Cite this dataset
Janiszewski, Helen et al. (2022). BBOBS_Noise_Properties_Review [Dataset]. Dryad. https://doi.org/10.25349/D90042
We present a new compilation and analysis of broadband ocean bottom seismometer noise properties from 15 years of seismic deployments. We compile a comprehensive dataset of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase, and admittance) for 551 unique stations spanning 18 US-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise dataset serves as a building block for more targeted future investigations by the marine seismology community.
Our study includes BBOBSs deployed as part of experiments facilitated by OBSIP or OBSIC from 2005 to the present. Each BBOBS in our dataset satisfies the following criteria. (1) It contains a 3-component, wideband or broadband seismometer (i.e., with flat instrument response between ~0.01 and ~10 Hz). We restrict our analysis to BBOBS designs with seismometers that are still actively used in the OBSIC fleet, which includes Guralp CMG-3T (CMG-3T), Nanometrics Trillium Compact (T-Compact), and Nanometrics Trillium 240 (T-240) instruments. (2) It includes a wide-band pressure sensor: either a differential pressure gauge (DPG) or an absolute pressure gauge (APG). (3) All four components of the BBOBS recorded data at a sample rate of at least 5 samples-per-second (sps). Our study does not constitute a quantification of overall data quality; we do not account for station dropouts, broken channels, or instrument return rate.
We select a subset of data at each station from which to calculate power spectra, cross-component coherence, admittance, and phase spectra, which make up the transfer functions used for noise corrections. We examine 25 randomly distributed days of data that are not significantly contaminated by earthquake signals, instrument glitches, or other transient signals. For all seismometer and DPG channels, we remove the instrument response using a high pass filter with a corner frequency of 1000 s.
We window each day of data into sixteen, 7200-second segments, overlapping by 30%, and apply a flat-Hanning taper to the windows. We calculate the auto- and cross-power spectral density functions from the finite Fourier transforms of the time series (Bell et al., 2015; Bendat & Piersol, 2010) for each of the 16 windows. Any windows that contain transient signals identified via quality control procedures (see Janiszewski et al., 2019 for details) are discarded; if more than 6 windows are discarded, the entire day is rejected and not counted towards the 25-day sample. The windows are subsequently averaged to calculate spectral density functions for each day of data.
We calculate deployment-average spectral functions for each station by averaging over all windows, avoiding inclusion of days that are dominated by anomalous signals unrepresentative of normal station noise. We take full-octave averages of the spectra in ⅛ octave interval, then visually inspect all averaged spectra and discard any that contain data dropouts, flatlined or obviously non-functioning instruments, or instruments where the secondary microseism peak was not visible (e.g., anomalously high noise floor). This results in an average spectra for vertical (Z), horizontal (H1, H2, or collectively H), and pressure (P) components at each BBOBS, as well as average cross-component coherence, admittance, and phase functions.
Lastly, we use the computed transfer functions to estimate average tilt- and compliance-corrected Z spectra for each BBOBS. See manuscript.
Please see README file, and associated manuscript.
Supplementary data corresponding to publication "Broadband Ocean Bottom Seismometer Noise Properties", Helen A. Janiszewski, Z. Eilon, J.B. Russell, B. Brunsvik, J.B. Gaherty, S.G. Mosher, W.B. Hawley, and S. Coats, submitted for review at Geophysical Journal International.
Explanation of Files:
TableS2.xlsx - Table containing metadata parameters for all broadband ocean bottom seismometers (BBOBS) analyzed in this study. Sources and detailed descriptions of the parameters can be found in the main text. Also includes an indicator if a particular seismometer channel was determined "good" and included in the analysis. A value of 1 indicates the data was "good" and included; 0 indicates the data was not "good" and was excluded.
Spectra_Supp_Files - Octave averaged noise spectra for each individual BBOBS. Filename is NN_STAT_spectra_CH.txt, where NN is the network code, STAT is the station name, and CH is the channel using the abbreviations used in the manuscript. In each file are two columns, where the first is the frequency in Hz, and the second is the power spectra in dB in units of acceleration (m2/s4/Hz).
Corr_Spectra_Supp_Files - Octave averaged corrected vertical noise spectra for each individual BBOBS. Filename is NN_STAT_spectra_Z_xxcorrected.txt. NN is the network code. STAT is the station name. xxcorrected is for the type of correction, where tilt means the data have been corrected for tilt noise, comp means the data have been corrected for compliance noise, and all means the data have been corrected for both tilt and compliance noise. In each file are two columns, where the first is the frequency in Hz, and the second is the power spectra in dB in units of acceleration (m2/s4/Hz).
Adm_Supp_Files - Admittance for pairs of channels for each individual BBOBS. Filename is NN_STAT_adm_CHCH.txt. NN is the network code. STAT is the station name. CHCH is the pair of channels for which the admittance is calculated. In each file are two columns, where the first is the frequency in Hz, and the second is the admittance.
Coh_Supp_Files - Coherence for pairs of channels for each individual BBOBS. Filename is NN_STAT_coh_CHCH.txt. NN is the network code. STAT is the station name. CHCH is the pair of channels for which the coherence is calculated. In each file are two columns, where the first is the frequency in Hz, and the second is the coherence.
Phs_Supp_Files - Phase for pairs of channels for each individual BBOBS. Filename is NN_STAT_phs_CHCH.txt. NN is the network code. STAT is the station name. CHCH is the pair of channels for which the phase is calculated. In each file are two columns, where the first is the frequency in Hz, and the second is the phase.
National Science Foundation, Award: OCE-1658491
National Science Foundation, Award: OCE-1658214
National Science Foundation, Award: OCE-1753722