The influence of observation environment on background noise level of gPhone gravimeter

Based on the observations of 36 gPhone gravimeters in 2015, the background noise levels in the seismic frequency band（200-600 s） and sub-seismic band（1-6 h） are calculated. The differences in the PSD（power spectrum density） of each band of gPhone gravimetric gauges in different surrounding environments were analyzed and compared with Peterson＇s NLNM（new low-noise model） which is derived from the envelope at the power spectrum density of 75 seismograph stations around the world. The results showed that： the influence of station type on the noise magnitude of gPhone gravimeter is very small; The seismic band noise magnitude（hereinafter referred to as SNM） and the sub-seismic band noise magnitude（hereinafter referred to as SSNM） in the coastal gPhone gravimeter are higher than those of inland stations. Although the local hydrological change has a great influence on the gravity observation, the rainfall is not directly relative to the noise magnitude of the instrument. Except 3 coastal stations, the eight stations which had the highest amplitudes in the SNM were located near the seismic belt. This indicates that the SNM of the gPhone Gravimeter may reflect some seismic information.Compared with the NLNM model, the PSD of the gPhone gravimeter is lower than the NLNM model in the long period band（〈3×10^（-5）Hz）, indicating that the gPhone gravimeter is more suitable for detecting long-period signals（〉10 h） than the seismometer.

Monitoring Efficiency Evaluation of the Well-water-level Observation Network in China

In order to understand the monitoring efficiency status of the well-water-level observation network in China after the completion of the 10 th "Five-year Plan " digital network project,and to provide a basis for the future network optimization and equipment updating, the monitoring efficiency of the well-water-level observation network was evaluated. On the whole,61. 8% observing stations have good monitoring effectiveness,the observation environment of 73. 5% of observing stations meets the monitoring requirements of well-water-level. The operation status of the network is as a whole getting better,operation rates of 75% observing instruments are above 95%. Most well water levels can monitor crustal stress changes and seismic activities. However,some observation stations,due to inherent deficiency in wells,environmental disturbance,instrument aging,and low-level operation and maintenance,need to improve the monitoring efficiency level by taking measures such as observation environment improvement,equipment updating,and management training. About 6. 5% of the stations need to stop observation due to the unqualified observational environment.

Effects of Wind Fences on the Wind Environment around Jang Bogo Antarctic Research Station

This study investigated the flow characteristics altered by Jang Bogo Antarctic Research Station using computational fluid dynamics(CFD) modeling. The topography and buildings around Jang Bogo Station were constructed with computeraided-design data in the CFD model domain. We simulated 16 cases with different inflow directions, and compared the flow characteristics with and without Jang Bogo Station for each inflow direction. The wind data recorded by the site’s automatic weather station(AWS) were used for comparison. Wind rose analysis showed that the wind speed and direction after the construction of Jang Bogo Station were quite different from those before construction. We also investigated how virtual wind fences would modify the flow patterns, changing the distance of the fence from the station as well as the porosity of the fence. For westerly inflows, when the AWS was downwind of Jang Bogo Station, the decrease in wind speed was maximized(-81% for west-northwesterly). The wind speed reduction was also greater as the distance of the fence was closer to Jang Bogo Station. With the same distance, the fence with medium porosity(25%–33%) maximized the wind speed reduction.These results suggest that the location and material of the wind fence should be selected carefully, or AWS data should be interpreted cautiously, for particular prevailing wind directions.