Characterizing the Background Noise Level of Rotational Ground Motions on Earth

Abstract

Abstract The development of high-sensitive ground-motion instrumentation for Earth and planetary exploration is governed by so-called low-noise models, which characterize the minimum level of physical ground motions, observed across a very broad frequency range (0.1 mHz–100 Hz). For decades, broadband instruments for seismic translational ground-motion sensing allowed for observations down to the Earth’s low-noise model. Knowing the lowermost noise level distribution across frequencies enabled not only to infer characteristics of Earth such as the ocean microseismic noise (microseisms) and seismic hum, but also to develop highly successful ambient seismic noise analysis techniques in seismology. Such a low-noise model currently does not exist for rotational ground motions. In the absence of a substantial observational database, we propose a preliminary rotational low-noise model (RLNM) for transverse rotations based on two main wavefield assumptions: the frequency range under investigation is dominated by surface-wave energy, and the employed phase velocity models for surface waves are representative. These assumptions hold, in particular, for a period range of about 2–50 s and lose validity towards long periods when constituents produced by atmospheric pressure dominate. Because noise levels of vertical and horizontal accelerations differ, we expect also different noise levels for transverse and vertical rotations. However, at this moment, we propose a common model for both types of rotations based on the transverse RLNM. We test our RLNM against available direct observations provided by two large-scale ring lasers (G-ring and ROMY) and array-derived rotations (Piñon Flats Observatory array, Gräfenberg array, and ROMY array). We propose this RLNM to be useful as guidance for the development of high-performance rotation instrumentation for seismic applications in a range of 2–50 s. Achieving broadband sensitivity below such a RLNM remains a challenging task, but one that has to be achieved.