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.

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.

In order to minimize interruptions to recording, geomagnetic observatories usually use a back-up instrument operating simultaneously with the primary instrument in order to obtain comparative observations. Based on the correction parameter calculation method established in the previous work, we focused on the effects of temperature and instrument drift on the comparative geomagnetic vector observations. The linear influence of temperature on the comparative data was shown to be variable. The relative temperature coefficient changed around the temperature inflection point and showed a V-type distribution in a scatter plot. This conclusion was verified in laboratory experiments. The long-term time drift between the comparative instruments exhibits a linear pattern, and the fitness of the correction model can be evaluated by the degree to which the residual distribution of the fitted straight line conforms to the normal distribution. However, the absolute value of the long-term time drift between variometers with the same type of probe is very small. Therefore, long-term time drift correction should be carried out with care. The associated analysis and conclusions have the potential to benefit data agreement correction of long-term comparative geomagnetic vector observations and comparative testing of the performance of vector instruments.

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, enhancing the robustness of comparative observatory observations and reducing errors in judgments of the size and arrival time of large magnetic disturbances or rapid magnetic variations.