Near surface solutions in South Rub Al-Khali, Saudi Arabia applying seismic-gravity joint inversion and redatuming

ABSTRACT Empirical models of geometrical-, Q-, t-star, and kappa-type attenuation of seismic waves and ground-motion prediction equations (GMPEs) are viewed as cases of a common empirical standard model describing variation of wave amplitudes with time and frequency. Compared with existing parametric and nonparametric approaches, several new features are included in this model: (1) flexible empirical parameterization with possible nonmonotonous time or distance dependencies; (2) joint inversion for time or distance and frequency dependencies, source spectra, site responses, kappas, and Q; (3) additional constraints removing spurious correlations of model parameters and data residuals with source–receiver distances and frequencies; (4) possible kappa terms for sources as well as for receivers; (5) orientation-independent horizontal- and three-component amplitudes; and (6) adaptive filtering to reduce noise effects. The approach is applied to local and regional S-wave amplitudes in southeastern Iran. Comparisons with previous studies show that conventional attenuation models often contain method-specific biases caused by limited parameterizations of frequency-independent amplitude decays and assumptions about the models, such as smoothness of amplitude variations. Without such assumptions, the frequency-independent spreading of S waves is much faster than inferred by conventional modeling. For example, transverse-component amplitudes decrease with travel time t as about t−1.8 at distances closer than 90 km and as t−2.5 beyond 115 km. The rapid amplitude decay at larger distances could be caused by scattering within the near surface. From about 90 to 115 km distances, the amplitude increases by a factor of about 3, which could be due to reflections from the Moho and within the crust. With more accurate geometrical-spreading and kappa models, the Q factor for the study area is frequency independent and exceeds 2000. The frequency-independent and Q-type attenuation for vertical-component and multicomponent amplitudes is somewhat weaker than for the horizontal components. These observations appear to be general and likely apply to other areas.

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Inversion of aeromagnetic data using digital terrain models

Geophysics ◽

10.1190/1.1443448 ◽

1993 ◽

Vol 58 (5) ◽

pp. 645-652 ◽

Cited By ~ 3

Author(s):

Derek J. Woodward

Keyword(s):

Joint Inversion ◽

Digital Terrain Model ◽

Vertical Direction ◽

Inverse Function ◽

Magnetic Data ◽

Stability Field ◽

Topographic Effects ◽

Near Surface ◽

Digital Terrain ◽

White Island

Although draped magnetic surveys contain more information about the magnetization of the rocks near the surface of the earth than surveys at constant elevation, allowance for the effects of the terrain is critical for their correct interpretation. A new method for calculating the magnetic effect of the topography from a digital terrain model by integrating analytically in the vertical direction and then numerically in the horizontal plane is presented. This method lends itself to the calculation of anomalies when the magnetization of the rocks varies with position and thus is well suited to the inversion of draped aeromagnetic surveys to obtain the apparent magnetization of the surficial rocks. This inversion is achieved by repeated use of an approximate inverse function in the form of a two‐dimensional (2-D) filter that is applied to gridded data. An example, using draped magnetic data collected over White Island, an active volcanic island of high relief, shows that although the anomaly pattern is dominated by topographic effects, the distribution of near‐surface magnetic bodies can be determined by a joint inversion of the data and the topography. One of the highly magnetized areas of White Island is interestingly in the vicinity of the active crater, with another near the inner wall of the caldera where there are numerous fumaroles. It may be expected that the higher temperatures in these areas would reduce the magnetization. However, it appears that an explanation for the higher magnetization can be found in the stability field of the mineral magnetite.

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Near-surface modeling challenges highlighted in recent workshop

The Leading Edge ◽

10.1190/tle39050354.1 ◽

2020 ◽

Vol 39 (5) ◽

pp. 354-356

Author(s):

Abdulaziz Saad ◽

Moosa Al-Jahdhami

Keyword(s):

Guided Waves ◽

Joint Inversion ◽

Waveform Inversion ◽

Surface Modeling ◽

Hydrocarbon Exploration ◽

Surface Model ◽

Near Surface ◽

Velocity Inversion ◽

Geophysical Imaging ◽

Near Surface Geophysics

Despite technological and computational advances in geophysical imaging, near-surface geophysics continues to pose significant challenges in modeling and imaging the subsurface. Geoscientists from around the world attended the first and second editions of the SEG/DGS Near-surface Modeling and Imaging Workshop in 2014 and 2016 to address these challenges. A range of near-surface disciplines were represented from academia and industry, covering aspects of engineering and hydrocarbon exploration. The previous workshops explored emerging and underdeveloped techniques, including deep learning (machine learning), nonseismic methods, full-waveform inversion (FWI), and joint inversion. The necessity to further understand guided waves, anisotropy, velocity inversion, and the creation of an inclusive near-surface model was identified. The previous editions led to a greater understanding of the importance of knowledge sharing among various disciplines in modeling and imaging of the near surface.

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Dispersion and the dissipative characteristics of surface waves in the generalized S-transform domain

Geophysics ◽

10.1190/geo2010-0330.1 ◽

2012 ◽

Vol 77 (1) ◽

pp. V11-V20 ◽

Cited By ~ 21

Author(s):

Roohollah Askari ◽

Robert J. Ferguson

Keyword(s):

Cost Function ◽

Surface Waves ◽

Joint Inversion ◽

Scaling Factor ◽

Surface Shear ◽

Low Frequencies ◽

Near Surface ◽

S Transform ◽

Generalized S Transform ◽

The Cost

Wavenumber, group velocity, phase velocity, and frequency-dependent attenuation characterize the propagation of surface waves in dispersive, attenuating media. We use a mathematical model based on the generalized [Formula: see text] transform to simultaneously estimate these characteristic parameters for later use in joint inversion for near-surface shear wave velocity. We use a scaling factor in the generalized S transform to enable the application of the method in a highly dispersive medium. We introduce a cost function in the [Formula: see text]-domain to estimate an optimum value for the scaling factor. We also use the cost function to generalize the application of the method for noisy data, especially data with a low signal-to-noise ratio at low frequencies. In that case, the estimated wavenumber is perturbed. As a solution, we estimate wavenumber perturbation by minimizing the cost function, using Simulated Annealing. We use synthetic and real data to show the efficiency of the method for the estimation of the propagation parameters of highly dispersive and noisy media.

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