and Kittler's thresholding method provided the most accurate delineation of riparian vegetation.     

Evaluation of soil collapse potential in regional scale

Collapsible soils have considerable strength and stiffness in their dry natural state but settle dramatically when they become wet. This paper documents a low-cost, qualitative evaluation scheme using fuzzy set analysis to determine site collapsibility based on subjective knowledge of the geological, geotechnical, and environmental conditions and their uncertainty. For each category, factors or subcategories were defined in a decision tree based on relevant literature. Each category and subcategory was then weighted or rated using linguistic terms developed from expert assessment. The linguistic data or information obtained from the assessments was represented and processed using fuzzy sets. To calibrate the criteria, 87 collapse potential tests were performed on undisturbed soil samples gathered from 27 different locations throughout Iran, leading to the definition of a standard collapse potential fuzzy set. Finally, on the basis of the established criteria, a collapse potential map was prepared for a suburban area in the western part of the city of Kerman, Iran.