scholarly journals Low‐Frequency Seismicity at Villarrica Volcano: Source Location and Seismic Velocities

2019 ◽  
Vol 124 (11) ◽  
pp. 11505-11530
Author(s):  
Johanna Lehr ◽  
Felix Eckel ◽  
Martin Thorwart ◽  
Wolfgang Rabbel
Keyword(s):  
Low Frequency ◽  
Source Location ◽  
Seismic Velocities ◽  
Villarrica Volcano

Noise source location and scaling of subsonic upper-surface blowing

2020 ◽  
Vol 19 (3-5) ◽  
pp. 191-206
Author(s):  
Trae L Jennette ◽  
Krish K Ahuja
Keyword(s):  
Strouhal Number ◽  
Noise Source ◽  
Low Frequency ◽  
Directivity Pattern ◽  
Source Location ◽  
Dipole Source ◽  
Frequency Noise ◽  
Noise Sources ◽  
Trailing Edge ◽  
Trailing Edge Noise

This paper deals with the topic of upper surface blowing noise. Using a model-scale rectangular nozzle of an aspect ratio of 10 and a sharp trailing edge, detailed noise contours were acquired with and without a subsonic jet blowing over a flat surface to determine the noise source location as a function of frequency. Additionally, velocity scaling of the upper surface blowing noise was carried out. It was found that the upper surface blowing increases the noise significantly. This is a result of both the trailing edge noise and turbulence downstream of the trailing edge, referred to as wake noise in the paper. It was found that low-frequency noise with a peak Strouhal number of 0.02 originates from the trailing edge whereas the high-frequency noise with the peak in the vicinity of Strouhal number of 0.2 originates near the nozzle exit. Low frequency (low Strouhal number) follows a velocity scaling corresponding to a dipole source where as the high Strouhal numbers as quadrupole sources. The culmination of these two effects is a cardioid-shaped directivity pattern. On the shielded side, the most dominant noise sources were at the trailing edge and in the near wake. The trailing edge mounting geometry also created anomalous acoustic diffraction indicating that not only is the geometry of the edge itself important, but also all geometry near the trailing edge.

Effect of non-parallel mean flow on the Green’s function for predicting the low-frequency sound from turbulent air jets

2012 ◽  
Vol 695 ◽  
pp. 199-234 ◽  
Author(s):  
M. E. Goldstein ◽  
Adrian Sescu ◽  
M. Z. Afsar
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Low Frequency ◽  
Source Location ◽  
Parallel Flow ◽  
Numerical Calculations ◽  
Mean Flow ◽  
Frequency Sound ◽  
The Mean ◽  
Radiated Sound

AbstractIt is now well-known that there is an exact formula relating the far-field jet noise spectrum to the convolution product of a propagator (that accounts for the mean flow interactions) and a generalized Reynolds stress autocovariance tensor (that accounts for the turbulence fluctuations). The propagator depends only on the mean flow and an adjoint vector Green’s function for a particular form of the linearized Euler equations. Recent numerical calculations of Karabasov, Bogey & Hynes (AIAA Paper 2011-2929) for a Mach 0.9 jet show use of the true non-parallel flow Green’s function rather than the more conventional locally parallel flow result leads to a significant increase in the predicted low-frequency sound radiation at observation angles close to the downstream jet axis. But the non-parallel flow appears to have little effect on the sound radiated at $9{0}^{\ensuremath{\circ} } $ to the downstream axis. The present paper is concerned with the effects of non-parallel mean flows on the adjoint vector Green’s function. We obtain a low-frequency asymptotic solution for that function by solving a very simple second-order hyperbolic equation for a composite dependent variable (which is directly proportional to a pressure-like component of this Green’s function and roughly corresponds to the strength of a monopole source within the jet). Our numerical calculations show that this quantity remains fairly close to the corresponding parallel flow result at low Mach numbers and that, as expected, it converges to that result when an appropriately scaled frequency parameter is increased. But the convergence occurs at progressively higher frequencies as the Mach number increases and the supersonic solution never actually converges to the parallel flow result in the vicinity of a critical- layer singularity that occurs in that solution. The dominant contribution to the propagator comes from the radial derivative of a certain component of the adjoint vector Green’s function. The non-parallel flow has a large effect on this quantity, causing it (and, therefore, the radiated sound) to increase at subsonic speeds and decrease at supersonic speeds. The effects of acoustic source location can be visualized by plotting the magnitude of this quantity, as function of position. These ‘altitude plots’ (which represent the intensity of the radiated sound as a function of source location) show that while the parallel flow solutions exhibit a single peak at subsonic speeds (when the source point is centred on the initial shear layer), the non-parallel solutions exhibit a double peak structure, with the second peak occurring about two potential core lengths downstream of the nozzle. These results are qualitatively consistent with the numerical calculations reported in Karabasov et al. (2011).

scholarly journals An Amplitude-Based Modeling Method and its Application on the Impedance Inversion in Heterogeneous Paleokarst Carbonate Reservoirs

2016 ◽  
Vol 5 (2) ◽  
pp. 199
Author(s):  
Yuanyin Zhang ◽  
Zandong Sun ◽  
Zhijun Jin ◽  
Ning Dong ◽  
Yequan Chen ◽  
...  
Keyword(s):  
Tarim Basin ◽  
Traditional Method ◽  
Low Frequency ◽  
Modeling Method ◽  
Carbonate Reservoirs ◽  
P Wave ◽  
Seismic Velocities ◽  
Frequency Model ◽  
Heterogeneous Reservoirs ◽  
Impedance Inversion

For the modeling of complex reservoirs with strong heterogeneity, for instance the deeply buried paleokarst reservoirs in the Tarim Basin, the traditional method by lateral interpolation and extrapolation of measured logs between well locations with the guiding of interpreted seismic horizons is driven by distance and often leads to non-geologic solutions, while the past improvements via adding seismic velocities or attributes information are still not accurate due to the resolution limitation or AVO (amplitude versus offset) effects contamination. In this paper, we present an amplitude-based modeling method by utilizing the heterogeneous information from seismic data to guide the geological model construction, based on the inverted pure P-wave data which have removed the AVO effects. The proposed method is applied in the impedance inversion of the paleokarst carbonate reservoirs in the Tarim Basin, where the reservoirs are characterized by substantial heterogeneity. Both the constructed Low frequency model (LFM) and the inverted impedance results of proposed method are more correlative with drilling data than that of traditional method. This method is more beneficial for strong heterogeneous reservoirs description especially in well insufficient or absent areas, suggested by the comparisons with traditional methods in the ZG8 area.

Estimates of velocity dispersion between seismic and ultrasonic frequencies

Geophysics ◽  
1986 ◽  
Vol 51 (1) ◽  
pp. 183-189 ◽  
Author(s):  
Kenneth W. Winkler
Keyword(s):  
Velocity Dispersion ◽  
Low Frequency ◽  
Degree Of Saturation ◽  
Fluid Viscosity ◽  
Seismic Velocities ◽  
S Wave ◽  
Local Flow ◽  
Saturated Rock ◽  
Analysis Technique ◽  
Zero Frequency

It is generally accepted that acoustic velocities in fluid‐saturated rocks vary with frequency. Evidence comes from experimental measurements and from theoretical causality arguments. We have developed a simple analysis technique that gives estimates of total velocity dispersion between zero frequency and any measurement frequency. The technique requires compressional (P) and shear (S) wave velocity measurements on dry and fully saturated rock. Assuming that the dry velocities are independent of frequency, the Biot‐Gassmann equations are used to calculate the zero‐frequency velocities in the fully saturated rock. Any difference between the measured velocities and the calculated zero‐frequency velocities is interpreted as evidence of dispersion. Application of this analysis technique to a variety c ultrasonic data sets gives consistent results. In many rocks, dispersion between zero frequency and ultrasonic frequencies is on the order of 10 percent at low effective stress, and it decreases to only a few percent at higher stresses. Dispersion varies with degree of saturation and with fluid viscosity in the same way as do low‐frequency attenuation measurements. The results are readily interpreted in terms of the same local‐flow absorption/dispersion mechanism that has been used to explain recent laboratory attenuation measurements. This apparent dispersion places upper bounds on seismic‐to‐sonic velocity differences. It also points out possible discrepancies between seismic velocities and ultrasonic laboratory measurements.

Stuck between a rock and a reflection: A tutorial on low-frequency models for seismic inversion

2017 ◽  
Vol 5 (2) ◽  
pp. B17-B27 ◽  
Author(s):  
Mark Sams ◽  
David Carter
Keyword(s):  
Elastic Properties ◽  
Model Building ◽  
Seismic Velocity ◽  
Low Frequency ◽  
Rock Physics ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Seismic Velocities ◽  
Frequency Model

Predicting the low-frequency component to be used for seismic inversion to absolute elastic rock properties is often problematic. The most common technique is to interpolate well data within a structural framework. This workflow is very often not appropriate because it is too dependent on the number and distribution of wells and the interpolation algorithm chosen. The inclusion of seismic velocity information can reduce prediction error, but it more often introduces additional uncertainties because seismic velocities are often unreliable and require conditioning, calibration to wells, and conversion to S-velocity and density. Alternative techniques exist that rely on the information from within the seismic bandwidth to predict the variations below the seismic bandwidth; for example, using an interpretation of relative properties to update the low-frequency model. Such methods can provide improved predictions, especially when constrained by a conceptual geologic model and known rock-physics relationships, but they clearly have limitations. On the other hand, interpretation of relative elastic properties can be equally challenging and therefore interpreters may find themselves stuck — unsure how to interpret relative properties and seemingly unable to construct a useful low-frequency model. There is no immediate solution to this dilemma; however, it is clear that low-frequency models should not be a fixed input to seismic inversion, but low-frequency model building should be considered as a means to interpret relative elastic properties from inversion.

scholarly journals Analysis of low frequency oscillation and source location in power systems

2018 ◽  
Vol 4 (1) ◽  
pp. 58-66 ◽  
Author(s):  
Yinbiao Shu ◽  
◽  
Xiaoxin Zhou ◽  
Wenfeng Li ◽  
◽  
...  
Keyword(s):  
Power Systems ◽  
Low Frequency ◽  
Source Location ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation

scholarly journals Source Mechanism of Seismic Explosion Signals at Santiaguito Volcano, Guatemala: New Insights From Seismic Analysis and Numerical Modeling

2021 ◽  
Vol 8 ◽  
Author(s):  
Alicia Rohnacher ◽  
Andreas Rietbrock ◽  
Ellen Gottschämmer ◽  
William Carter ◽  
Yan Lavallée ◽  
...  
Keyword(s):  
Particle Motion ◽  
Seismic Data ◽  
Velocity Structure ◽  
Seismic Analysis ◽  
Layer Structure ◽  
Noise Analysis ◽  
Seismic Signal ◽  
Field Investigation ◽  
Source Location ◽  
Seismic Velocities

Volcanic activity at the Santiaguito dome complex (Guatemala) is characterized by lava extrusion interspersed with small, regular, gas-and-ash explosions that are believed to result from shallow magma fragmentation; yet, their triggering mechanisms remain debated. Given that the understanding of source processes at volcanoes is essential to risk assessments of future eruptions, this study seeks to shed light on those processes. We use data from a permanent seismic and infrasound network at Santiaguito volcano, Guatemala, established in 2018 and additional temporary stations, including a seismic array deployed during a 13-day field investigation in January 2019 to analyze and resolve the source characteristics of fragmentation leading to gas-and-ash explosions. Seismic data gathered within a distance of 4.5 km from the vent show a weak seismic signal 2–6 s prior to the explosions and associated main seismic signal. To resolve the source location and origin of the seismic signals, we first used ambient noise analysis to assess seismic velocities in the subsurface and then used two-dimensional spectral element modeling (SPECFEM2D) to simulate seismic waveforms. The analyzed data revealed a two-layer structure beneath the array, with a shallow, low-velocity layer (vs = 650 m/s) above deeper, high-velocity rocks (vs = 2,650 m/s). Using this velocity structure, possible source mechanisms and depths were constrained using array and particle motion analyses. The comparison of simulated and observed seismic data indicated that the precursory signal is associated with particle motion in the RZ-plane, pointing toward the opening of tensile cracks at a depth of ∼600 m below the summit; in contrast, the main signal is accompanied by a vertical single force, originating at a shallow depth of about ∼200 m. This suggests that the volcanic explosions at Santiaguito are following a bottom-up process in which tensile fractures develop at depth and enable rapid gas rise which leads to the subsequent explosion. The result indicates that explosions at Santiaguito do not occur from a single source location, but from a series of processes possibly associated with magma rupture, gas channeling and accumulation, and fragmentation. Our study provides a good foundation for further investigations at Santiaguito and shows the value of comparing seismic observations with synthetic data calculated for complex media to investigate in detail the processes leading up to gas-ash-rich explosions found at various other volcanoes worldwide.

Seismic detection and characterization of a man-made karst analog — A feasibility study

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. WB35-WB48
Author(s):  
Roland Gritto ◽  
Ali Elobaid Elnaiem ◽  
Fateh Alrahman Mohamed ◽  
Fadhil Sadooni
Keyword(s):  
Ambient Noise ◽  
Seismic Imaging ◽  
Imaging Techniques ◽  
A Priori ◽  
Low Frequency ◽  
Sharp Decrease ◽  
Seismic Velocities ◽  
Water Drainage ◽  
Imaging Results ◽  
Noise Data

At the site of a water drainage shaft on the campus of Qatar University that serves as a man-made karst analog, two seismic imaging techniques were adapted to use resonant scattered waves recorded during active-source seismic surveys and during passive ambient-noise surveys. Data acquisition included two seismic transmission surveys that encompassed the shaft and a passive ambient-noise survey that extended across the top of the shaft. Seismic imaging of band-pass-filtered resonance waves correctly estimated the location and dimension of the shaft. Furthermore, the method detected the presence and the location of a horizontal drainage pipe and gravel bed connecting neighboring water shafts. Ambient-noise data were analyzed by computing amplitude values of the seismic records in spectral passbands. The results indicated an amplification of seismic amplitudes above the shaft for low-frequency passbands and a sharp decrease in amplitude values for high-frequency passbands. The high- and low-amplitude values displayed as a function of the receiver position allowed for accurate detection and location of the shaft in space. Ground truthing of the imaging results confirmed the accuracy of the seismic techniques, whereas numerical modeling supported the interpretation of the ambient-noise data. The techniques used do not require knowledge of the seismic velocities in the subsurface, but they depend on a priori information about the approximate location of the target.

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