Detecting pre-earthquake anomalies in Schumann Resonance (SR) data is a significant challenge due to the low signal-to-noise ratio, with faint precursor signals often obscured by strong electromagnetic background noise. To address this, this paper proposes a novel, two-stage hybrid filtering method. The approach first uses a one-dimensional convolutional neural network (1D-CNN) to learn the patterns of a robust sliding interquartile range (IQR) detector, thereby acquiring "prior knowledge," and then applies a fine-tuning stage to the network's weights to selectively enhance pre-seismic patterns. The method was developed and validated using a multi-year dataset (2013-2021) of SR spectrograms and corresponding seismic events in California. Experimental results demonstrate a significant improvement in signal clarity: the average proportion of anomalies occurring within the 20 days prior to an earthquake increased from 69.91% before filtering to 83.46% after, representing a noteworthy average uplift of 13.55%. This study confirms that our fine-tuned prior knowledge network is an effective approach for enhancing the visibility of potential seismic precursors in noisy SR data, reinforcing the potential of SR as a tool for short-term earthquake studies.

Seismic monitoring in the ultra-low frequency range (0.0083-20 Hz) is essential for understanding Earth's structure. Existing electrical instruments are effective but costly and challenging to deploy in complex environments, while FBG accelerometers are adaptable but limited in sensitivity at ultra-low frequencies. This study introduces a high-sensitivity ultra-low frequency three-axis FBG accelerometer with a multi-flexible beam structure. Sensitivity and resonant frequency formulas are derived, and optimization is performed using a differential evolutionary genetic algorithm. A prototype sensor, validated through modal simulations and low- frequency vibration tests, achieves a sensitivity of 1025 pm/g, cross-sensitivity < 4.10 %, and a dynamic range of 97 dB, a flat response in the 0.05-80 Hz frequency range, exhibiting satisfactory sensitivity and precise angular characterization. Additionally, for the first time, a three-channel correlation method was applied to the FBG accelerometer for seismometer self-noise testing. The results enable scalable FBG low-frequency seismic monitoring in harsh environments.

Because the detection of malicious files is not yet popular, there are often various "backdoors" for files on most forums. For Internet users who publish other people's privacy, bad comments or pictures in cyberspace, and upload and download malicious files in various forums and communities, this paper proposes that through the use of naive Bayesian algorithm and AC computer algorithm, we can quickly and accurately identify malicious content in text, pictures, and software. Through the secondary development of API, we can monitor hundreds of millions of data in real-time, It greatly alleviates the problem of detecting malicious files in the current environment.

Magnetic field measurement plays an extremely important role in various fields, such as earthquake-generating mechanisms, resource exploration, and national defense security. Addressing the current issues of low sensitivity and susceptibility to temperature in optical fiber magnetic field sensors, a high-sensitivity optical fiber extrinsic Fabry-Perot interferometer (EFPI) cavity magnetic field sensor based on the coupling of giant magnetostrictive material (GMM) and a Fabry-Perot (F-P) cavity is proposed. The sensor employs TbDyFe material as the sensing element and utilizes neodymium-iron-boron to provide a bias magnetic field, enhancing the conversion efficiency of the GMM rod. An F-P cavity is formed by precisely aligning the end face of a single-mode optical fiber with the smooth surface of a permanent magnet. When the external magnetic field changes, the length of the GMM rod changes accordingly, which in turn causes a variation in the length of the F-P cavity. Experimental results show that within the range of 0- 150 mu T, the highest sensitivity of the sensor reaches 676 nm/mT, corresponding to a magnetic field resolution of 147 nT. A fiber Bragg grating (FBG) temperature decoupling method is proposed to perform temperature compensation on the sensor and correct the magnetic field measurement results, thereby solving the problem of cross-sensitivity to temperature. The designed sensor features a compact structure and simple fabrication, providing a reference for the development of high-sensitivity optical fiber magnetic field sensors.

Strain monitoring is widely used in civil engineering, machinery, oil and gas exploration and other engineering fields. In order to solve the problems of low sensitivity and temperature cross-sensitivity of existing FBG strain sensors, a double FBG strain sensor based on integrated flexure hinge is proposed. The mechanical model is established, the theoretical formula is deduced, the structural parameters are optimized by the control variable method, and the prototype is developed after software simulation. The strain and temperature testing system is designed. The experimental results show that the sensitivity of the sensor is 5.09 pm/mu s, the linearity is more than 99 %, and the sensitivity after temperature compensation is 0.38 pm/degrees C. It is highly sensitive and stable and has the ability of temperature compensation, which provides new ideas for the development of similar sensors.

Low-temperature measurement is widely applied in fields such as aerospace, energy transportation, and structural health monitoring. In response to the low sensitivity of Fiber Bragg Grating (FBG) temperature sensors in low-temperature environments, a bimetallic sensitized FBG temperature sensor structure is proposed. Leveraging the difference in thermal expansion coefficients between Kovar alloy and 7075 aluminum for sensitization design, ANSYS is utilized for thermal stress analysis of the sensor, followed by the fabrication of the sensor prototype based on simulation results. Low-temperature glass soldering technique is employed instead of traditional adhesives to achieve a two-point welding fixation of the FBG with the sensitization structure, and a metal casing is used for encapsulation and protection. Finally, a temperature testing system is constructed to evaluate the sensor’s performance. Experimental results indicate that within the temperature range of − 60 to 50 °C, the developed sensor exhibits a temperature sensitivity of 77.075 pm/°C, a linear fit degree of 0.999, and good stability. The sensor’s simple packaging makes it easily realizable and holds significant potential for application in low-temperature measurement fields.

Monitoring low-frequency and ultralow-frequency ( $0.01\sim 20$ Hz) vibration signals is crucial for various engineering structures and projects, including rotating machinery, bridges, dams, marine platforms, nuclear power plants, and seismic research. To address the limitations of conventional fiber Bragg grating (FBG) sensors in detecting 0.01 Hz vibrations, a novel FBG sensor with a dual-spring leaf structure is proposed. The sensor's dynamic response formula is established, and the influence of structural parameters on sensitivity and natural frequency is analyzed. Simulation analysis is conducted to determine static stress and modal characteristics, followed by performance testing using an ultralow-frequency vibration test system. Results show that the proposed sensor can effectively monitor vibration signals in the 0.01-20 Hz frequency range with a natural frequency of 50 Hz. In addition, it demonstrates displacement sensing capabilities in the 0.01-1 Hz frequency band, providing valuable insights for similar sensor designs.

This paper utilized data acquired from the dense magnetometer array established as part of the National Key Research and Development Program project titled “Development of new portable seismic monitoring devices” in the southern segment of the Xiaojiang fault zone (Yuxi and Honghe regions of southern Yunnan). Employing the Empirical Mode Decomposition (EMD) method, a quality analysis was performed on the Z-component data outputs. The results indicate that the standard deviation of the synchronous differences in diurnal variations of the geomagnetic Z-component across various stations stabilizes within the range of 0.86 nT to 1.17 nT, with correlation coefficients for magnetically quiet days achieving notably high values between 0.9720 and 1. This highlights favorable consistency in the diurnal variation patterns, amplitudes, and conditions during quiet days. The outcomes of this study contribute significant insights into the temporal and spatial characteristics of geomagnetic field variations in the region, offering valuable references for comprehending local geophysical phenomena and potential seismic activities.

Simultaneous measurement of strain and temperature in vibrating environments has always been a key technical issue in the field of structural health monitoring. To address the bottleneck of inadequate dual-parameter measurement performance in existing Fiber Bragg Grating (FBG) strain and temperature sensors, a new type of fiber optic grating sensor with anti-vibration capabilities for the measurement of both strain and temperature is proposed. The sensor utilizes a flexible hinge structure made of 304 steel with high elastic modulus for strain sensitization and a bimetallic structure composed of 7075 aluminum and 4 J36 Invar alloy for temperature sensitization, integrating both types of sensors. Theoretical analysis of the sensor was conducted, followed by the fabrication of a prototype and the establishment of a testing system for performance evaluation. The results show that the designed dual-parameter FBG sensor has a strain sensitivity of approximately 3.06 pm/mu epsilon, about 2.62 times that of a bare FBG, and a temperature sensitivity of about 50.2 pm/degrees C, roughly 4.97 times that of a bare FBG. In anti-vibration performance tests between 5-100 Hz, the wavelength drift of the strain and temperature sections of the FBG in the sensor were both within 1 pm, meeting the requirements for simultaneous measurement of strain and temperature in vibrating environments. These findings provide a reference for the development of similar sensors and further improvement of the measurement performance of FBG strain and temperature sensors.

地磁台站观测有相对记录和绝对观测 2种,相对记录数据需经基线值改正后方可得到地磁场要素的实际值.磁通门经纬仪是获取基线值的基本设备之一,在地磁观测中发挥着重要作用.目前台站采用的磁通门经纬仪需人工操作,人为因素会对观测数据质量产生影响,因在偏远地区和环境恶劣地区无法实现地磁绝对观测,造成地磁绝对观测的空间覆盖空白.子午工程二期将在我国大陆地区首次开展自动化磁偏角和磁倾角地磁绝对观测,必将推动地磁绝对观测技术创新与发展.自动磁通门经纬仪是我国自主研发的磁偏角和磁倾角自动化观测设备,采用无磁材料和压电电机,通过优化设计与改进加工工艺,以及引入多参量误差补偿算法,有效克服和消除了系统误差,提高了测量精度.该设备在河北涉县台、吉林合隆台、陕西乾陵台和北京白家疃台等台站开展了长时间的实际观测,并与台站的人工磁通门经纬仪观测结果进行了对比.结果表明,自动磁通门经纬仪的主要性能指标达到了人工磁通门经纬仪水平.该设备也通过了中国地震局前兆设备入网测试,功能和性能指标符合地磁台站地磁绝对观测要求.

Fiber optic accelerometers have a wide range of applications in low-frequency vibration measurement, such as in earthquake monitoring, disaster early warning, underground resource exploration, and anti-seismic monitoring projects for critical structures like dams and bridges. In response to the current issues of Fiber Bragg Gratings (FBG) accelerometers being insensitive to low-frequency vibration signals and challenging to apply in engineering, a dual straight-wing FBG accelerometer for low-frequency vibration measurement is proposed. Firstly, a sensor model is established and theoretically analyzed. Secondly, the impact of the sensor's main structural parameters on sensitivity and natural frequency is analyzed, and the sensor is simulated using ANSYS. Finally, a prototype of the sensor is developed, and a low-frequency vibration testing system is set up to test the sensor's performance. Experimental results show that the accelerometer has a natural frequency of 39.1 Hz, operates in the 5-22 Hz frequency band, and has a sensitivity of 171.7 p.m./g, a dynamic range of 69.82 dB, and a transverse interference resistance of less than 9.5%. This research provides a new reference for the development of similar sensors.

Low-frequency vibration measurement is of great significance in fields such as earthquake and tsunami monitoring, large-scale structural health detection, and precision instrument manufacturing. Aiming at the difficulty of accurately extracting low-frequency microvibration signals, a kind of fiber Bragg grating (FBG) acceleration sensor combining a bearing and cantilever beam is proposed. The sensor pickup model is built, and its theoretical formula is derived. The optimization objective function of key parameters is solved by MATLAB, the simulation is carried out by ANSYS, and the sensor is developed and tested for performance. The research results indicate that the sensitivity of the sensor is 1226.6 pm/g, the natural frequency is 82 Hz, the flat response range is 0.3-57 Hz, the temperature sensitivity is 0.10 pm/celcius, and the lateral interference resistance is less than 6.88 %. The study's findings serve as a guide for enhancing the capability of low-frequency FBG acceleration sensors for lowfrequency vibration measurement.

Inclination monitoring plays a significant role in research on deformation monitoring of slopes, inclination monitoring of bridges, earthquake monitoring, and other areas of monitoring. Existing electromagnetic signal-based inclinometers face practical issues such as difficulty adapting to harsh environments, poor large-scale networking capabilities, and unstable signal transmission. Hence, what we believe to be a novel inclinometer based on fiber sensing principles is proposed. The sensor employs suspension sensing based on the plumb principle, using bearings to overcome mechanical friction caused by rigid fixation between the mass block and the cantilever, thereby improving sensitivity and accuracy of the sensor. Key structural parameters of the sensor were optimized and simulated, followed by fabrication of the sensor and performance test on an inclination test platform. Experimental results indicate that, within a measurement range of ±9∘, the sensor exhibited a sensitivity of 305.2 pm/°, a resolution of approximately 3.3×10-4 ∘, an accuracy of 2%, a repeatability error of 1.9%, and favorable creep resistance stability for long-term measurement, thus addressing the requirements for slope deformation monitoring.

In order to pick up the low frequency vibration signal, a triaxial dual FBG accelerometer based on a vibration-sensitive structure of cross diaphragm spring is proposed in this paper, then the modal analysis and parameter optimization are done by simulation analysis software, and the structure parameters of the triaxial dual FBG accelerometer are determined by combining the actual processing, at last, a very low frequency vibration testing system is built to test its vibration characteristics. The test results show that the natural frequency of the FBG accelerometer is 85 Hz, and the operating frequency is 0.1-50 Hz. The sensitivity of the three components are 590 pm/g, 390 pm/g, 395 pm/g respectively, the minimum detectable acceleration of accelerometer is 0.01 g and the maximum detectable acceleration is 3.0 g. This kind of FBG accelerometer with high sensitivity in low frequency band provides important reference value for the design of similar accelerometers.

Absolute observation and relative record are two methods in geomagnetic station observation. The actual values of geomagnetic field elements can be obtained after the relative record data is corrected by baseline data. Fluxgate theodolite is one of the basic tools required to obtain baseline values and plays an important role in geomagnetic observations. At present, fluxgate theodolite used in stations requires manual operation, and human factors affect the observation data quality. Because absolute geomagnetic observation cannot be realized in remote areas or areas with harsh environments, some blank areas in the coverage of geomagnetic absolute observation exist. The second phase of the Chinese Meridian Project will carry out automatic geomagnetic absolute observation of magnetic declination and inclination for the first time in China's mainland, which will promote the innovation and development of geomagnetic absolute observation technology. The automatic fluxgate theodolite is a self-developed equipment from China for automatic observation of magnetic declination and inclination. It adopts non-magnetic materials and piezoelectric motors, effectively overcomes and eliminates the systematic errors by optimizing the design, improves the machining process, and compensates for the multi-parameter error algorithm, so as to ensure measurement accuracy. The equipment has been applied to long-term practical observation in stations such as Shexian Station in Hebei Province, Helong Station in Jilin Province, Qianling Station in Shanxi Province and Baijiatuan Station in Beijing. Compared with the observation results of artificial fluxgate theodolite in these stations, the results show that the main performance of the automatic fluxgate theodolite is the same as that of the artificial fluxgate theodolite. The equipment has also passed a precursor equipment standard test from the China Earthquake Administration, and its functions and performance meet the requirements of absolute geomagnetic observation for geomagnetic stations.

Waveforms of artificially induced explosions and collapse events recorded by the seismic network share similarities with natural earthquakes. Failure to identify and screen them in a timely manner can introduce confusion into the earthquake catalog established using these recordings, thereby impacting future seismological research. Therefore, the identification and separation of natural earthquakes from continuous seismic signals contribute to the monitoring and early warning of destructive tectonic earthquakes. A 1D convolutional neural network (CNN) is proposed for seismic event classification using an efficient channel attention mechanism and an improved light inception block. A total of 9937 seismic sample records are obtained after waveform interception, filtering, and normalization. The proposed model can obtain better classification performance than other major existing methods, exhibiting 96.79% overall classification accuracy and 96.73%, 94.85%, and 96.35% classification accuracy for natural seismic events, collapse events, and blasting events, respectively. Meanwhile, the proposed model is lighter than the 2D convolutional and common inception networks. We also apply the proposed model to the seismic data recorded at the University of Utah seismograph stations and compare its performance with that of the CNN-waveform model.

Magnetic field sensors have extensive applications in various fields such as resource exploration, industrial production, and geomagnetic detection. Traditional electric magnetic field sensors have good stability, but they have disadvantages such as susceptibility to chemical corrosion, high cost, large size, and poor resistance to electromagnetic interference. In contrast, fiber optic magnetic field sensors have advantages such as compact structure, high precision, small size, and strong resistance to electromagnetic interference. Existing fiber optic magnetic field sensors mainly include the following four categories based on basic principles：based on magnetostrictive materials, based on magnetic fluid materials, based on Faraday effect, and based on fiber lasers. Among these, fiber optic magnetic field sensors based on magnetostriction have more advantages in manufacturing processes and applications. This article introduces the development of fiber optic sensing technology for magnetic field measurement, analyzes the principles, advantages and disadvantages, and research status of four different types of sensors. At the same time, the application of fiber optic magnetic field sensors in geomagnetic monitoring was also introduced. Finally, the challenges and future development trends faced by fiber optic magnetic field sensors were analyzed.

激光干涉绝对重力仪干涉信号处理算法的动态适应性研究是进行动态绝对重力测量的基本前提。本文基于希尔伯特变换、直接正交法、过零点法三种瞬时相位处理算法,提出了基于背景振动物理量的合理动态约束条件,即反向的振动速度不能超过落体的下落速度。构建的单频振动信号仿真实验表明:在满足约束条件时,采样频率为60 MHz,希尔伯特变换算法的精度优于10 -13 m/s~2,过零点算法的精度优于10 -9 m/s~2,直接正交算法的精度为（-7.9±2.0）×10 -8 m/s~2。基于海浪模拟平台的实测试验表明:满足约束条件时,这三类瞬时相位处理算法均适用于动态环境,并获得了标准差为4.6×10 -5 m/s~2的绝对重力测量值。更进一步,基于本文动态适应性结论对系泊和海面船载动态环境进行评估,结果表明:测量船在3级以下海况可以进行10 -5 m/s~2量级绝对重力的测量;在3—4级海况下,需根据振动信号对测量结果进行筛选以获得10 -5 m/s~2量级绝对重力测量结果;4级以上海况则不适宜进行动态绝对重力测量。

It is a challenge to detect pre-seismic anomalies by using only one dataset due to the complexity of earthquakes. Therefore, it is a promising direction to use multiparameteric data. The earthquake cross partial multi-view data fusion approach (EQ-CPM) is proposed in this paper. By using this method, electromagnetic data and seismicity indicators are fused. This approach tolerates the absence of data and complements the missing part in fusion. First, the effectiveness of seismicity indicators and electromagnetic data was validated through two earthquake case studies. Then, four machine learning algorithms were applied to detect pre-seismic anomalies by using the fused data and two original datasets. The results show that the fused data provided better performance than the single-modal data. In the Matthews correlation coefficient index, the results of our method showed an 8% improvement compared with the latest study.

This paper introduces the application of fiber optic jerk sensor in earthquake monitoring of high-rise buildings. The fiber optic jerk sensor can monitor different types and different intensities of seismic wave, and has good consistency with the standard piezoelectric accelerometer.