1,721,008 research outputs found
Crustal wave speed structure of North Texas and Oklahoma based on ambient noise cross-correlation functions and adjoint tomography
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article). Non UTD affiliates will find the web address for this item by clicking the "Show full item record" link, copying the "dc.relation.uri" metadata and pasting it into a browser.Recently, seismologists observed increasing seismicity in NorthTexas and Oklahoma. Based on seismic observations and other geophysical measurements, numerous studies have suggested links between the increasing seismicity andwastewater injection during unconventional oil and gas exploration. To better monitor seismic events and investigate their triggering mechanisms, we need an accurate 3-D crustalwave speed model for the study region. Considering the uneven distribution of earthquakes in this area, seismic tomography with local earthquake records has difficulties achieving even illumination. To overcome this limitation, in this study, ambient noise cross-correlation functions are used to constrain subsurface variations in wave speeds. I use adjoint tomography to iteratively fit frequency-dependent phase differences between observed and predicted band-limited Green's functions. The spectral element method is used to numerically calculate the band-limited Green's functions and the adjoint method is used to calculate misfit gradients with respect to wave speeds. A total of 25 preconditioned conjugate gradient iterations is used to updatemodel parameters and minimize datamisfits. Features in the new crustal model TO25 correlate well with geological provinces in the study region, including the Llano uplift, the Anadarko basin, the Ouachita orogenic front, etc. In addition, there are relatively good correlations between seismic results with gravity and magnetic observations. This new crustal model can be used to better constrain earthquake source parameters in North Texas and Oklahoma, such as epicentre location as well as moment tensor solutions, which are important for investigating triggering mechanisms between these induced earthquakes and unconventional oil and gas exploration activities. © The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society.School of Natural Sciences and Mathematic
Seismogram registration via Markov chain Monte Carlo optimization and its applications in full waveform inversion
No full textCycle skipping is a serious issue in full waveform inversion (FWI) since it leads to local minima. To date, most FWI algorithms depend on local gradient based optimization approaches, which cannot guarantee convergence towards the global minimum if the misfit function involves local minima and the starting model is far from the true solution. In this study, I propose a misfit function based on non-stationary time warping functions, which can be calculated by solving a seismogram registration problem. Considering the inherent cycle skipping and local minima issues of the registration problem, I use a Markov chain Monte Carlo (MCMC) method to solve it. With this global optimization approach, I am able to directly sample the global minimum and measure non-stationary traveltime differences between observed and predicted seismograms. The a priori constraint about the sparsity of the local warping functions is incorporated to eliminate unreasonable solutions. No window selections are required in this procedure. In comparison to other approaches for measuring traveltime differences, the proposed method enables us to align signals with different numbers of events. This property is a direct consequence of the usage of MCMC optimization and sparsity constraints. Several numerical examples demonstrate that the proposed misfit function allows us to tackle the cycle skipping problem and construct accurate long-wavelength velocity models even without low frequency data and good starting models.School of Natural Sciences and Mathematic
Elastic Wavefield Separation in Anisotropic Media Based on Eigenform Analysis and Its Application in Reverse-Time Migration
No full textSeparating compressional and shear wavefields is an important step in elastic reverse-time migration, which can remove wave-mode crosstalk artefacts and improve imaging quality. In vertical (VTI) and titled (TTI) transversely isotropic media, the state-of-the-art techniques for wavefield separation are based on either non-stationary filter or low-rank approximation. They both require intensive Fourier transforms for models with strong heterogeneity. Based on the eigenform analysis, we develop an efficient pseudo-Helmholtz decomposition method for the VTI and TTI media, which produces vector P and S wavefields with the same amplitudes, phases and physical units as the input elastic wavefields. Starting from the elastic VTI wave equations, we first derive the analytical eigenvalues and eigenvectors, then use the Taylor expansion to approximate the square-root term in the eigenvalues, and finally obtain a zero-order and a first-order pseudo-Helmholtz decomposition operator. Because the zero-order operator is the true solution for the case of ϵ = δ, it produces accurate wavefield separation results for elliptical anisotropic media. The first-order separation operator is more accurate for non-elliptical anisotropy. Since the proposed pseudo-Helmholtz decomposition requires solving an anisotropic Poisson's equation, we propose two fast numerical solvers. One is based on the sparse lower-upper (LU) factorization, which can be repeatedly applied to the input elastic wavefields once computing the lower and upper triangular matrices. The second solver assumes the model parameters are laterally homogeneous within a given migration aperture. This assumption allows us to efficiently solve the anisotropic Poisson's equation in the z k x domain, where k x and z denote the horizontal wavenumber and depth, respectively. Using the coordinate transform, we extend the pseudo-Helmholtz decomposition to the TTI media. The separated vector wavefields are used to produce PP and PS images by applying a dot-product imaging condition. Several numerical examples demonstrate the feasibility and applicability of the proposed methods. © The Author(s) 2019. Published by Oxford University Press on behalf of The Royal Astronomical Society.Natural Sciences and Mathematic
Locating and monitoring microseismicity, hydraulic fracture and earthquake rupture using elastic time-reversal imaging
No full textLocating and monitoring passive seismic sources provides us important information for studying subsurface rock deformation, injected fluid migration, regional stress conditions as well as fault rupture mechanism. In this paper, we present a novel passive-source monitoring approach using vector-based elastic time-reversal imaging. By solving the elastic wave equation using observed multicomponent records as boundary conditions, we first compute back-propagated elastic wavefields in the subsurface. Then, we separate the extrapolated wavefields into compressional (P-wave) and shear (S-wave) modes using the vector Helmholtz decomposition. A zero-lag cross-correlation imaging condition is applied to the separated pure-mode vector wavefields to produce passive-source images. We compare imaging results using three implementations, that is dot-product, energy and power. Numerical experiments demonstrate that the power imaging condition gives us the highest resolution and is less sensitive to the presence of random noises. To capture the propagation of microseismic fracture and earthquake rupture, we modify the traditional zero-lag cross-correlation imaging condition by summing the multiplication of the separated P and S wavefields within local time windows, which enables us to capture the temporal and spatial evolution of earthquake rupture. 2-D and 3-D numerical examples demonstrate that the proposed method is capable of accurately locating point sources, as well as delineating dynamic propagation of hydraulic fracture and earthquake rupture.School of Natural Sciences and Mathematic
Full-waveform inversion using seislet regularization
No full text Because of inaccurate, incomplete, and inconsistent waveform records, full-waveform inversion (FWI) in the framework of a local optimization approach may not have a unique solution, and thus it remains an ill-posed inverse problem. To improve the robustness of FWI, we have developed a new model regularization approach that enforced the sparsity of solutions in the seislet domain. The construction of seislet basis functions requires structural information that can be estimated iteratively from migration images. We implement FWI with seislet regularization using nonlinear shaping regularization and impose sparseness by applying soft thresholding on the updated model in the seislet domain at each iteration of the data-fitting process. The main extra computational cost of the method relative to standard FWI is the cost of applying forward and inverse seislet transforms at each iteration. This cost is almost negligible compared with the cost of solving wave equations. Numerical tests using the synthetic Marmousi model demonstrate that seislet regularization can greatly improve the robustness of FWI by recovering high-resolution velocity models, particularly in the presence of strong crosstalk artifacts from simultaneous sources or strong random noise in the data. </jats:p
Time-domain least-squares migration using the Gaussian beam summation method
No full textArticle is freely available on publisher's website. Use the Link to ArticleWith a finite recording aperture, a limited source spectrum and unbalanced illumination, traditional imaging methods are insufficient to generate satisfactory depth profiles with high resolution and high amplitude fidelity. This is because traditional migration uses the adjoint operator of the forward modelling rather than the inverse operator.We propose a least-squares migration approach based on the time-domain Gaussian beam summation, which helps to balance subsurface illumination and improve image resolution. Based on the Born approximation for the isotropic acoustic wave equation, we derive a linear time-domain Gaussian beam modelling operator, which significantly reduces computational costs in comparison with the spectral method. Then, we formulate the corresponding adjoint Gaussian beam migration, as the gradient of an L2-norm waveform misfit function. An L1-norm regularization is introduced to the inversion to enhance the robustness of least-squares migration, and an approximated diagonal Hessian is used as a pre-conditioner to speed convergence. Synthetic and field data examples demonstrate that the proposed approach improves imaging resolution and amplitude fidelity in comparison with traditional Gaussian beam migration. © The Author(s) 2018. Published by Oxford University Press on behalf of The Royal Astronomical Society.School of Natural Sciences and Mathematic
Full Wavefield Reconstruction and Full Waveform Inversion
No full textTwo-way reverse time extrapolation of the recorded seismic data is the essential step in seismic reverse time migration (RTM). Various RTM algorithms have been developed to produce
accurate image locations rather than correct amplitude information because of inadequate
compensation of attenuation, dispersion, and transmission losses. Thus, there is a need
to evaluate the requirements, and determined the theoretical feasibility, of true amplitude
recovery of recorded seismic data. We apply analytic Zoeppritz equations and numerical elastodynamic finite differencing to evaluate the validity and requirements for true amplitude
recovery of incident plane and spherical waves, respectively. An application of true amplitude recovery to remove downgoing reflections produced at interfaces at depths shallower
than a horizontal well, in reconstructed incident wavefields, is demonstrated.
The migration performance also relies on the accuracy of the velocity model, which determines both the kinematics (image locations) and amplitude information. Conventional full
waveform inversion (FWI) is a least-squares optimization method to update a seismic velocity
model by iteratively fitting the calculated data to the observed data. We propose three FWI
algorithms to update a seismic velocity model in different ways: parametric convolutional neural-network-domain FWI (CNN-domain FWI), source-domain FWI, and CNN-boosted
FWI. Parametric CNN-domain FWI implements CNN within full waveform inversion to automatically capture the salient features (e.g., salt bodies) in a given initial training velocity
model, and then concentrate the velocity inversion on the captured features in the velocity model. Source-domain FWI updates velocity model by minimizing source-domain misfit
function, which contains the least-squares virtual source artifacts. Virtual source artifacts
are created by replacing the propagating source wavefield by the forward-time observed data
at the receiver positions, as a data-residual constraint. Similar to conventional FWI, source domain FWI can be implemented in either the frequency or the time domain, which is unlike
previous source-domain solutions, which have to be implemented only in the time domain, to
solve the normal equations. CNN-boosted FWI applies CNN as a weak learner, at each iteration, to approximate the model residuals, by minimizing the data residuals. In addition to
finding the optimal step length, just as gradient-descent FWI does, CNN-boosted FWI fixes
this optimal step length and optimizes the CNN, which is originally trained to approximate
the negative gradients at each iteration, to update the velocity model. CNN-boosted FWI
inverts for the velocity model with lower model and data errors than the gradient-descent
FWI does
Ambient noise full waveform inversion for noise source distribution
No full textWe use full waveform inversion of ambient noise to estimate noise source distributions and
model parameters. We derive the sensitivity kernel with respect to a noise source distribution
from ambient noise data. To evaluate the capability of the algorithm, we perform several
numerical examples. Our approach is to a simulate propagation of a diffusive wavefield and
to calculate interstation correlograms. Waveform differences are used to measure differences
between predicted correlograms, and correlation functions computed from observed seismic
recordings. This measurement is used as a adjoint source to propagate the adjoint wavefield,
and to calculate the associated gradients. Due to the low resolution and accuracy, we apply
L1 model regularization in the Fourier domain to constrain the inversion result and remove
artifacts. As with conventional full waveform inversion, the inversion with ambient noise
information suffers from multi-parameter problems. We test the capability of ambient noise
full waveform inversion with a complicated velocity model. The inversion results illustrate
that full waveform inversion of ambient noise algorithm can give an accurate estimation of
noise source distribution with sparse data acquisition
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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