A Seismological Study of the Sos Enattos Area—the Sardinia Candidate Site for the Einstein Telescope

Abstract The recent discovery of gravitational waves (GWs) and their potential for cosmic observations prompted the design of the future third-generation GW interferometers, able to extend the observation distance for sources up to the frontier of the Universe. In particular, the European detector Einstein Telescope (ET) has been proposed to reach peak strain sensitivities of about 3×10−25 Hz−1/2 in the 100 Hz frequency region and to extend the detection band down to 1 Hz. In the bandwidth [1,10] Hz, the seismic ambient noise is expected to represent the major perturbation to interferometric measurements, and the site that will host the future detectors must fulfill stringent requirements on seismic disturbances. In this article, we conduct a seismological study at the Italian ET candidate site, the dismissed mine of Sos Enattos in Sardinia. In the range between few mHz to hundreds of mHz, out of the detection bandwidth for ET, the seismic noise is compatible with the new low-noise model (Peterson, 1993); in the [0.1,1] Hz bandwidth, we found that seismic noise is correlated with sea wave height in the northwestern Mediterranean Sea. In the [1,10] Hz frequency band, noise is mainly due to anthropic activities; within the mine tunnels (≃100 m underground), its spectrum is compliant with the requirements of the ET design. Noise amplitude decay with depth is consistent with a dominance of Rayleigh waves, as suggested by synthetic seismograms calculated for a realistic velocity structure obtained from the inversion of phase- and group-velocity dispersion data from array recording of a mine blasting. Further investigations are planned for a quantitative assessment of the principal noise sources and their spatiotemporal variations.

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Frequency limits for seismometers as determined from signal-to-noise ratios. Part 1. The electromagnetic seismometer

Bulletin of the Seismological Society of America ◽

10.1785/bssa0820021071 ◽

1992 ◽

Vol 82 (2) ◽

pp. 1071-1098 ◽

Cited By ~ 3

Author(s):

Peter W. Rodgers

Keyword(s):

Operational Amplifier ◽

Seismic Noise ◽

Low Noise ◽

Noise Model ◽

Noise Voltage ◽

Signal To Noise ◽

Noise Current ◽

System Noise ◽

Large Gain ◽

Total Noise

Abstract The range of frequencies that a seismometer can record is nominally set by the corner frequencies of its amplitude frequency response. In recording pre-event noise in very quiet seismic sites, the internally generated self-noise of the seismometer can put further limits on the range of frequencies that can be recorded. Some examples of such low seismic noise sites are Lajitas, Texas; Deep Springs, California; and Karkaralinsk, U.S.S.R. In such sites, the seismometer self-noise can be large enough to degrade the signal-to-noise ratio (SNR) of the recorded pre-event data. The widely used low seismic noise model (LNM) (due to Peterson, 1982; Peterson and Hutt, 1982; Peterson and Tilgner, 1985; Peterson and Hutt, 1989) is used as representative of the input ground motion acceleration power density spectrum (pds) at such very low noise sites. This study determines the range of frequencies for which the SNR of an electromagnetic seismometer exceeds 3 db (a factor of 2 in power and 1.414 in amplitude). In order to do this, an analytic expression is developed for the SNR of a generalized electromagnetic seismometer. The signal pds using Peterson's LNM as an input is developed for an electromagnetic seismometer. Suspension noise is modeled following Usher (1973). In order to determine the electronically caused component of the self-noise, noise properties are compared among three commonly used amplifiers. The advantages and disadvantages of the inverting and noninverting configurations in terms of their SNR are discussed. In most cases, the noninverting configuration is to be preferred as it avoids the use of the large gain setting resistances required in the inverting configuration to avoid loading the seismometer output. A noise model is developed for a typical low noise operational amplifier (Precision Monolithics OP-27). This noise model is used to numerically compute the SNRs for the three electromagnetic seismometers used as examples. The degradation in SNR caused by large gain setting resistances is shown. Numerical examples are given using the Mark Products L-4C and L-22D and the Teledyne Geotech GS-13 electromagnetic seismometers. For each of the example seismometers, the calculated range of frequencies for which their SNR exceeds 3 db is as follows: the GS-13, 0.078 to 56.1 Hz; the L-4C, 0.113 to 7.2 Hz; and the L-22D, 0.175 to 0.6 Hz. For the GS-13, the calculated lower and upper frequencies at which the SNR is 3 db are 0.078 and 56.1 Hz. This compares with the values 0.073 and 59 Hz measured in the noise tests on the vertical GS-13. Expressions for the total noise voltage referred to the input of an operational amplifier are developed in Appendix A. It is shown that in the inverting configuration, although no noise current flows in the input resistor, the noise current appears in the expression for the total noise voltage as if it did. In Appendix B, it is shown that any noise current flowing through an electromagnetic seismometer having a generator greater than several hundred V/m/sec generates a back emf that adds significantly to the noise of the system. This implies that system noise tests that substitute a resistor at the noninverting input of the preamplifier or clamp the seismometer mass will tend to underestimate the system noise.

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Frequency limits for seismometers as determined from signal-to-noise ratios. Part 2. The feedback seismometer

Bulletin of the Seismological Society of America ◽

10.1785/bssa0820021099 ◽

1992 ◽

Vol 82 (2) ◽

pp. 1099-1123

Author(s):

Peter W. Rodgers

Keyword(s):

Seismic Noise ◽

Transfer Functions ◽

Low Noise ◽

Noise Model ◽

Power Density Spectrum ◽

Signal To Noise ◽

Density Spectrum ◽

Velocity Feedback ◽

Noise Models ◽

Feedback Seismometer

Abstract The range of frequencies that a seismometer can record is nominally set by the corner frequencies of its amplitude frequency response. In recording pre-event noise in very quiet seismic sites, the internally generated self-noise of the seismometer can put further limits on the range of frequencies that can be recorded. Some examples of such low seismic noise sites are Lajitas, Texas; Deep Springs, California; and Karkaralinsk, U.S.S.R. In such sites, the seismometer self-noise can be large enough to degrade the signal-to-noise ratio (SNR) of the recorded pre-event data. The widely used low seismic noise model (LNM) (due to Peterson, 1982; Peterson and Hutt, 1982; Peterson and Tilgner, 1985; Peterson and Hutt, 1989) is used as representative of the input ground motion acceleration power density spectrum (pds) at such very low noise sites. This study determines the range of frequencies for which the SNR of a feedback seismometer exceeds 3 db (a factor of 2 in power and 1.414 in amplitude). Analytic expressions for the SNR are developed for three types of feedback seismometers. These are the displacement feedback, velocity feedback, and coil-to-coil velocity feedback seismometers. It was found that the analytic SNRs of the displacement and velocity feedback seismometers are identical and that the SNRs for the coil-coil feedback seismometer and the electromagnetic seismometer are also the same. The signal pds using Peterson's LNM as an input is developed for each of the three types of feedback seismometers. Suspension noise is modeled following Aki and Richards (1980). In order to model the electronically caused component of the self-noise, the electronic noise properties of two commonly used operational amplifiers (Precision Monolithics OP-27 and the Burr-Brown OPA2111 FET) are described. Using these, noise models are developed for a synchronous demodulator and a chopper-stabilized amplifier. These noise models are used to numerically compute the SNRs for the two feedback seismometers used as examples, which are the Guralp Systems CMG-3ESP and Sprengnether Instruments SBX-1000 feedback seismometers. For each of the example seismometers, the calculated range of frequencies for which their SNR exceeds 3 db is as follows: the CMG-3ESP, 0.025 to 13.3 Hz; the SBX-1000, 0.098 to 11.3 Hz. The calculated and measured SNRs for the CMG-3ESP are compared. The calculated upper frequency for a SNR of 3 db was 13.3 Hz compared with 18.4 Hz measured in the noise tests. The calculated lower frequency for a SNR of 3 db was 0.025 Hz, whereas the measured value was 0.047 Hz. The difference is most likely due to the fact the CMG-3ESP is cut off at 0.1 Hz. Formulas are developed in Appendix A for calculating the SNR and self-noise of identical, colocated seismometers from their recorded outputs. The analytic transfer functions, midband gain, upper and lower corner frequencies, and bandwidths for the three types of feedback seismometers are given in Appendix B for comparison with the frequency limits set by the SNR.

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Group velocity dispersion characteristics and one-dimensional regional shear velocity structure of the eastern Indian craton

Journal of Asian Earth Sciences ◽

10.1016/j.jseaes.2016.11.027 ◽

2017 ◽

Vol 134 ◽

pp. 231-243 ◽

Cited By ~ 6

Author(s):

Prantik Mandal

Keyword(s):

Group Velocity ◽

Velocity Dispersion ◽

Velocity Structure ◽

Group Velocity Dispersion ◽

Shear Velocity ◽

Dispersion Characteristics ◽

One Dimensional ◽

Indian Craton

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Imaging New Zealand's Crustal Structure Using Ambient Seismic Noise Recordings from Permanent and Temporary Instruments

10.26686/wgtn.16985062.v1 ◽

2021 ◽

Author(s):

◽

Yannik Behr

Keyword(s):

Seismic Noise ◽

Cross Correlation ◽

Velocity Structure ◽

Shear Velocity ◽

Ambient Seismic Noise ◽

Uppermost Mantle ◽

Noise Sources ◽

Noise Field ◽

Rayleigh And Love Waves ◽

Noise Cross Correlation

<p>We use ambient seismic noise to image the crust and uppermost mantle, and to determine the spatiotemporal characteristics of the noise field itself, and examine the way in which those characteristics may influence imaging results. Surface wave information extracted from ambient seismic noise using cross-correlation methods significantly enhances our knowledge of the crustal and uppermost mantle shear-velocity structure of New Zealand. We assemble a large dataset of three-component broadband continuous seismic data from temporary and permanent seismic stations, increasing the achievable resolution of surface wave velocity maps in comparison to a previous study. Three-component data enables us to examine both Rayleigh and Love waves using noise cross-correlation functions. Employing a Monte Carlo inversion method, we invert Rayleigh and Love wave phase and group velocity dispersion curves separately for spatially averaged isotropic shear velocity models beneath the Northland Peninsula. The results yield first-order radial anisotropy estimates of 2% in the upper crust and up to 15% in the lower crust, and estimates of Moho depth and uppermost mantle velocity compatible with previous studies. We also construct a high-resolution, pseudo-3D image of the shear-velocity distribution in the crust and uppermost mantle beneath the central North Island using Rayleigh and Love waves. We document, for the first time, the lateral extent of low shear-velocity zones in the upper and mid-crust beneath the highly active Taupo Volcanic Zone, which have been reported previously based on spatially confined 1D shear-velocity profiles. Attributing these low shear-velocities to the presence of partial melt, we use an empirical relation to estimate an average percentage of partial melt of < 4:2% in the upper and middle crust. Analysis of the ambient seismic noise field in the North Island using plane wave beamforming and slant stacking indicates that higher mode Rayleigh waves can be detected, in addition to the fundamental mode. The azimuthal distributions of seismic noise sources inferred from beamforming are compatible with high near-coastal ocean wave heights in the period band of the secondary microseism (~7 s). Averaged over 130 days, the distribution of seismic noise sources is azimuthally homogeneous, indicating that the seismic noise field is well-suited to noise cross-correlation studies. This is underpinned by the good agreement of our results with those from previous studies. The effective homogeneity of the seismic noise field and the large dataset of noise cross-correlation functions we here compiled, provide the cornerstone for future studies of ambient seismic noise and crustal shear velocity structure in New Zealand.</p>

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Seismic Noise Model for Romania Revisited

10.3997/2214-4609.202149bgs77 ◽

2021 ◽

Author(s):

A. Tolea ◽

B. Grecu ◽

C. Neagoe

Keyword(s):

Seismic Noise ◽

Noise Model

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Radiation modes of propeller tonal noise

Journal of Vibration and Acoustics ◽

10.1115/1.4051864 ◽

2021 ◽

pp. 1-25

Author(s):

Hanbo Jiang ◽

Siyang Zhong ◽

Han Wu ◽

Xin Zhang ◽

Xun Huang ◽

...

Keyword(s):

Spherical Harmonics ◽

Sound Field ◽

Low Noise ◽

Noise Model ◽

Sound Waves ◽

Tonal Noise ◽

Noise Sources ◽

Separate Source ◽

Radial Forces ◽

Flow Variables

Abstract This paper focuses on the radiation modes and efficiency of propeller tonal noise. The thickness noise and loading noise model of propellers has been formulated in spherical coordinates, thereby simplifying numerical evaluation of the integral noise source. More importantly, the radiation field can be decomposed and projected to spherical harmonics, which can separate source-observer positions and enable an analysis of sound field structures. Thanks to the parity of spherical harmonics, the proposed model can mathematically explain the fact that thrusts only produce antisymmetric sound waves with respect to the rotating plane. In addition, the symmetric components of the noise field can be attributed to the thickness, as well as drags and radial forces acting on the propeller surface. The radiation efficiency of each mode decays rapidly as noise sources approach the rotating centre, suggesting the radial distribution of aerodynamic loadings should be carefully designed for low-noise propellers. The noise prediction model has been successfully applied to a drone propeller and achieved a reliable agreement with experimental measurements. The flow variables employed as an input of the noise computation were obtained with computational fluid dynamics (CFD), and the experimental data were measured in an anechoic chamber.

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