Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction.

Landslides refer to occurrences of massive ground movements due to geological (and meteorological) factors, and can have disastrous impacts on property, economy, and even lead to the loss of life. The advances in remote sensing provide accurate and continuous terrain monitoring, enabling the study and analysis of land deformation which, in turn, can be used for land deformation prediction. Prior studies either rely on predefined factors and patterns or model static land observations without considering the subtle interactions between different point locations and the dynamic changes of the surface conditions, causing the prediction model to be less generalized and unable to capture the temporal deformation characteristics. To address these issues, we present DyLand, a dynamic manifold learning framework that models the dynamic structures of the terrain surface. We contribute to the land deformation prediction literature in four directions. First, DyLand learns the spatial connections of interferometric synthetic aperture radar (InSAR) measurements and estimates the conditional distributions on a dynamic terrain manifold with a novel normalizing flow-based method. Second, instead of modeling the stable terrains, we incorporate surface permutations and capture the innate dynamics of the land surface while allowing for tractable likelihood estimations on the manifold. Third, we formulate the spatiotemporal learning of land deformations as a dynamic system and unify the learning of spatial embeddings and surface deformation. Finally, extensive experiments on curated real-world InSAR datasets (land slopes prone to landslides) show that DyLand outperforms existing benchmark models.

High Detectivity of PbS Films Deposited on Quartz Substrates: The Role of Enhanced Photogenerated Carrier Separation.

PbS films grown on quartz substrates by the chemical bath deposition method were annealed in an O2 atmosphere to investigate the role of oxygen in the sensitization process at different annealing temperatures. The average grain size of the PbS films gradually increased as the annealing temperature increased from 400 °C to 700 °C. At an annealing temperature of 650 °C, the photoresponsivity and detectivity reached 1.67 A W-1 and 1.22 × 1010 cm Hz1/2 W-1, respectively. The role of oxides in the sensitization process was analyzed in combination with X-ray diffraction and scanning electron microscopy results, and a three-dimensional network model of the sensitization mechanism of PbS films was proposed. During the annealing process, O functioned as a p-type impurity, forming p+-type PbS layers with high hole concentrations on the surface and between the PbS grains. As annealing proceeds, the p+-type PbS layers at the grain boundaries interconnect to form a three-dimensional network structure of hole transport channels, while the unoxidized p-type PbS layers act as electron transport channels. Under bias, photogenerated electron-hole pairs were efficiently separated by the formed p+-p charge separation junction, thereby reducing electron-hole recombination and facilitating a higher infrared response.