Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning (EEW) systems. Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise, missing P-waves and inaccurate P-wave arrival estimation. To address these issues, an automatic algorithm based on the convolution neural network (DPick) was developed, and trained with a moderate number of data sets of 17,717 accelerograms. Compared to the widely used approach of the short-term average/long-term average of signal characteristic function (STA/LTA), DPick is 1.6 times less likely to detect noise as a P-wave, and 76 times less likely to miss P-waves. In terms of estimating P-wave arrival time, when the detection task is completed within 1 s, DPick’s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band, and 1.6 times when the error band is 0.10 s. This verified that the proposed method has the potential for wide applications in EEW.

Deep learning for P-wave arrival picking in earthquake early warning

From real to virtual sensors, an artificial intelligence approach for the industrial phase of end-of-line quality control of GDI pumps

50Hz global positioning system(GPS)high rates data of 8continual operation reference system(CORS)sites nearby Lushan are used to analyze the earthquake trigger time,epicenter and magnitude.All sites position time series on N,E directions are resolved with the track software,and horizontal accurancy can reach to 2cm at least.Horizontal position time series analyses show that the earthquake mainly affects QLAI,SCTQ,YAAN sites,the horizontal peak amplitudes can reach to50 mm,the maximum instantaneous velocity can reach to 72.36 mm/s and the maximum instantaneous acceleration have reached to 105.9mm/s2.Analyzing the position time series by the method of S transformation,the arrival time of seismic wave is estimated.With seismic wave arriving time and coordinates of three sites which are first detecting the seismic wave,the earthquake's epicenter and trigger time can be fast determined by three-dimensional search method.Moreover,the earthquake magnitude can also be estimated by horizontal peak amplitudes from the sites using the regression method.These suggest exiting GPS infrastructure could be developed into an effective component of earthquake assessment.

Quasi-real time determination of 2013Mw 6.6Lushan Earthquake Epicenter,Trigger Time And Magnitude Using 50Hz GPS Observations

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. 

Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing. Deep learning has demonstrated outstanding performance in various image processing tasks, making it a popular research topic. In recent years, image matting methods based on deep learning have gained significant attention due to their superior performance. Therefore, this article presents a comprehensive overview of the deep learning-based image matting algorithms that have been proposed in recent years. This paper initially introduces frequently used datasets and their production methods, along with the basic principles of traditional image matting techniques. We then analyze deep learning-based matting algorithms in detail and introduce commonly used image matting evaluation metrics. Additionally, this paper discusses the application scenarios of image matting, conducts experiments to illustrate the limitations of current image matting methods, and outlines potential future research directions in this field. Overall, this paper can serve as a valuable reference for researchers that are interested in image matting.

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Deep Learning Methods in Image Matting: A Survey

A New Point Cloud Simplification Method with Feature and Integrity Preservation by Partition Strategy

This study explores the quasi-real time inversion principle and precision estimation of three-dimensional coordinates of the epicenter, trigger time and magnitude of earthquakes with the aim to improve traditional methods, which are flawed due to missing information or distortion in the seismograph records. The epicenter, trigger time and magnitude from the Lushan earthquake are inverted and analyzed based on high-frequency GNSS data. The inversion results achieved a high precision, which are consistent with the data published by the China Earthquake Administration. Moreover, it has been proven that the inversion method has good theoretical value and excellent application prospects.

Quasi-real time inversion method of three-dimensional epicenter coordinate, trigger time, and magnitude based on CORS

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information.

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Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method

Extracting facade maps from 3D point clouds is a fast and economical way to describe a building’s surface structure. Existing methods lack efficiency, robustness, and accuracy, and depend on many additional features such as point cloud reflectivity and color. This paper proposes a robust and automatic method to extract building facade maps. First, an improved 3D Hough transform is proposed by adding shift vote and 3D convolution of the accumulator to improve computational efficiency and reduce peak fuzziness and dependence on the step selection. These modifications make the extraction of potential planes fast and accurate. Second, the coplane and vertical plane constraints are introduced to eliminate pseudoplanes and nonbuilding facades. Then, we propose a strategy to refine the potential facade and to achieve the accurate calibration and division of the adjacent facade boundaries by clustering the refined point clouds of the facade. This process solves the problem where adjoining surfaces are merged into the same surface in the traditional method. Finally, the extracted facade point clouds are converted into feature images. Doors, windows, and building edges are accurately extracted via deep learning and digital image processing techniques, which combine to achieve accurate extraction of building facades. The proposed method was tested on the MLS and TLS point cloud datasets, which were collected from different cities with different building styles. Experimental results confirm that the proposed method decreases computational burden, improves efficiency, and achieves the accurate differentiation of adjacent facade boundaries with higher accuracy compared with the traditional method, verifying the robustness of the method. Additionally, the proposed method uses only point cloud geometry information, effectively reducing data requirements and acquisition costs.

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Dongsheng Xiao

Papers

A New Point Cloud Simplification Method with Feature and Integrity Preservation by Partition Strategy

Quasi-real time inversion method of three-dimensional epicenter coordinate, trigger time, and magnitude based on CORS

Cloud Removal from Satellite Images Using a Deep Learning Model with the Cloud-Matting Method

A Robust Automatic Method to Extract Building Facade Maps from 3D Point Cloud Data