“See” subsurface soils using surface waves
Zhiqu Lu — firstname.lastname@example.org
National Center for Physical Acoustics, The University of Mississippi,
1 Chucky Mullins,
University, MS, 38677
Lay language paper 4pEA4
Presented Thursday afternoon, November 5, 2015
170th ASA Meeting, Jacksonville
Within a few meters beneath the earth surface, three distinctive soil layers are formed: a top dry and hard layer, a middle moist and soft region, and a deeper zone where the mechanical strength of the soil increases with depth. The information of this subsurface soil is required for agricultural, environmental, civil engineering, and military applications. A seismic surface wave method has been recently developed to non-invasively obtain such information (Lu, 2014; Lu, 2015). The method, known as the multichannel analysis of surface wave method (MASW) (Park, et al., 1999; Xia, et al., 1999), consists of three essential parts: surface wave generation and collection (Figure 1), spectrum analysis, and inversion process. The implement of the technique employs sophisticated sensor technology, wave propagation modeling, and inversion algorithm.
“Figure 1. The experimental setup for the MASW method”
The technique makes use of the characteristic of one type of surface waves, the so-called Rayleigh waves that travel along the earth’s surface within a depth of one and a half wavelengths. Therefore the components of surface waves with short wavelength contain information of shallow soil, whereas the longer wavelength surface waves provide the properties of deep soil (Figure 2).
“Figure 2. Rayleigh wave propagation”
The outcome of the MASW method is a soil vertical profile, i.e., the acoustic shear (S) wave velocity as a function of depth (Figure 3).
“Figure 3. A typical soil profile”
By repeating the MASW measurements either spatially or temporarily, one can measure and “see” the spatial and temporal variations of the subsurface soils. Figure 4 shows a typical vertical cross-section image in which the intensity of the image represents the value of the shear wave velocity. From this image, three different layers mentioned above are identified.
“Figure 4. A typical example of soil vertical cross-section image “
Figure 5 displays another two-dimensional image in which a middle high velocity zone (red area) appears. This high velocity zone represents a geological anomaly, known as a fragipan, a naturally occurring dense and hard soil layer (Lu, et al., 2014). The detection of fragipan is important in agricultural land managements.
“Figure 5. A vertical cross-section image showing the presence of a fragipan layer”
The MASW method can also be applied to monitor weather influence on soil properties (Lu 2014). Figure 6 shows the temporal variations of the underground soil. This is a result of a long term survey conducted in 2012. By drawing a vertical line and moving it from left side to right side, i.e., along the time index number axis, the evolution of the soil profile due to weather effects can be evaluated. In particular, the high velocity zones occurred in the summer of 2012, reflecting very dry soil conditions.
“Figure 6. The temporal variations of soil profile due to weather effects”
Lu, Z., 2014. Feasibility of using a seismic surface wave method to study seasonal and weather effects on shallow surface soils. Journal of Environmental & Engineering Geophysics, DOI: 10.2113/JEEG19.2.71, Vol.19, 71–85.
Lu, Z. 2015. Self-adaptive method for high frequency multi-channel analysis of surface wave method, Journal of Applied Geophysics, Vol. 121, 128-139. http://dx.doi.org/10.1016/j.jappgeo.2015.08.003
Lu, Z., Wilson, G.V., Hickey, C.J., 2014. Imaging a soil fragipan using a high-frequency MASW method. In Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP 2014), Boston, MA., Mar. 16-20.
Park, C.B., Miller, R.D., Xia, J., 1999. Multichannel analysis of surface waves. Geophysics, Vol. 64, 800-808.
Xia, J., Miller, R.D., Park, C.B., 1999. Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics, Vol. 64, 691-700.
Turn around when you’re talking to me!
Jennifer Whiting – email@example.com
Timothy Leishman, PhD – firstname.lastname@example.org
K.J. Bodon – email@example.com
Brigham Young University
N283 Eyring Science Center
Provo, UT 84602
Popular version of paper 2pAAa10, “High-resolution measurements of speech directivity”
Presented Tuesday afternoon, November 3, 2015, 4:40 PM, Grand Ballroom 3
170th ASA Meeting, Jacksonville
In general, most sources of sound do not radiate equally in all directions. The human voice is no exception to this rule. How strongly sound is radiated in a given direction at a specific frequency, or pitch, is called directivity. While many [references] have studied the directivity of speaking and singing voices, some important details are missing. The research reported in this presentation measured directivity of live speech at higher angular and frequency resolutions than have been previously measured, in an effort to capture the missing details.
The approach uses a semicircular array of 37 microphones spaced with five-degree polar-angle increments, see Figure 1. A subject sits on a computer-controlled rotating chair with his or her mouth aligned at the axis of rotation and circular center of the microphone array. He or she repeats a series of phonetically-balanced sentences at each of 72 five-degree azimuthal-angle increments. This results in 2522 measurement points on a sphere around the subject.
[Figure 1. A subject and the measurement array]
The measurements are based on audio recordings of the subject who tries to repeat the sentences with exactly the same timing and inflection at each rotation. To account for the inevitable differences in each repetition, a transfer function and the coherence between a reference microphone near the subject and a measurement microphone on the semicircular array is computed. The coherence is used to examine how good each measurement is. The transfer function for each measurement point makes up the directivity. To visualize the results, each measurement is plotted on a sphere, where the color and the radius of the sphere indicate how strongly sound is radiated in that direction for a given frequency. Animations of these spherical plots show how the directivity differs for each frequency.
[Figure 2. Balloon plot for male speech directivity at 500 and 1000 Hz.]
[Figure 3. Balloon plot for female speech directivity at 500 and 1000 Hz.]
[Animation 1. Male Speech Directivity, animated]
[Animation 2. Female Speech Directivity, animated]
Results and Conclusions
Some unique results are visible in the animations. Most importantly, as frequency increases, one can see that most of the sound is radiated in the forward direction. This is one reason for why it’s hard to hear someone talking in the front of a car when you’re sitting in the back, unless they turn around to talk to you. One can also see in the animations that as frequency increases, and most of the sound radiates forwards, there is poor coherence in the back area. This doesn’t necessarily indicate a poor measurement, just poor signal-to-noise ratio, since there is little sound energy in that direction. It’s also interesting to see that the polar angle of the strongest radiation also changes with frequency. At some frequencies the sound is radiated strongly downward and to the sides, but at other frequencies the stound is radiated strongly upwards and forwards. Male and female directivities are similar in shape, but at different frequencies, since the fundamental frequency of males and females is so different.
A more complete understanding of speech directivity has great benefits to several industries. For example, hearing aid companies can use speech directivity patterns to know where to aim microphones in the hearing aids to pick up the best sound for the hearing aid wearer having a conversation. Microphone placement in cell phones can be adjusted to get clearer signal from those talking into the cell phone. The theater and audio industries can use directivity patterns to assist in positioning actors on stage, or placing microphones near the speakers to record the most spectrally rich speech. The scientific community can develop more complete models for human speech based on these measurements. Further study on this subject will allow researchers to improve the measurement method and analysis techniques to more fully understand the results, and generalize them to all speech containing similar phonemes to those in these measurements.