For storage and recovery requirements on large-scale seismic waveform data of the National Earthquake Data Backup Center (NEDBC), a distributed cluster processing model based on Kafka message queues is designed to optimize the inbound efficiency of seismic waveform data stored in HBase at NEDBC. Firstly, compare the characteristics of big data storage architectures with that of traditional disk array storage architectures. Secondly, realize seismic waveform data analysis and periodic truncation, and write HBase in NoSQL record form through Spark Streaming cluster. Finally, compare and test the read/write performance of the data processing process of the proposed big data platform with that of traditional storage architectures. Results show that the seismic waveform data processing architecture based on Kafka designed and implemented in this paper has a higher read/write speed than the traditional architecture on the basis of the redundancy capability of NEDBC data backup, which verifies the validity and practicability of the proposed approach.
In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.
To solve the problems of slow information acquisition, low processing efficiency, weak information storage and communication ability in earthquake rescue, an earthquake emergency command system on the basis of cloud computing and Internet of Things (IoT) is designed. First, the cloud computing technology is introduced, and the traditional earthquake emergency command system built by cloud computing and the Internet of things is analyzed. Then, based on the satellite remote sensing data, the characteristics of the middle-wave infrared remote sensing data before and after the recent earthquakes in China are explored. Subsequently, a new earthquake emergency command system is built based on cloud computing and Internet of things technology along with the data from the satellite middle-wave infrared remote sensing. Finally, the feasibility of the system is evaluated. The results show that the surface radiation changes significantly before the earthquake, the infrared brightness temperature difference value also fluctuates violently, and the abnormal area will gradually get closer to the epicenter as time goes by. The peak value of the relative power spectrum in the earthquake is more than 9 times of the average value in the normal time. In conclusion, the evaluation result of the emergency command system based on satellite remote sensing data, cloud computing, and Internet of things is good, suggesting satellite infrared remote sensing data can be applied to earthquake prediction, and the earthquake emergency command system constructed combining cloud computing and Internet of things technology has a good feasibility.
Highly similar waveforms recorded from repeating earthquakes can be utilized to evaluate the data quality of a seismic station. We used a hypothesis testing method to establish a data quality detection model based on repeating earthquakes. The model effectiveness was verified by using waveforms of a pair of repeating earthquakes, which occurred in northeastern Japan on 20 March 2021 (Mw=7.0) and 1 May 2021 (Mw=6.9), from 109 stations in the Global Seismographic Network. A total of 842 permanent broadband stations in mainland China were evaluated using this model. Eighteen anomalies were found mainly attributed to calibration, instrument noise, mass recentering, and regional long-period interference. We found that most of the stations function well. Moreover, utilizing repeating earthquakes to analyze the waveform quality can circumvent the need for extensive forward calculations, as well as greatly reduce the influence of source parameter uncertainties and structural complexity on the seismogram. Additionally, the need for detection in other datasets in different regional networks has broadened the scope of these applications.
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