Constraints on Crustal P‐wave Structure with Leaking Mode Dispersion Curves

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

Download Full-text

Automatic picking of multi-mode dispersion curves using CNN-based machine learning

10.1190/segam2020-3427827.1 ◽

2020 ◽

Author(s):

Li Ren ◽

Fuchun Gao ◽

Yulang Wu ◽

Paul Williamson ◽

Wenlong Wang ◽

...

Keyword(s):

Machine Learning ◽

Dispersion Curves ◽

Mode Dispersion ◽

Automatic Picking ◽

Multi Mode

Download Full-text

Internal optic mode dispersion curves and frequency distribution functions in dipole coupled crystals: Applications to NaN3and KBrO3

The Journal of Chemical Physics ◽

10.1063/1.1681369 ◽

1974 ◽

Vol 60 (6) ◽

pp. 2354-2359 ◽

Cited By ~ 3

Author(s):

Roger Frech

Keyword(s):

Frequency Distribution ◽

Dispersion Curves ◽

Distribution Functions ◽

Mode Dispersion

Download Full-text

The P-wave structure of the lithosphere-asthenosphere in the Western Pacific: a comparison of ocean versus continent

Tectonophysics ◽

10.1016/0040-1951(88)90149-7 ◽

1988 ◽

Vol 147 (1-2) ◽

pp. 85-93 ◽

Cited By ~ 2

Author(s):

Shozaburo Nagumo ◽

Kasahara Junzo

Keyword(s):

Wave Structure ◽

Western Pacific ◽

P Wave ◽

The Western Pacific

Download Full-text

Software Development for Guided waves Dispersion curves and Wave structure

The Proceedings of the Materials and processing conference ◽

10.1299/jsmemp.2003.11.273 ◽

2003 ◽

Vol 2003.11 (0) ◽

pp. 273-274

Author(s):

Chiga Tamayama ◽

Morimasa Murase ◽

Takahiro Hayashi ◽

Koichiro Kawashima

Keyword(s):

Software Development ◽

Guided Waves ◽

Wave Structure ◽

Dispersion Curves

Download Full-text

DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency–Bessel Dispersion Spectrum

Bulletin of the Seismological Society of America ◽

10.1785/0120210033 ◽

2021 ◽

Author(s):

Sheng Dong ◽

Zhengbo Li ◽

Xiaofei Chen ◽

Lei Fu

Keyword(s):

Surface Wave ◽

Dispersion Curve ◽

Shear Wave ◽

Wave Structure ◽

Wave Dispersion ◽

Dispersion Curves ◽

Surface Wave Dispersion ◽

Transform Method ◽

Practical Applications ◽

Noise Data

ABSTRACT The subsurface shear-wave structure primarily determines the characteristics of the surface-wave dispersion curve theoretically and observationally. Therefore, surface-wave dispersion curve inversion is extensively applied in imaging subsurface shear-wave velocity structures. The frequency–Bessel transform method can effectively extract dispersion spectra of high quality from both ambient seismic noise data and earthquake events data. However, manual picking and semiautomatic methods for dispersion curves lack a unified criterion, which impacts the results of inversion and imaging. In addition, conventional methods are insufficiently efficient; more precisely, a large amount of time is required for curve extraction from vast dispersion spectra, especially in practical applications. Thus, we propose DisperNet, a neural network system, to extract and discriminate the different modes of the dispersion curve. DisperNet consists of two parts: a supervised network for dispersion curve extraction and an unsupervised method for dispersion curve classification. Dispersion spectra from ambient noise and earthquake events are applied in training and validation. A field data test and transfer learning test show that DisperNet can stably and efficiently extract dispersion curves. The results indicate that DisperNet can significantly improve multimode surface-wave imaging.

Download Full-text

Interatomic forces in hexamethylenetetramine

Proceedings of the Royal Society of London Series A - Mathematical and Physical Sciences ◽

10.1098/rspa.1973.0068 ◽

1973 ◽

Vol 333 (1594) ◽

pp. 363-384 ◽

Cited By ~ 26

Keyword(s):

Internal Force ◽

Dispersion Curves ◽

Force Constants ◽

Dynamical Matrix ◽

Force System ◽

Interatomic Force ◽

Pair Potentials ◽

External Mode ◽

Mode Dispersion ◽

Rigid Molecule

A formalism expressing the intermolecular mode frequencies of rigid molecules explicitly in terms of phenomenological interatomic force constants is developed for a general uni-molecular crystal. The restraints on the force constants necessary for the dynamical matrix to be hermitian for all symmetries and general force fields are given. This ‘interatomic’ lattice dynamical model is used to analyse the intermolecular mode dispersion curves of deuterated hexamethylenetetramine (DHMT), assuming various interatomic force systems. It is found that an unambiguous choice of the best force system cannot be made from this analysis, but the shortest D. . . N bond is found to be the dominant bond. A ‘6-exponential’ form is then assumed for the interatomic pair potentials in DHMT, and the constants of these potentials are determined by fitting to the DHMT dispersion curves. Most of the constants are poorly determined by this analysis, only the long-range part of the D-D interaction showing a significant change from the values independently estimated from hydrocarbons. The ‘rigid molecule’ assumption is then removed, and using the fitted interatomic pair potentials and the known internal force field the effect of the internal vibrations on the intermolecular modes is calculated. Significant shifts, larger than the experimental error in some cases, are found in many external mode frequencies due to the molecular distortions. Small shifts are also found in the internal mode frequencies, some of them being unexpectedly negative. The rigid molecule approximation is indicated to be of doubtful validity for most molecules, but an approximate procedure is suggested for retaining it in analyses of external mode dispersion curves. Certain systematic discrepancies are noted in the fitting and it is suggested that neglect of the octopole moment of the DHMT molecule may be the cause.

Download Full-text

On the Utility of Horizontal-to-Vertical Spectral Ratios of Ambient Noise in Joint Inversion with Rayleigh Wave Dispersion Curves for the Large-N Maupasacq Experiment

Sensors ◽

10.3390/s21175946 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5946

Author(s):

Maik Neukirch ◽

Antonio García-Jerez ◽

Antonio Villaseñor ◽

Francisco Luzón ◽

Jacques Brives ◽

...

Keyword(s):

Wave Velocity ◽

Large Scale ◽

Joint Inversion ◽

Dispersion Curves ◽

P Wave ◽

Gravity Inversion ◽

Spectral Ratios ◽

Near Surface ◽

Dispersion Data ◽

Short Period

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity (VS) structure. Knowing the VS structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity (VP) structures such as travel time tomography or full waveform inversions or to directly study the VS structure for geo-engineering purposes (e.g., ground motion prediction). The joint inversion of HVSR and dispersion data for 1D VS structure allows characterising the uppermost crust and near surface, where the HVSR data (0.03 to 10s) are most sensitive while the dispersion data (1 to 30s) constrain the deeper model which would, otherwise, add complexity to the HVSR data inversion and adversely affect its convergence. During a large-scale experiment, 197 three-component short-period stations, 41 broad band instruments and 190 geophones were continuously operated for 6 months (April to October 2017) covering an area of approximately 1500km2 with a site spacing of approximately 1 to 3km. Joint inversion of HVSR and DC allowed estimating VS and, to some extent density, down to depths of around 1000m. Broadband and short period instruments performed statistically better than geophone nodes due to the latter’s gap in sensitivity between HVSR and DC. It may be possible to use HVSR data in a joint inversion with DC, increasing resolution for the shallower layers and/or alleviating the absence of short period DC data, which may be harder to obtain. By including HVSR to DC inversions, confidence improvements of two to three times for layers above 300m were achieved. Furthermore, HVSR/DC joint inversion may be useful to generate initial models for 3D tomographic inversions in large scale deployments. Lastly, the joint inversion of HVSR and DC data can be sensitive to density but this sensitivity is situational and depends strongly on the other inversion parameters, namely VS and VP. Density estimates from a HVSR/DC joint inversion should be treated with care, while some subsurface structures may be sensitive, others are clearly not. Inclusion of gravity inversion to HVSR/DC joint inversion may be possible and prove useful.

Download Full-text