ABSTRACT
Advances in seismic instrumentation have enabled data to be recorded at increasing sample rates. This has in turn created a need to establish higher frequency baselines for assessing data quality, as the widely used new high-noise model (NHNM) and new low-noise model (NLNM) of Peterson (1993) do not extend to frequencies above 10 Hz. To provide a baseline for higher frequencies (10–100 Hz), we examine power spectral density probability density functions (PSDPDFs) for high-sample rate stations available from the Incorporated Research Institutions for Seismology Data Services Modular Utility for STAtistical kNowledge Gathering (IRIS MUSTANG) quality control system. We compute high-frequency high- and low-noise baselines by matching the appropriate composite PSDPDF percentile points to NHNM and NLNM power levels at overlapping frequencies (1–10 Hz) and then extending to higher frequencies (10–100 Hz) with piecewise linear fits to the matching PSDPDF percentile.
We find that the Peterson NLNM remains an accurate representation of the lower bound of global ambient Earth noise because it is lower than 99.9% of Global Seismographic Network power spectral densities. We present high-frequency high- and low-noise baselines intended primarily for use by temporary networks targeting high-frequency signals (e.g., monitoring of aftershocks or induced seismicity) based on statistics of PSDPDFs from all publicly available high-sample rate data.
Most publicly available high-sample rate data are recorded by temporary deployments, and the experiment design and scientific targets of these deployments strongly influence the observed statistical distribution of high-frequency noise. We anticipate that the noise baselines presented here will be useful in automated quality control of high-sample rate seismic data. However, we note that establishing a low-noise model that accurately represents the lowest possible ambient Earth noise at frequencies up to 100 Hz will require additional continuous high-sample rate data from high-quality permanent stations in low-noise environments.
A scalable high-frequency noise model for bipolar transistors with application to optimal transistor sizing for low-noise amplifier design
