Rapid earthquake focal mechanism inversion using high-rate GPS velometers in sparse network

In this study, we demonstrate an approach for inverting earthquake source parameters based on high-rate global positioning system （GPS） velocity seismograms. The velocity records obtained from single-station GPS velocity solutions with broadcast ephemeris are used directly for earthquake source parameter inversion using the Cut and Paste method, without requiring conversion of the velocity records into displacement records. Taking the E1 Mayor-Cucapah earthquake as an example, GPS velocity records from 10 stations with reasonable azimuthal coverage provide earthquake source parameters very close to those from the Global centroid moment tensor （Global CMT） solution. In sparse network tests, robust source parameters with acceptable bias can be achieved with as few as three stations. When the number of stations is reduced to two, the bias in rake angle becomes appreciable, but the magnitude and strike estimations are still robust. The results of this study demonstrate that rapid and reliable estimation of earthquake source parameters can be obtained from GPS velocity data. These parameters could be used for early earthquake warning and shake map construction, because such GPS velocity records can be obtained in real time.

Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array

The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples.In particular,the data-driven mechanism of deep learning cannot identify false samples,aggravating the difficulty in noncooperative underwater target recognition.A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively.The sound field cross-correlation compression(SCC)feature is developed to reduce noise and computational redundancy.Starting from an incomplete dataset,a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity,aiming to discover the unknown underwater targets.The adversarial prediction label is converted to initialize the joint co-forest,whose evaluation function is optimized by introducing adaptive confidence.The experiments prove the strong denoising performance,low mean square error,and high separability of SCC features.Compared with several state-of-the-art approaches,the numerical results illustrate the superiorities of the proposed method due to feature compression,secondary recognition,and decision fusion.

Prediction of Disease Transmission Risk in Universities Based on SEIR and Multi-hidden Layer Back-propagation Neural Network Model

Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.

Comparison between several multi-parameter seismic inversion methods in identifying plutonic igneous rocks

With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks. The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI), Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion. Through the application of a variety of inversion methods with log curves correction, we obtained relatively high-resolution impedance and velocity sections, effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of igneous rocks. By means of a comprehensive comparative study, we arrived at the following conclusions: the CSSI inversion has good waveform continuity, and the ANN inversion has lower resolution than the CSSI inversion. The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.

Sparse rail network robustness analysis:Functional vulnerability levels of accidents resulting from human errors

Transportation network vulnerability analysis has developed increasingly in the last decade with the goal to identify the most critical locations against incidences.In this domain,many of the previous researches have focused on congested urban networks;however,there is still a need to consider regional and interurban sparse rail networks,specifically those networks in developing countries.In such sparse rail networks,there are limited possibilities to redirect trains if a link is disrupted,there might be less possibility of finding redundant alternative routes,and network failures are usually accompanied by a phenomenon called‘unsatisfied demand.’The study reported in this paper stemmed from research aimed to design precautionary actions for a developing country’s sparse railway system.Our study framework deemed to find the most vulnerable part of an inter-urban sparse rail network using a network scan approach,which found the consequences of network disruptions.A number of criteria were defined to determine the total cost including unsatisfied demand and additional transportation costs due to disruptions.The results showed that how well the process of the vulnerability analysis,considering the features of both supply and demand sides,can be a guide for railway authorities in applying system safety measures.

Contact modeling and prediction-based routing in sparse mobile networks

Mobile ad-hoc networks(MANETs)provide highly robust and self-configuring network capacity required in many critical applications,such as battlefields,disaster relief,and wild life tracking.In this paper,we focus on efficient message forwarding in sparse MANETs,which suffers from frequent and long-duration partitions.Asynchronous contacts become the basic way of communication in such kind of network instead of data links in traditional ad-hoc networks.Current approaches are primarily based on estimation with pure probability calculation.Stochastic forwarding decisions from statistic results can lead to disastrous routing performance when wrong choices are made.This paper introduces a new routing protocol,based on contact modeling and contact prediction,to address the problem.Our contact model focuses on the periodic contact pattern of nodes with actual inter-contact time involved,in order to get an accurate realization of network cooperation and connectivity status.The corresponding contact prediction algorithm makes use of both statistic and time sequence information of contacts and allows choosing the relay that has the earliest contact to the destination,which results in low average latency.Simulation is used to compare the routing performance of our algorithm with three other categories of forwarding algorithm proposed already.The results demonstrate that our scheme is more efficient in both data delivery and energy consumption than previously proposed schemes.