A novel fine-resolution snow depth retrieval model to revealdetailed spatiotemporal patterns of snow cover in NortheastChina

Seasonal snow cover is a key component of the global climate and hydrological system,it has drawn considerable attention under global warming conditions.Although several passive microwave(PMW)snow depth(SD)products have been developed since the 1970s,they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD,especially in complex mountainous regions.In this paper,we developed afine-resolution SD retrieval model(FSDM)using machine learning to improve SD estimation quality for Northeast China and produced a long-term,fine-resolution,daily SD dataset.The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products.The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation,with the root-mean-square error(RMSE),bias,and correlation coefficient(R)of 7.10 cm,-0.13 cm,and 0.60.Additionally,we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range,Lesser Khingan Mountains,and Changbai Mountain regions.The SD exhibited high-low distribution patterns with the increased latitude.The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.

Computational Creation of Hollow Mask Type Illusionary Solids with Shading Effect

＂Hollow mask illusion＂ is an optical illusion and appears due to an error in the process of reconstructing the three-dimensional objects from our two-dimensional retinal image. In this paper, we present a computational method to create ＂Hollow mask＂ type new illusionary solid by calculating the hollow structure and its shading. The straight line Voronoi diagram for a given shape obtains the three-dimensional vertices of the hollow structure and the shading effect on each surface is calculated under the assumption that each surface has diffusely reflecting surface（Lambertian reflectance）. We also show two examples of our new illusionary solid works.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

The April 24, 2013 Changning M_s4.8 earthquake: a felt earthquake that occurred in Paleozoic sediment

The dense broadband seismic network provides more high-quality waveform that is helpful to improve constraint focal depth of shallow earthquake. Many shal- low earthquakes occurring in sediment were regarded as induced events. In Sichuan basin, gas industry and salt mining are dependent on fluid injection technique that triggers microseismicity. We method with regional records adopted waveform inversion to obtain focal mechanism of an Ms4.8 earthquake at Changning. The result suggested that the Changning earthquake occurred at a ESE thrust fault, and its focal depth was about 3 km. The depth phases including teleseismic pP phase and regional sPL phase shows that the focal depth is about 2 km. The strong, short- period surface wave suggests that this event is a very shallow earthquake. The amplitude ratio between Rayleigh wave and direct S wave was also used to estimate the source depth of the mainshock. The focal depth （2-4 km） is far less than the depth of the sedimentary layer thickness （6-8 km） in epicentral region. It is close to the depth of fluid injection of salt mining, which may imply that this event was triggered by the industrial activity.

Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.