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Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) measures water level using the interference pattern in signal-to-noise ratio (SNR) from direct and reflected signals off the sea surface, retrieved from standard geodetic antennas. Significant wave height is also measured by determining the satellite elevation angles where reflections become incoherent. We developed an approach for standard geodetic antennas to simultaneously measure sea levels and waves using a criterion for identifying coherent reflections. We tested the method at an exposed coastal environment at the E.B. Scripps Memorial Pier in California. The 1-year test captures a broad range of sea states and benefits from several co-located standard oceanographic sensors. By including GPS, Galileo, and GLONASS observations, the retrieval rate increases by a factor of ∼2 over GPS alone. Uncorrected water levels are estimated with a root-mean-square (RMS) error of 18.2 cm with respect to a conventional tide gauge. We further developed a simplified correction to remove the effect of phenomena altering the SNR oscillatory frequency and phase, which reduces RMS errors to 9.4 cm. We estimate the significant wave height with 15 cm RMS error with respect to a traditional wave gauge. The method, however, requires a short calibration. We find the wave height errors increase abruptly beyond a fixed limit when high waves are present, that may be a result of the particular deployment geometry. With this caveat, the technology could be useful to deploy in under-sampled regions affected by compounded coastal hazards, such as in areas affected by tropical cyclones and flooding.

To understand the impact that induced seismicity in Oklahoma is expected to have on the built environment, it is critical to develop realistic building response models for typical regional building stock. Traditionally, accelerometers are used to monitor buildings’ structural health during potentially damaging events like earthquakes. In this work, we describe an improvement on this typical system using a realtime instrument network which incorporates 3 types of instruments monitoring a single structure. Using this realtime network, we can rapidly estimate the damage state of the building following a potentially damaging event and can continually improve building response models. We have instrumented a 12-story building in Stillwater, Oklahoma, which is representative of aging reinforced concrete building infrastructure that can be vulnerable to frequent induced seismicity in the region. We have instrumented this building with 2 gyroscopes and 2 accelerometers, on the ground and top floors, both streaming data in realtime, and a high-rate GNSS receiver with an antenna on the roof. The data from the gyroscopes and accelerometers are distributed via an Antelope seismic acquisition and database system where the datasets can be rapidly utilized following an earthquake. We use the dataset of ~ M3-4.5 earthquakes collected by the network to date to calibrate an existing nonlinear finite element model (FEM) of the building. This calibrated model is then used to train a neural network to model the expected building response and estimate the damage state of the building for a range of seismic event sizes and input ground motions. The realtime data streams then become the inputs to this neural network to model building response and estimate damage states with a short delay following a major shaking event. This neural network has the advantage of being computationally more efficient than running the complex non-linear FEM. This system is intended to demonstrate an important use case for multi-instrument realtime structural health monitoring data which can be generalized to other areas of high seismic hazard for buildings where the evaluation of building response and risk is required., NSF Award Number: 1835372