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Abstract:
Distributed Acoustic Sensing (DAS) is used to record high-spatial resolution strain-rate data. For ground motion observation, the DAS data can be converted from strain rate to acceleration or velocity by array-based measurements with coherent plane waves. DAS provides an opportunity to map high-resolution shaking patterns near faults. We installed collocated geophones and optical fiber in Hualien City (a very seismically active area in Taiwan) from the end of January to the end of February in 2022. Earthquakes with magnitudes (Mw) between 3.2 and 5.4 have been recorded. These records illustrate the typical magnitude-distance dependence of ground-motion but also show saturation for higher magnitudes and/or at shorter distances (e.g for an earthquake of Mw 5.2 earthquake recorded at 100 km). For frequency-based analyses, clipped signals on DAS result in challenges not present in classical instruments (seismometers). The upper limit in dynamic range of seismometers results in easily identifiable trapezoidal signals. The dynamic range of DAS interrogators is limited by gauge length, sampling frequency, and wrapped phase in the interferometric phase demodulation. We observe that clipped DAS signals not only affect time series but also contaminate their spectra on all frequencies, due to the random nature of clipping in DAS—contrasting to the flat plateaus in clipped time series on seismometers. Therefore, the identification of the start and end points of clipped DAS records poses a major challenge, which we aim to resolve with a neural network. This approach enhances the efficiency for quality control of massive DAS datasets.
