4.5 Article

Do Low-Cost Seismographs Perform Well Enough for Your Network? An Overview of Laboratory Tests and Field Observations of the OSOP Raspberry Shake 4D

Journal

SEISMOLOGICAL RESEARCH LETTERS
Volume 90, Issue 1, Pages 219-228

Publisher

SEISMOLOGICAL SOC AMER
DOI: 10.1785/0220180251

Keywords

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Funding

  1. Mendenhall Postdoctoral Fellowship program of the U.S. Geological Survey (USGS)

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Seismologists have recently begun using low-cost nodal sensors in dense deployments to sample the seismic wavefield at unprecedented spatial resolution. Earthquake early warning systems and other monitoring networks (e.g., wastewater injection) would also benefit from network densification; however, current nodal sensors lack power systems or the real-time data transmission required for these applications. A candidate sensor for these networks may instead be a low-cost, all-in-one package such as the OSOP Raspberry Shake 4D (RS-4D). The RS-4D includes a vertical-component geophone, three-component accelerometer, digitizer, and near-real-time mini-SEED data transmission and costs only a few hundred dollars per unit. Here, we step through instrument testing of three RS-4Ds at the Albuquerque Seismological Laboratory (ASL). We find that the geophones have sensitivities constrained to within 4% of nominal, but that they have relatively high self-noise levels compared with the broadband sensors typically used in seismic networks. To demonstrate the impact this would have on characterizing nearby events, we estimate local magnitudes of earthquakes in Oklahoma using Trillium Compact broadband sensor data from U.S. Geological Survey aftershock deployments as well as 23 Raspberry Shakes operated by hobbyists and private owners within the state. We find that for M-L 2.0-4.0 earthquakes at distances of 20-100 km from seismic stations, the Raspberry Shakes require events of magnitude similar to 0.3 larger than the broadband sensors to reliably estimate M-L at a given distance from the epicenter. We conclude that RS-4Ds are suitable for densifying backbone networks designed for studies of local and regional events.

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