Isolating non-subduction-driven tectonic processes in Cascadia

Several tectonic processes combine to produce the crustal deformation observed across the Cascadia margin: (1) Cascadia subduction, (2) the northward propagation of the Mendocino Triple Junction (MTJ), (3) the translation of the Sierra Nevada–Great Valley (SNGV) block along the Eastern California Shear Zone–Walker Lane and, (3) extension in the northwestern Basin and Range, east of the Cascade Arc. The superposition of deformation associated with these processes produces the present-day GPS velocity field. North of ~ 45° N observed crustal displacements are consistent with inter-seismic subduction coupling. South of ~ 45° N, NNW-directed crustal shortening produced by the Mendocino crustal conveyor (MCC) and deformation associated with SNGV-block motion overprint the NE-directed Cascadia subduction coupling signal. Embedded in this overall pattern of crustal deformation is the rigid translation of the Klamath terrane, bounded on its north and west by localized zones of deformation. Since the MCC and SNGV processes migrate northward, their impact on the crustal deformation in southern Cascadia is a relatively recent phenomenon, since ~ 2 –3 Ma.

Modeling of the impact of rigid ellipsoidal blocks by means of an elastic-visco-plastic constitutive model

<p>Rockfall is one of the most common hazards in mountain areas causing severe damages to structures/infrastructures and, human lives. For this reason, numerous are the papers published in the last decades on this subject, both introducing reliable approaches to simulate the boulder trajectory and defining design methods for sheltering structures. As is well known, the most popular strategy to simulate the block trajectory and velocity is based on the lumped mass material point approach. This is capable of describing the block trajectory, before either its natural arrest or impact against an artificial/natural obstacle, by suitably considering its interaction with soil/rock materials, interaction always dynamic, very often highly dissipative and defined, according to its nature, as sliding, rolling or impact.</p><p>In this framework, this study focusses on impacts and, in particular, on the role of block geometry in affecting the block kinematic response. The problem is approached numerically; by modifying a previously conceived elastic-viscoplastic constitutive model, based on the macro-element concept. and capable of satisfactorily simulating impacts of spherical blocks.</p><p>The modified constitutive model relaxes the assumption of spherical block by assuming an ellipsoidal shape and by allowing for the boulder rotation. These two changes make the problem more complex but allow to model more realistically the impact. For the sake of simplicity, the results shown in this work consider the block motion to be planar, but the model already allows to include general three dimensional conditions.</p><p>In this work, the model is briefly outlined and the procedure for calibrating the model constitutive parameters described. Then, the results of an extensive parametric analysis, employing constitutive parameters calibrated on experimental data taken from the literature, are discussed. In particular, the role of (i) the inner block orientation, and (ii) the inner impact angle is considered in terms of both kinematic variables and restitution coefficients. Finally, interpolation functions to compute restitution coefficients, once both block shape and inner impact block orientation are known, are provided.</p>

Tracking Individual Targets for high Density Crowd Scenes Analysis

In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate.In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density.The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of the local densities in the Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented. Tracking in such places where pedestrian density reaches 7 to 8 Persons/m$^2$ is extremely challenging due to the small numberof pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The tracking method which is outlined in this paper overcomes these challenges by using a virtual camera which is matched in position, rotation and focal length to the original camera in such a way that the features of the 3D-model match the feature position of the filmed mosque. In this model an individual feature has to be identified by eye, where contrast is a criterion. We do know that the pilgrims walk on a plane, and after matching the camera we also have the height of the plane in 3D-space from our 3D-model. A point object is placed at the position of a selected pedestrian. During the animation we set multiple animation-keys (approximately every 25 to 50 frames which equals 1 to 2 seconds) for the position, such that the position of the point and the pedestrian overlay nearly at every time.By combining all these variables with the available appearance information, we are able to track individual targets in high density crowds.

BlockNet: A Deep Neural Network for Block-Based Motion Estimation Using Representative Matching

Owing to the limitations of practical realizations, block-based motion is widely used as an alternative for pixel-based motion in video applications such as global motion estimation and frame rate up-conversion. We hereby present BlockNet, a compact but effective deep neural architecture for block-based motion estimation. First, BlockNet extracts rich features for a pair of input images. Then, it estimates coarse-to-fine block motion using a pyramidal structure. In each level, block-based motion is estimated using the proposed representative matching with a simple average operator. The experimental results show that BlockNet achieved a similar average end-point error with and without representative matching, whereas the proposed matching incurred 18% lower computational cost than full matching.