Abstract
The accuracy and repeatability of microgravity measurements for surveying purposes are affected by two main sources of noise; instrument noise from the sensor and electronics, and environmental sources of noise from anthropogenic activity, wind, microseismic activity and other sources of vibrational noise. There is little information in the literature on the quantitative values of these different noise sources and their significance for microgravity measurements. Experiments were conducted to quantify these sources of noise with multiple instruments, and to develop methodologies to reduce these unwanted signals thereby improving the accuracy or speed of microgravity measurements. External environmental sources of noise were found to be concentrated at higher frequencies (> 0.1 Hz), well within the instrument's bandwidth. In contrast, the internal instrumental noise was dominant at frequencies much lower than the reciprocal of the maximum integration time, and was identified as the limiting factor for current instruments. The optimum time for integration was found to be between 120 and 150 s for the instruments tested.In order to reduce the effects of external environmental noise on microgravity measurements, a filtering and despiking technique was created using data from noisy environments next to a main road and outside on a windy day. The technique showed a significant improvement in the repeatability of measurements, with between 40% and 50% lower standard deviations being obtained over numerous different data sets.The filtering technique was then tested in field conditions by using an anomaly of known size, and a comparison made between different filtering methods. Results showed improvements with the proposed method performing better than a conventional, or boxcar, averaging process. The proposed despiking process was generally found to be ineffective, with greater gains obtained when complete measurement records were discarded. Field survey results were worse than static measurement results, possibly due to the actions of moving the Scintrex during the survey which caused instability and elastic relaxation in the sensor, or the liquid tilt sensors, which generated additional low frequency instrument noise. However, the technique will result in significant improvements to accuracy and a reduction of measurement time, both for static measurements, for example at reference sites and observatories, and for field measurements using the next generation of instruments based on new technology, such as atom interferometry, resulting in time and cost savings.
Highlights
Microgravity measurements are a useful tool within the geophysicist's toolbox for locating subsurface voids, as the instrument responds to the physical property that defines a void as opposed to a proxy
This paper has shown that gravity surveys using commercial Scintrex CG5 instruments are affected by both instrumental and environmental noise sources during measurements which greatly affect the accuracy of the collected data and have received little consideration
A long-term set of measurements with multiple instruments was used to determine the scale of instrumental noise, which varies between instruments, and environmental noise from microseismic sources
Summary
Microgravity measurements are a useful tool within the geophysicist's toolbox for locating subsurface voids, as the instrument responds to the physical property that defines a void as opposed to a proxy (i.e. density contrast). As a passive method, it measures a gravity field and has no theoretical limitations on penetration depth These advantages give it a capability unparalleled by other geophysical techniques, especially for deeper features. Instruments such as the Scintrex CG5 (Scintrex, 2006) perform many corrections to the raw gravity signal. To demonstrate the efficacy of this data-driven filtering approach, the data collected from the three Scintrex CG5 instruments located in a basement of the University of Birmingham was analysed. These instruments were set to collect data for 37 h, equating to 507 records of 1536 samples (256 s observation periods).
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