Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out fro...Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.展开更多
In order to improve the accuracy of cable fault position location at a low cost and make the testing results intuitive, a cable fault detector based on wave form reconstruction is designed. In this detector, the cable...In order to improve the accuracy of cable fault position location at a low cost and make the testing results intuitive, a cable fault detector based on wave form reconstruction is designed. In this detector, the cable fault position is located based on the time-domain pulse reflection （TDR） principle. A pulse waveform is injected in the tested cable, and a high-speed comparator with changeable reference voltages is used to binarize the test pulse waveform to a binary sequence on a certain voltage. Through scanning the reference voltage in a full voltage range, multi-sequences are acquired to reconstruct the pulse waveform transmission in the cable, and then the pulse attenuation feature, electrical open circuit fault, electrical short circuit fault, and the fault position of the cable are diagnosed. Experimental results show that the designed cable fault detector can determine the fault type and its position of the cable being tested, and the testing results are intuitive.展开更多
The Mw 6.8 Adassil earthquake that occurred in the High Atlas on September 8,2023,was a catastrophic event that provided a rare opportunity to study the mechanics of deep crustal seismicity.This research aimed to deci...The Mw 6.8 Adassil earthquake that occurred in the High Atlas on September 8,2023,was a catastrophic event that provided a rare opportunity to study the mechanics of deep crustal seismicity.This research aimed to decipher the rupture characteristics of the Adassil earthquake by analyzing teleseismic waveform data in conjunction with interferometric synthetic aperture radar(InSAR)observations from both ascending and descending orbits.Our analysis revealed a reverse fault mechanism with a centroid depth of approximately 28 km,exceeding the typical range for crustal earthquakes.This result suggests the presence of cooler temperatures in the lower crust,which facilitates the accumulation of tectonic stress.The earthquake exhibited a steep reverse mechanism,dipping at 70°,accompanied by minor strike-slip motion.Within the geotectonic framework of the High Atlas,known for its volcanic legacy and resulting thermal irregularities,we investigated the potential contributions of these factors to the initiation of the Adassil earthquake.Deep seismicity within the lower crust,away from plate boundaries,calls for extensive research to elucidate its implications for regional seismic hazard assessment.Our findings highlight the critical importance of studying and preparing for significant seismic events in similar geological settings,which would provide valuable insights into regional seismic hazard assessments and geodynamic paradigms.展开更多
The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces ...The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.展开更多
基金supported by the National Key R&D Program of China(2017YFC1405600)the Fundamental Research Funds for the Central Universities(JB180213)
文摘Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.
基金The National Natural Science Foundation of China(No.61240032)the Natural Science Foundation of Jiangsu Province(No.BK2012560)+1 种基金the College Scientific and Technological Achievements Transformation Promotion Project of Jiangsu Province(No.JH-05)the Science and Technology Support Program of Jiangsu Province(No.BE2012740)
文摘In order to improve the accuracy of cable fault position location at a low cost and make the testing results intuitive, a cable fault detector based on wave form reconstruction is designed. In this detector, the cable fault position is located based on the time-domain pulse reflection （TDR） principle. A pulse waveform is injected in the tested cable, and a high-speed comparator with changeable reference voltages is used to binarize the test pulse waveform to a binary sequence on a certain voltage. Through scanning the reference voltage in a full voltage range, multi-sequences are acquired to reconstruct the pulse waveform transmission in the cable, and then the pulse attenuation feature, electrical open circuit fault, electrical short circuit fault, and the fault position of the cable are diagnosed. Experimental results show that the designed cable fault detector can determine the fault type and its position of the cable being tested, and the testing results are intuitive.
基金the National Natural Science Foundation of China(Grant Nos.42030311,and 42325401)the Science and Tech-nology Innovation Talent Program of Hubei Province(Grant No.2022EJD015).
文摘The Mw 6.8 Adassil earthquake that occurred in the High Atlas on September 8,2023,was a catastrophic event that provided a rare opportunity to study the mechanics of deep crustal seismicity.This research aimed to decipher the rupture characteristics of the Adassil earthquake by analyzing teleseismic waveform data in conjunction with interferometric synthetic aperture radar(InSAR)observations from both ascending and descending orbits.Our analysis revealed a reverse fault mechanism with a centroid depth of approximately 28 km,exceeding the typical range for crustal earthquakes.This result suggests the presence of cooler temperatures in the lower crust,which facilitates the accumulation of tectonic stress.The earthquake exhibited a steep reverse mechanism,dipping at 70°,accompanied by minor strike-slip motion.Within the geotectonic framework of the High Atlas,known for its volcanic legacy and resulting thermal irregularities,we investigated the potential contributions of these factors to the initiation of the Adassil earthquake.Deep seismicity within the lower crust,away from plate boundaries,calls for extensive research to elucidate its implications for regional seismic hazard assessment.Our findings highlight the critical importance of studying and preparing for significant seismic events in similar geological settings,which would provide valuable insights into regional seismic hazard assessments and geodynamic paradigms.
基金the financial support of the National Key R&D Program of China(2021YFC3000701)the China Seismic Experimental Site in Sichuan-Yunnan(CSES-SY)for providing data for this study.
文摘The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.
