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Purpose In response to the common low sensitivity of fiber Bragg grating (FBG) temperature sensors in measurement, an FBG temperature sensor sensitized in a substrate-type package structure is proposed. Design/methodology/approach The sensitivity of sensors is analyzed theoretically; aluminum alloys with large coefficient of thermal expansion are used; the ANSYS software is used for simulation analysis and optimization design of sensors; real sensors are developed based on simulation results; in this study, a test system was built to test the performance of the proposed sensor. Findings The results suggested that the sensitivity of encapsulated FBG temperature sensor is 27.3 pm/°C in the range of −20 °C to 40 °C, which is 2.7 times that of bare FBG sensor, while the linearity is up to more than 0.99. Practical implications The sensitivity of FBG temperature sensor is greatly improved by the design of the structure. Originality/value This study innovatively proposes substrate-type sensitized FBG temperature sensor. The temperature sensitivity of fiber grating can be improved by single metal structure, and the effect of structural strain can be reduced by a tab structure. The study results provide a reference for the development of like sensors and the further improvement in the sensitivity of FBG temperature sensors.

As the volume of seismic observation time-series data grows larger, web-based visualization schemes suffer from longer system response times. Although big data visualization schemes based on sampling and filtering can greatly reduce the data scale and shorten transmission time, what it gains in speed it loses in information. Progressive visualization has become an increasingly popular scheme because it can quickly “see” some results without having to wait for all the data, thus enabling users to grasp a data-change trend quickly and perceive the rules behind it. In this paper, a Cloudberry-based progressive real-time visualization schema for earthquake big data (PVSEBD) is proposed for the first time. It greatly shortens the transmission time of each data slice, improves the user interaction experience, and meets the long-term, large-scale visualization needs of earthquake consultation business. Because the correctness of average aggregation function (AVG) in progressive visualization is often not guaranteed, this paper proposes an innovative AVG translation rule solution based on the accumulability of the COUNT and SUM aggregation functions. The experimental results showed that PVSEBD automatically adjusts the amount of data returned each time according to size and has a shorter response time for each interaction compared with the solution based on the web-based visualization toolkit Portable Progressive Parallel Processing Pipeline (P5). &nbsp

In order to quickly and comprehensively detect residual stress, microcracks and other damages in ferromagnetic materials, this study developed an integrated magnetic non-destructive testing system with changeable magnetic field excitation based on anisotropic magnetoresistive (AMR) sensors. The system integrated magnetic memory detection, magnetic flux leakage (MFL) detection and magnetic Barkhausen noise (MBN) detection and was used to perform detection experiments on a 45# steel test-piece with precast residual stress zones and cracks in different directions. The experimental results showed that the proposed system can detect residual stress in a ferromagnetic test-piece, identify microcrack defects of different angles and quantitatively evaluate the residual stress. This paper provided a useful reference for the comprehensive evaluation of damages in ferromagnetic materials.

Inter-seasonal and geodynamics-related gravity changes are important geoscientific signals that are extractable from gravimeter observations after removing background information as local hydrology gravity effect. With two superconducting gravimeters (SGs: OSG-053 and iGrav-007) located in different tectonic units, continuous global navigation satellite system data and absolute gravity observations, Wuhan, China, is an ideal location for investigating the effects of gravity resulting from significant local hydrology mass variations. We have processed approximately 26 months of gravity data collected from the SGs in Wuhan and obtained residuals of [Formula: see text] for OSG-053 and [Formula: see text] for iGrav-007. The hydrological observations indicate an estimated gravity increase of [Formula: see text] near iGrav-007, which mainly results from an increase in unconfined water level with an aquifer-specific yield of approximately 0.1. However, the gravity changes around OSG-053 are mainly from soil moisture and reach −[Formula: see text]. The soil type, thickness, and water content parameters are obtained from hydrogeological surveys and drilling data. The deep confined water level rises by 2.5 m, which introduces a [Formula: see text] gravity variation with a specific storage approximately 0.00001 from the field unsteady-flow pumping test. The modeled gravity is approximately [Formula: see text] around OSG-053 and [Formula: see text] around iGrav-007, in accordance with the observed gravity variations. The difference in gravity changes between the two SG observations can be explained by different local water storage environments. Our results suggest that unconfined and soil water significantly impact the in-situ gravimetry, and that further detailed hydrogeological surveys are required. A combined investigation of gravity and water levels can be a useful approach for monitoring aquifer storage conditions and groundwater management.

The stress testing and evaluation of ferromagnetic materials that are widely applied in engineering has always been a focus of, and presented difficulties for, non-destructive testing. As there is still no effective method for detecting the stress of ferromagnetic materials, this paper puts forward the idea of applying the magnetic anisotropy method based on the inverse magnetostriction effect in stress testing of ferromagnetic materials. According to the principle of the magnetic anisotropy method, this paper discusses the development of Mn-Zn ferrite probes of three different structures, the construction of a magnetic anisotropy testing system comprising an excitation system, a signal collecting system and a signal processing system and the way in which a testing experiment was conducted on a 16MnR steel plate specimen under different conditions of stress, frequency and excitation voltage. All three types of probe can effectively determine the stress location of the specimen and present different phenomena and characteristics of the test. According to the experiment, significant correlation is seen between the stress and the magnetic signal, which provides a new idea for stress testing of ferromagnetic materials.

Purpose This study aims to solve the problem of temperature cross sensitivity of fiber Bragg grating in structural health monitoring, proposing a novel acceleration sensor based on strain chirp effect which is insensitive to temperature. Design/methodology/approach A kind of M-shaped double cantilever beam structure is developed. The fiber grating is pasted in the gradient strain region of the beam, and the chirp effect is produced under the action of non-uniform stress, and then the change of acceleration is converted into the change of reflection bandwidth to demodulate and eliminate the temperature interference. Through theoretical analysis, simulation and experimental verification with rectangular beam sensor. Findings The results show that the sinusoidal curvature beam sensor is insensitive to the change of temperature and is more likely to produce chirp effect. The sensitivity is about 317 pm/g, and the natural frequency is 56 Hz. Originality/value This paper fulfils an insensitive to temperature changes sensor which has effectively solved the temperature cross-sensitivity problem in building structure health monitoring.

In low-permeability porous media, the velocity of a fluid flow exhibits a nonlinear dependence on the imposed pressure gradient. This non-Darcian flow behavior has important implications to geological disposal of nuclear waste, hydrocarbon extraction from shale, and flow and transport in clay-rich aquifers. Temperature has been postulated to affect the threshold pressure gradient of a non-Darcian flow; however, the supporting data is very limited. In this study we for the first time report a systematic measurement of the threshold pressure gradient under various permeabilities and temperatures. The results show that a higher temperature leads to a lower threshold pressure gradient under the same permeability and a faster reduction of the threshold pressure gradient with increasing permeability. The experimental data are fitted to a two-parameter model to determine the parameters, h0 and a, which characterize the interfacial fluid-solid interactions and the transition between the Darcy and non-Darcian regimes.

The measurement of low-frequency vibration signals is a very important job in the fields of earthquake early warning, health monitoring of large-scale engineering structures, and geological exploration. Aiming at the problem of Fiber Bragg Grating (FBG) acceleration sensors’ cross-sensitivity to temperature and strain when measuring low-frequency vibration signals, this paper developed an M-shaped double cantilever beam structure that can produce chirp effect with its own structural characteristics, thereby making the sensor insensitive to temperature. Through theoretical analysis and simulation, the paper obtained the relationship between the reflection spectrum bandwidth of the chirped FBG and its acceleration. It adopted a spectrometer and a vibration test system to detect the reflection spectrum bandwidth of the FBG, and then obtained the acceleration. The experimental results showed that, compared with bare fiber grating, the temperature sensitivity of the proposed sensor was significantly lower, and its reflection spectrum bandwidth was not sensitive to temperature changes, moreover, there are good linear relationships between the reflection spectrum bandwidth, the power of the light, and the acceleration. The sensitivity was about 256 pm/g, the natural frequency was 66 Hz; therefore, the proposed sensor had realized high-performance detection of low-frequency vibration signals.

Temperature measurement is of great significance for research in the health monitoring of large structures and earthquake precursors. Against the frequently reported low sensitivity of fiber Bragg grating (FBG) temperature sensors, a bimetallic-sensitized FBG temperature sensor was proposed. The sensitization structure of the FBG temperature sensor was designed, and the sensor sensitivity was analyzed; the lengths and materials of the substrate and strain transfer beam were analyzed theoretically; 7075 aluminum and 4J36 invar were chosen as bimetallic materials, and the ratio of the substrate length to the sensing fiber length was determined. The structural parameters were optimized; the real sensor was developed, and its performance was tested. The results suggested that the sensitivity of the FBG temperature sensor was 50.2 pm/°C, about five times than that of a bare FBG sensor, and its linearity was more than 0.99. The findings offer a reference for developing sensors of the same type and further improving the sensitivity of the FBG temperature sensors.

A modelling method integrating principal component analysis (PCA) and the least-squares support vector machine with particle swarm optimisation (PSO-LSSVM) is proposed to address the difficulties in quantitative identification of fatigue cracks. The widths and depths of fatigue cracks are quantitatively identified by establishing a non-linear mapping relationship between these dimensions and magnetic flux leakage (MFL) signals. A series of fatigue crack samples are prepared through the fatigue tensile test for the MFL detection system. A sample library is established through MFL experimentation to verify the feasibility of the method for quantitative identification of fatigue cracks based on PSO-LSSVM. The results indicate that the method is in a position to effectively and quantitatively identify the widths and depths of fatigue cracks less than 1 mm in size with a maximum error of approximately 0.3 mm.

Acceleration detection is an important technology in the fields of seismic monitoring, structural health monitoring and resource exploration. A FBG acceleration sensor with the combination of L-shaped rigid beam and spring structure based on bearings is proposed against the low sensitivity that predominates in the low-frequency vibration measurement by FBG acceleration sensors, where L-shaped rigid beam is utilized to amplify the vibration signal, and is fixed by the bearings at both ends to effectively suppress the transverse crosstalk. The effects of structural parameters on the sensitivity and natural frequency of the sensors were analyzed using Origin theory, and such parameters were optimized; next, static stress and modal simulation analysis was made using COMSOL; in the end, a test system was built to test the performance of the real sensors. According to the findings, the acceleration sensor, whose natural frequency is 57 Hz, is of a flat sensitivity response in the low frequency range of 1–35 Hz, with the dynamic range being 89.83 dB, the acceleration sensitivity being up to 1241.85 pm/g, the coefficient of determination R2 for the sensitivity fit is 0.9997, and the transverse crosstalk being -26.20 dB within the operating frequency band. The findings offer a reference for improving the low-frequency vibration measurement capability of FBG acceleration sensors.

Geomagnetism, similar to other areas of geophysics, is an observation-based science. Data agreement between comparative geomagnetic vector observations is one of the most important evaluation criteria for high-quality geomagnetic data. The main influencing factors affecting the agreement between comparative observational data are the attitude angle, scale factor, long-term time drift, and temperature. In this paper, we propose a method based on a genetic algorithm and linear regression to correct for these effects and use the distribution pattern of points in Bland–Altman plots with a 95% confidence interval length to qualitatively and quantitatively evaluate the agreement between the comparative observational data. In Bland–Altman plots with better agreement, that is, with the corrected data, more than 95% of the points are distributed within the 95% confidence interval and there is no obvious pattern in the distribution of the points. Meanwhile, the length of 95% confidence interval decreased significantly after the correction. The method presented here has positive effects on the vector instrumentation detection and would enhance the robustness of geomagnetic observatory by bringing the data quality of the backup variometer data in line with the primary variometer. Graphical Abstract

Traditional fluxgate sensors used in geomagnetic field observations are large, costly, power-consuming and often limited in their use. Although the size of the micro-fluxgate sensors has been significantly reduced, their performance, including indicators such as accuracy and signal-to-noise, does not meet observational requirements. To address these problems, a new race-track type probe is designed based on a magnetic core made of a Co-based amorphous ribbon. The size of this single-component probe is only Φ10 mm × 30 mm. The signal processing circuit is also optimized. The whole size of the sensor integrated with probes and data acquisition module is Φ70 mm × 100 mm. Compared with traditional fluxgate and micro-fluxgate sensors, the designed sensor is compact and provides excellent performance equal to traditional fluxgate sensors with good linearity and RMS noise of less than 0.1 nT. From operational tests, the results are in good agreement with those from a standard fluxgate magnetometer. Being more suitable for modern dense deployment of geomagnetic observations, this small-size fluxgate sensor offers promising research applications at lower costs.

Studying the temporal and spatial evolution trends in earthquakes in an area is beneficial for determining the earthquake risk of the area so that local governments can make the correct decisions for disaster prevention and reduction. In this paper, we propose a new method for analyzing the temporal and spatial evolution trends in earthquakes based on earthquakes of magnitude 3.0 or above from 1980 to 2019 in California and Nevada. The experiment’s results show that (1) the frequency of earthquake events of magnitude 4.5 or above present a relatively regular change trend of decreasing–rising in this area, (2) by using the weighted average center method to analyze the spatial concentration of earthquake events of magnitude 3.0 or above in this region, we find that the weighted average center of the earthquake events in this area shows a conch-type movement law, where it moves closer to the center from all sides, (3) the direction of the spatial distribution of earthquake events in this area shows a NW–SE pattern when the standard deviational ellipse (SDE) method is used, which is basically consistent with the direction of the San Andreas Fault Zone across the north and south of California, and (4) the spatial distribution pattern of the earthquake events in this region is found to be clustered using the global spatial autocorrelation analysis method. This study provides a new perspective for the exploration of the temporal and spatial evolution trends in earthquakes and understanding the earthquake risk in an area.

The measurement of low-frequency vibration signals is of great significance in seismic monitoring, health monitoring of large and medium-sized engineering structures, and resource exploration. In view of the low sensitivity of fiber Bragg grating (FBG) acceleration sensors in measuring low-frequency vibration signals, a high-sensitivity, low-frequency dual-FBG acceleration sensor is proposed. Theoretical formula derivation and ANSYS software were used to optimize the design and simulation analysis of the structural parameters of the sensor. The real sensor was made based on the simulation results, and a test system was established to test its performance. According to the findings, the natural frequency of the acceleration sensor is 65 Hz. It gives a flat sensitivity response in the low frequency band of 3–45 Hz. The dynamic range is 92.63 dB at 10 Hz, the acceleration sensitivity is 1498.29 pm/g, and the linearity R 2 is 0.9998. The relative standard deviation of the sensor repeatability is 1.75%, and the transverse crosstalk in the working frequency band is − 33.99 d B . Designed with a high sensitivity and excellent temperature compensation capacity in the low-frequency band, the designed sensor is suitable for low- and medium-frequency vibration detection in engineering.

The heap-based optimizer (HBO) is an optimization method proposed in recent years that may face local stagnation problems and show slow convergence speed due to the lack of detailed analysis of optimal solutions and a comprehensive search. Therefore, to mitigate these drawbacks and strengthen the performance of the algorithm in the field of medical diagnosis, a new MGOHBO method is proposed by introducing the modified Rosenbrock's rotational direction method (MRM), an operator from the grey wolf optimizer (GWM), and an orthogonal learning strategy (OL). The MGOHBO is compared with eleven famous and improved optimizers on IEEE CEC 2017. The results on benchmark functions indicate that the boosted MGOHBO has several significant advantages in terms of convergence accuracy and speed of the process. Additionally, this article analyzed the diversity and balance of MGOHBO in detail. Finally, the proposed MGOHBO algorithm is utilized to optimize the kernel extreme learning machines (KELM), and a new MGOHBO-KELM is proposed. To validate the performance of MGOHBO-KELM, seven disease diagnostic questions were introduced for testing in this work. In contrast to advanced models such as HBO-KELM and BP, it can be concluded that the MGOHBO-KELM model can achieve optimal results, which also proves that it has practical significance in solving medical diagnosis problems.

The collection of low-frequency vibration signals is very important for the monitoring research in various fields such as national defense engineering, bridges and dams, aerospace, and the earthquake resistance of civil building structures. During the structural design of existing cantilever Fiber Bragg Grating (FBG) acceleration sensors, the tilt angle of the cantilever is often ignored, therefore, aiming at this problem, this paper proposed a new FBG acceleration sensor with double titled cantilevers, and used formula derivation and ANSYS software to optimize the structural parameters of the proposed sensor and conduct simulation analysis. Then, according to the simulation results, two sensor prototypes were fabricated, one with tilted cantilevers and the other with horizontal cantilevers, then, the performance of the two was tested, compared, and analyzed to verify the impact of the tilt angle of cantilevers on the performance of the sensor. The experimental results showed that, the natural frequency of the proposed sensor was about 46 Hz, which can be used to monitor vibration signals with the 6-30 Hz frequency range; the sensitivity of the proposed sensor was about 4.16 pm/m ⋅ s−2; and its degree of lateral an-interference was less than 5.62%. Compared with the sensor with horizontal cantilevers, the flat response area of the sensor with tilted cantilevers increased by about 30%, while its sensitivity reduced by about half. The research findings of this paper provide a useful reference for the research on similar type sensors and further expansion of the measurement range of FBG acceleration sensors.

Measurement error arising from vibration interference is recognized as the primary obstacle limiting the accuracy and stability of laser interference absolute gravimeters. The present work addresses this issue by proposing a global search optimization algorithm that determines the optimal absolute value of gravity based on the measured time-displacement coordinates of a falling body and the signal obtained from the passive vibration isolation system of the inertial reference corner-cube in a laser interference absolute gravimeter. Results of numerical calculations conducted under vibration interference conditions with added white noise resulting in a signal-to-noise ratio of 40 dB demonstrate the following. (1) The accuracy and standard deviation of the gravimeter obtained using the proposed algorithm are −0.04 μGal (1 μGal = 1 × 10−8 m/s2) and 0.24 μGal, respectively, while those values obtained by the standard least-squares solution are 10.19 μGal and 154.11 μGal, respectively. (2) The resolution of the test results shows that the average response of the reference value of acceleration due to gravity superimposed by a disturbance of 1.00 μGal is 1.01 μGal using the proposed algorithm and 0.87 μGal using the standard least-squares solution.

In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1–20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude–frequency response of its input and output within −3 dB is DC −80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.

A fast Fourier transform was applied to fit the geomagnetic diurnal variation. Fitting results showed that when the polynomial degree was greater than 160, the residual error was close to 0 nT. White noise is the main component of the residual error when the polynomial degree was greater than 160, so this method was adopted to calculate the background noise of the geomagnetic field. Spectrum analysis further demonstrated that the noise estimation result is reliable.

To achieve long-term geomagnetic vector field measurements, high resolution, low noise, reliability, and great stability are required, relative to the recording instrument. In this paper, we report the design and production of a high-performance magnetometer based on the fluxgate effect. To avoid drift due to the tilt of the observation pillar, which is often the main cause of baseline drift, and leveling error due to the installation process, the developed sensor was fixed to a suspended platform for automatic horizontal leveling. A special gimbaled construction was used, which allowed automatic leveling of the vertical position of the z-axis within tilts of ±6° of arc. The performance of the proposed sensor was tested in a zero-magnetic-field device, and the results showed that the accuracy of the scale factor was 0.01%, the standard deviation of the residuals was 0.1723, and the noise was ⩽0.08 nT (Peak-Peak). At the time of this research, the developed instrument had been in continuous operation for >6 months in the Jinghai geomagnetic observatory. The monthly residual standard deviation maximum values of the D, H, and Z components were 0.68 nT, 0.58 nT, and 0.44 nT respectively, which were smaller than the requirements of the China Geomagnetic Network. Therefore, the observational data and the baseline values indicated that the prototype was stable and reliable. Additionally, owing to its integrated design, the prototype was easy to carry and set up, so it was very suitable for short-term earthquake tracking and the intensive monitoring of magnetic anomaly areas.

Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode of moth lateral positioning. The basic MFO has no specific, deep strategies in different periods of the algorithm and a fragile evolutionary basis, which may lead to the problem of falling into local optimum and slow convergence trend. Therefore, this paper introduces a double adaptive weight mechanism into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes. The proposed WEMFO adjusts the search strategy adaptively in different periods of the algorithm, making it more flexible between global search (diversification) and local search (intensification). The WEMFO algorithm is compared with some illustrious metaheuristic solvers and advanced metaheuristic methods developed in recent years on thirty benchmark functions. The experimental results expose that the developed WEMFO has apparent compensations in terms of convergence speed and solution accuracy. Moreover, this paper analyzes the diversity and balance of WEMFO and applies the algorithm to several engineering problems. The experimental results show that the WEMFO algorithm has good performance in engineering problems. Additionally, the proposed WEMFO was also applied to train Kernel Extreme Learning Machine (KELM), the resultant optimized WEMFO-KELM model was applied to six clinical disease classification problems. By comparing with MFO-KELM and other five classification models, the experimental results showed that the proposed algorithm had shown better performance in practical problems. An online guide for the algorithm in this research WEMFO and proposed classifier WEMFO-KELM will be publicly available at https://aliasgharheidari.com .

Electrical resistivity structure in the deep Earth can provide important information and constraints on regional geodynamics, especially in regions with complex tectonics and geodynamical processes. In this study, we image the electrical resistivity structure in the depth range of 350–1200 km beneath China by inverting the frequency-dependent ratios of geomagnetic field components at a relatively dense network of geomagnetic observatories. Very long time series of hourly mean values of the three-component geomagnetic field from 45 geomagnetic observatories are collected and processed to derive robust ratios of the geomagnetic components, which are then converted to the apparent resistivities and impedance phases in the period range of 5.3–113.8 days and used in 1-D Occam inversion with smoothing constraint and the ρ+ method to obtain the electrical resistivity profiles and delta function models at individual observatories. 1-D electrical resistivity models suggest that low electrical resistivity structure exists in the mantle transition zone beneath eastern China. High water content in the mantle minerals due to the subduction of seismically fast Pacific slab is proposed as the main reason for the low electrical resistivity beneath eastern China. Our results provide further evidence that the western front of the subducting Pacific slab in the mantle transition zone roughly coincides with the abrupt change in the surface topography in eastern China.

Combining with the actual monitoring work of seismic geophysical observation stations, a seismic monitoring instrument automatic monitoring software was developed. The software is suitable for real-time or regular automatic network monitoring of IP instruments and equipment under different network environments. Automatic monitoring of the clock, status and observation data of geophysical observation instruments, which can automatically identify invalid data and abnormal amplitude changes in the observation data of the day, interference, earthquake and amplitude limitation, etc. Without other hardware and software, the alarm information can be sent to the duty personnel and management personnel by SMS, which can realize automatic backup and manual recovery of the original observation data of the instrument, and One-click restart and clock correction and other control operations, you can view the integrity and validity of the observed data curve and analysis data of the day without cross-platform operation, thereby achieving unified and efficient management of the instrument.

A fast Fourier transform was applied to fit the geomagnetic diurnal variation. Fitting results showed that when the polynomial degree was greater than 160, the residual error was close to 0 nT. White noise is the main component of the residual error when the polynomial degree was greater than 160, so this method was adopted to calculate the background noise of the geomagnetic field. Spectrum analysis further demonstrated the necessity to remove background noise from geomagnetic data.