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Abstract: The 1200km long North Anatolian Fault (NAF) is part of an east–west trending dextral shear zone (NAF system), along the boundary between the Anatolian and Eurasian plates, that widens to the west. This widening zone of deformation complicates potential earthquake rupture paths and highlights the importance of understanding the geometry of active fault systems. In the central portion of the NAF system – just west of the town of Bolu – the NAF splits into two major faults: the northern and southern strands. These two faults diverge, almost converge, and then diverge again to border the Marmara Sea. Earthquake data from the region where the two faults converge indicate that they may be linked by an active fault. We model the active fault geometries with and without the linking fault to explore its impact on the output regional stress field (from a finite element model). These results are compared to focal mechanism records and lineament analyses to determine which geometry best simulate the stress field in a regional model. Our results show that a linking fault between the northern and southern strands of the NAF system is necessary to best match the primary stress orientations of the model with the maximum paleostress orientations inferred from deformation patterns, and observed in earthquake focal mechanisms. Furthermore, the linking fault should be a significant component in future models of the NAF system within the region. [Copyright &y& Elsevier]

Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods. [ABSTRACT FROM AUTHOR]

Carbon capture and sequestration (CCS) is a technology that provides a near-term solution to reduce anthropogenic CO2 emissions to the atmosphere and reduce our impact on the climate system. Assessments of carbon sequestration resources that have been made for North America using existing methodologies likely underestimate uncertainty and variability in the reservoir parameters. This paper describes a geostatistical model developed to estimate the CO2 storage resource in sedimentary formations. The proposed stochastic model accounts for the spatial distribution of reservoir properties and is implemented in a case study of the Oriskany Formation of the Appalachian sedimentary basin. Results indicate that the CO2 storage resource for the Pennsylvania part of the Oriskany Formation has substantial spatial variation due to heterogeneity of formation properties and basin geology leading to significant uncertainty in the storage assessment. The Oriskany Formation sequestration resource estimate in Pennsylvania calculated with the effective efficiency factor, E=5%, ranges from 0.15 to 1.01 gigatonnes (Gt) with a mean value of 0.52 Gt of CO2 (E=5%). The methodology is generalizable to other sedimentary formations in which site-specific trend analyses and statistical models are developed to estimate the CO2 sequestration storage capacity and its uncertainty. More precise CO2 storage resource estimates will provide better recommendations for government and industry leaders and inform their decisions on which greenhouse gas mitigation measures are best fit for their regions.