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The ambient infrasound noise environment is characterized for 21 globally distributed infrasound arrays in the frequency band of 0.03 to 7 Hz. Power Spectral Density (PSD) is measured for one site of each array for 21 intervals at each of four times of day from January 2003 through January 2004. The ambient noise at infrasound stations is highly variable by season, time of day and station. Noise spectra for an individual station may vary by four orders of magnitude at any given frequency. Preliminary infrasound noise models are defined, which can be used as baselines for evaluating ambient noise at current and new infrasound stations. Median noise levels in the microbarom band centered on 0.2 Hz vary smoothly in an annual pattern, with most stations observing maximum noise during local winter. Noise amplitudes do not have a normal or log‐normal distribution, but rather are skewed to larger amplitudes.

We present a new web service, Syngine (http://ds.iris.edu/ds/products/syngine/), running at the IRIS DMC, that offers on-demand and custom tailored seismograms over a broad period range from 1 to 100 seconds, served over HTTP. The free service produces full seismic waveforms including effects like attenuation and anisotropy that are calculated in commonly used spherically symmetric Earth models (PREM, ak135-f, iasp91). Users can freely adjust sources and receivers, retrieve seismograms from finite sources, convolve with arbitrary source time functions, and download Green's functions suitable for moment tensor inversions. Syngine extracts and processes seismograms in as fast as fractions of a second making it suitable for applications demanding short iteration times and a large number of waveforms. For the fist time, researchers without large computational resources or specialized knowledge can easily access high-quality, custom, broadband seismograms. In this manuscript we present the rational and basic principles of our method, including its limitations. Additionally we demonstrate the features of Syngine and the included Earth models, showcase several applications, and discuss future possibilities.

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Power spectral density (PSD) estimates are widely used in seismological studies to characterize background noise conditions, assess instrument performance, and study quasi-stationary signals that are difficult to observe in the time domain. However, these studies often utilize different processing techniques, each of which can inherently bias the resulting PSD estimates. The level of smoothing, the size of the data window, and the method used for actually estimating the spectral content can all have strong influences on PSD estimates and background noise statistics. We show that although smoothing reduces the variance of the PSD estimate, the corresponding decrease in frequency resolution can eliminate or distort features of interest. For instance, popular software packages such as Incorporated Research Institutions for Seismology Modular Utility for STAatistical kNowledge Gathering (MUSTANG) and earlier versions of Portable Array Seismic Studies of the Continental Lithosphere Quick Look eXtended (PQLX), which were designed for data quality control and are effective in that regard, are less suitable for scientific studies that require accurate resolution of spectral peaks, even for peaks as broad as the primary (∼14  s period) and secondary (∼7  s period) microseisms. We also demonstrate how the 1 and 3 hr data windows used in MUSTANG and PQLX can be strongly influenced by energy generated from moderate-size (M>∼4.8) teleseismic earthquakes. The ubiquity of these events is likely skewing median ambient-noise estimates by as much as 5 dB upward, for periods of 10–50 s at high-quality broadband stations. Finally, we illustrate that many of the discrepancies between global low-noise models are attributable to processing methodologies rather than fundamental differences in the underlying seismic data.