Search for: All records

Abstract We construct a high‐resolution shear‐wave velocity (V_S) model for the uppermost 100 m using ambient noise tomography near the West Antarctic Ice Sheet Divide camp. This is achieved via joint inversion of Rayleigh wave phase velocity and H/V ratio, whose signal‐to‐noise ratios are boosted by three‐station interferometry and phase‐matched filtering, respectively. The V_Sshows a steep increase (0.04–0.9 km/s) in the top 5 m, with sharp interfaces at ∼8–12 m, followed by a gradual increase (1.2–1.8 km/s) between 10 and 45 m depth, and to 2 km/s at ∼65 m. The compressional‐wave velocity and empirically‐obtained density profile compares well with the results from Herglotz–Wiechert inversion of diving waves in active‐source shot experiments and ice core analysis. Our approach offers a tool to characterize high‐resolution properties of the firn and shallow ice column, which helps to infer the physical properties of deeper ice sheets, thereby contributes to improved understanding of Earth's cryosphere. 

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover completely human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject’s interaction with the surrounding, e.g., crossing behind another object, entering a a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal effect relations between an object’s visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method to challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions