Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self-noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors.

With the availability of low-cost, mass-market dual-frequency GNSS (Global Navigation Satellite System) receivers, standalone processing methods such as Precise Point Positioning (PPP) are no longer restricted to geodetic-grade GNSS equipment only. However, with cheaper equipment, data quality is expected to degrade. This same principle also affects low-cost GNSS antennas, which usually suffer from poorer multipath mitigation and higher antenna noise compared to their geodetic-grade counterparts. This work assesses the quality of a particular piece of low-cost GNSS equipment for real-time PPP and high-rate dynamic monitoring applications, such as strong-motion seismology. We assembled the u-blox ZED-F9P chip in a small and light-weight data logger. With observational data from static experiments—which are processed under kinematic conditions—we assess the precision and stability of the displacement estimates. We tested the impact of different multi-band antenna types, including geodetic medium-grade helical-type (JAVAD GrAnt-G3T), as well as a low-cost helical (Ardusimple AS-ANT2B-CAL) and a patch-type (u-blox ANN-MB) antenna. Besides static tests for the assessment of displacement precision, strong-motion dynamic ground movements are simulated with a robot arm. For cross-validation, we collected measurements with a JAVAD SIGMA G3T geodetic-grade receiver. In terms of precision, we cross-compare the results of three different dual-frequency, real-time PPP solutions: (1) an ambiguity-float solution using the Centre National d’Études Spatiales (CNES) open-source software, (2) an ambiguity-float and an AR (ambiguity-resolved) solution using the raPPPid software from TU Vienna, and (3) and a PPP-RTK solution using the u-blox PointPerfect positioning service. We show that, even with low-cost GNSS equipment, it is possible to obtain a precision of one centimeter. We conclude that these devices provide an excellent basis for the densification of existing GNSS monitoring networks, as needed for strong-motion seismology and earthquake-early-warning.

The ground motion of an earthquake or the ambient motion of a large engineered structure not only has translational motion, but it also includes rotation around all three axes. No current sensor can record all six components, while the fusion of individual instruments that could provide such recordings, such as accelerometers or Global Navigation Satellite System (GNSS) receivers, and rotational sensors, is non-trivial. We propose achieving such a fusion via a six-component (6C) Kalman filter (KF) that is suitable for structural monitoring applications, as well as earthquake monitoring. In order to develop and validate this methodology, we have set up an experimental case study, relying on the use of an industrial six-axis robot arm, on which the instruments are mounted. The robot simulates the structural motion resulting atop a wind-excited wind turbine tower. The quality of the 6C KF reconstruction is assessed by comparing the estimated response to the feedback system of the robot, which performed the experiments. The fusion of rotational information yields significant improvement for both the acceleration recordings but also the GNSS positions, as evidenced via the substantial reduction of the RMSE, expressed as the difference between the KF predictions and robot feedback. This work puts forth, for the first time, a KF-based fusion for all six motion components, validated against a high-precision ground truth measurement. The proposed filter formulation is able to exploit the strengths of each instrument and recover more precise motion estimates that can be exploited for multiple purposes.

In this article, we demonstrate that a single station can be used to measure the dynamic properties of a structure. The station includes a collocated accelerometer and rotational sensor, hence, can record both three-component translation and three-component rotation and is referred to as the 6C-station within this study. The key advantage of this approach is to provide a fast and simple path to a comprehensive structural health monitoring characterization that is comparable to the use of a traditional approach using a horizontal array of three-component accelerometers. The deployment of newly developed high-quality rotational sensors allows the direct measurement of structural rotations, facilitating the extraction of structural mode shapes. In this work, we show how an established system identification tool, stochastic subspace identification, can be applied to the 6C-station data and characterize modal properties and structural response. Our results are verified and contrasted against standard horizontal and vertical array configurations. The Prime Tower, a high-rise structure in Zürich, serves as a case study. A structural characterization of this building is presented for the first time. We show that a 6C-station is capable of defining the frequencies of this stiff high-rise building with a fidelity that is on par with a five-sensor horizontal array. The mode shapes of the roof can be precisely determined with a confidence margin that is comparable to conventional sensing array solutions. However, the effectiveness of using only a 6C-station is determined by the noise level of the sensors—in particular, the rotational seismometer needs to be of high quality. The results indicate that, owing to the collocation measurement of translation and rotation, a 6C-station can deliver a comprehensive structural monitoring solution with minimum time, effort, and footprint.

Understanding the full wave field is imperative for seismic data analysis, as the different components induce errors in the sensors. Recent development of rotational seismometers allows for detailed measurements of the wave field gradients. Providing additional information that was previously unattained. However, it is well-known from navigation solutions that rotational data requires proper processing to be physically meaningful. In this study, we focus on investigating and quantifying two errors affecting recording of rotations: 1) misorientation of sensor to local system called misorientation of rotations and 2) changing projection of the Earth’s spin in the recordings - Earth spin leakage. Using 6-component datasets, including 3C translation and 3C rotation, from near-field events at the Kīlauea Caldera in Hawai‘i and the Mw 7.4 Hualien event on 2024-04-02, we find that the Earth spin leakage is negligible, while the misorientation of the rotations increases with ground motion amplitude, potentially becoming significant for large earthquakes in the near-field. While these errors do not significantly affect acceleration corrections in our dataset, they may be relevant for high-amplitudes or in highly sensitive applications. This work offers the first quantification of these errors in seismology and provides guidance for assessing the need for corrections in future studies.