Near-surface characterization using traveltime and full-waveform inversion with vertical and horizontal component seismic data

2019 ◽  
Vol 7 (1) ◽  
pp. T141-T154 ◽  
Author(s):  
Md. Iftekhar Alam
Keyword(s):  
Surface Characterization ◽  
Seismic Waves ◽  
Seismic Velocity ◽  
Waveform Inversion ◽  
Horizontal Component ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform ◽  
Attenuation Models

Seismic imaging of the shallow subsurface (approximately 5 m) can be very challenging when reflections are absent and the data are dominated by ground roll. I analyzed the transmission coda to produce fine-scale, interpretable vertical and horizontal component seismic velocity ([Formula: see text] and [Formula: see text]) models using full-waveform inversion (FWI). Application of FWI is tested through imaging two buried targets. The first target is a pair of well-documented utility pipes with known diameters (0.8 m) and burial depths (approximately 1.5 m). The second target is a poorly documented former location of the pipe(s), which is now a backfilled void. Data are acquired along a 23 m 2D profile using a static array with single-component vertical and horizontal geophones. Our results indicate considerable velocity updates in the [Formula: see text] and [Formula: see text] models across the pipes and backfill. The pipes appear as negative velocity updates in the final inverted [Formula: see text] and [Formula: see text] models, whereas the backfilled area represents negative and positive velocity updates in the [Formula: see text] and [Formula: see text] models, respectively. Variations of the polarities in the inverted models ([Formula: see text] and [Formula: see text]) across the backfill can be indicative of the medium, which respond differently to the vertical and horizontal component seismic waves. The attenuation models show a general decreasing trend with increasing depth. Therefore, simultaneous applications of vertical ([Formula: see text]) and horizontal ([Formula: see text]) component seismic velocity modeling can be an effective tool to understand the subsurface medium in near-surface characterization.

Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Velocity Mapping ◽  
Signal To Noise ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

Seismic SH Full Waveform Inversion: A tool for high-resolution near-surface characterization

2021 ◽  
Author(s):  
D. Köhn ◽  
M. Zolchow ◽  
R. Mecking ◽  
D. Wilken ◽  
T. Wunderlich ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Characterization ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform

Full Waveform Inversion for the Near Surface Characterization, Onshore UAE, Case Study

2021 ◽  
Author(s):  
Rahul Dixit ◽  
Pavel Vasilyev ◽  
Ivica Mihaljevic ◽  
Michelle Tham ◽  
Denes Vigh ◽  
...  
Keyword(s):  
Seismic Data ◽  
Surface Characterization ◽  
Waveform Inversion ◽  
Surface Modeling ◽  
Surface Model ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Seismic Responses ◽  
Near Surface ◽  
Full Waveform

Abstract Full-waveform inversion (FWI) has become a well-established method for obtaining a detailed earth model suitable for improved imaging, near-surface characterization and pore-pressure prediction. FWI for onshore data has always been challenging and has seen limited application (Vigh et al, 2018). It requires a dedicated data processing approach related to the lower signal-to-noise ratio, accounting for variable topography and complex near-surface related effects. During the past few years, ADNOC has been acquiring and processing one of the world's largest combined 3D onshore and offshore seismic surveys in the Emirate of Abu Dhabi. The modern acquisition parameters that were implemented enabled the acquisition of broadband onshore seismic data rich in low frequencies that could benefit the initial stages of the FWI workflow. Sand dunes and sabkha layers at the surface, and high-velocity carbonate and dolomite layers in the subsurface pose a significant challenge for near-surface modeling in the UAE. The purpose of this work is to evaluate FWI application onshore UAE for near-surface characterization. We will compare the FWI results with conventional approaches for the near-surface model building that has been used routinely on land datasets in UAE, such as data-driven image-based statics (DIBS, Zarubov et al, 2019). One of the main challenges is data preconditioning, as onshore seismic data typically exhibits high levels of noise. It is imperative to denoise gathers sufficiently prior to the FWI process. A well sonic velocity function with large smoothing was used to build the starting velocity model for FWI. The process aims to minimize the least-squared difference between predicted and observed seismic responses by means of updating the model on which the prediction is based. As the predicted and seismic responses are functions of model parameters as well as source signature, a good estimate of the source wavelet is important for update and convergence in FWI. During this FWI work, source wavelet inversion was done as a separate step and used in subsequent FWI passes. FWI inversion started with adjustive FWI (Kun et al, 2015) on lower frequencies, moving to higher frequencies where both adjustive and least square objective functions were used. We will further show assessment of the anisotropy, initial conditions, usage of geological constraints, and comparisons to the conventional solutions. A comparison of results shows that FWI has successfully added velocity details to the near-surface model that follow the geological trend and conforms to well information while producing a plausible static solution. We have demonstrated the application of FWI onshore UAE for near-surface modeling. Although turnaround time (TAT) has increased compared to the conventional approach, the learning that was gained during this trial will decrease TAT for the future FWI work.

FULL-WAVEFORM INVERSION WITH SOURCE AND RECEIVER COUPLING EFFECTS CORRECTION

Geophysics ◽  
2021 ◽  
pp. 1-37
Author(s):  
Xinhai Hu ◽  
Wei Guoqi ◽  
Jianyong Song ◽  
Zhifang Yang ◽  
Minghui Lu ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Linear Inversion ◽  
Near Surface ◽  
Model Parameter ◽  
Full Waveform ◽  
Coupling Factors

Coupling factors of sources and receivers vary dramatically due to the strong heterogeneity of near surface, which are as important as the model parameters for the inversion success. We propose a full waveform inversion (FWI) scheme that corrects for variable coupling factors while updating the model parameter. A linear inversion is embedded into the scheme to estimate the source and receiver factors and compute the amplitude weights according to the acquisition geometry. After the weights are introduced in the objective function, the inversion falls into the category of separable nonlinear least-squares problems. Hence, we could use the variable projection technique widely used in source estimation problem to invert the model parameter without the knowledge of source and receiver factors. The efficacy of the inversion scheme is demonstrated with two synthetic examples and one real data test.

Tunnel detection at Yuma Proving Ground, Arizona, USA — Part 1: 2D full-waveform inversion experiment

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. B95-B105 ◽  
Author(s):  
Yao Wang ◽  
Richard D. Miller ◽  
Shelby L. Peterie ◽  
Steven D. Sloan ◽  
Mark L. Moran ◽  
...  
Keyword(s):  
Surface Wave ◽  
Waveform Inversion ◽  
Velocity Profiles ◽  
Infinite Length ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Near Surface ◽  
Tunnel Detection ◽  
Full Waveform ◽  
Wave Methods

We have applied time domain 2D full-waveform inversion (FWI) to detect a known 10 m deep wood-framed tunnel at Yuma Proving Ground, Arizona. The acquired seismic data consist of a series of 2D survey lines that are perpendicular to the long axis of the tunnel. With the use of an initial model estimated from surface wave methods, a void-detection-oriented FWI workflow was applied. A straightforward [Formula: see text] quotient masking method was used to reduce the inversion artifacts and improve confidence in identifying anomalies that possess a high [Formula: see text] ratio. Using near-surface FWI, [Formula: see text] and [Formula: see text] velocity profiles were obtained with void anomalies that are easily interpreted. The inverted velocity profiles depict the tunnel as a low-velocity anomaly at the correct location and depth. A comparison of the observed and simulated waveforms demonstrates the reliability of inverted models. Because the known tunnel has a uniform shape and for our purposes an infinite length, we apply 1D interpolation to the inverted [Formula: see text] profiles to generate a pseudo 3D (2.5D) volume. Based on this research, we conclude the following: (1) FWI is effective in near-surface tunnel detection when high resolution is necessary. (2) Surface-wave methods can provide accurate initial S-wave velocity [Formula: see text] models for near-surface 2D FWI.

A focusing study of near-surface full-waveform inversion based on wave-mode separation

2017 ◽  
Author(s):  
Yao Wang ◽  
Richard Miller ◽  
Shelby Peterie ◽  
Steven Sloan ◽  
Mark Moran ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Wave Mode ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform ◽  
Mode Separation

3D elastic full-waveform inversion of surface waves in the presence of irregular topography using an envelope-based misfit function

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Fuchun Gao ◽  
Jeroen Tromp
Keyword(s):  
Surface Wave ◽  
Objective Function ◽  
Surface Waves ◽  
Large Scale ◽  
Body Wave ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Near Surface ◽  
Full Waveform

Full-waveform inversion (FWI) is a powerful method for estimating the earth’s material properties. We demonstrate that surface-wave-driven FWI is well-suited to recovering near-surface structures and effective at providing S-wave speed starting models for use in conventional body-wave FWI. Using a synthetic example based on the SEG Advanced Modeling phase II foothills model, we started with an envelope-based objective function to invert for shallow large-scale heterogeneities. Then we used a waveform-difference objective function to obtain a higher-resolution model. To accurately model surface waves in the presence of complex tomography, we used a spectral-element wave-propagation solver. Envelope misfit functions are found to be effective at minimizing cycle-skipping issues in surface-wave inversions, and surface waves themselves are found to be useful for constraining complex near-surface features.

Targeted stacking of weak seismic phases for improving mantle discontinuity imaging using full-waveform inversion

2021 ◽  
Author(s):  
Maria Koroni ◽  
Andreas Fichtner
Keyword(s):  
Seismic Waves ◽  
Waveform Inversion ◽  
Real Data ◽  
Full Waveform Inversion ◽  
Finite Frequency ◽  
Adjoint Methods ◽  
Frequency Sensitivity ◽  
Full Waveform ◽  
Discontinuity Structure ◽  
Mantle Discontinuity

<p>This study is a continuation of our efforts to connect adjoint methods and full-waveform inversion to common beamforming techniques, widely used and developed for signal enhancement. Our approach is focusing on seismic waves traveling in the Earth's mantle, which are phases commonly used to image internal boundaries, being however quite difficult to observe in real data. The main goal is to accentuate precursor waves arriving in well-known times before some major phase. These waves generate from interactions with global discontinuities in the mantle, thus being the most sensitive seismic phases and therefore most suitable for better understanding of discontinuity seismic structure. </p><p>Our work is based on spectral-element wave propagation which allows us to compute exact synthetic waveforms and adjoint methods for the calculation of sensitivity kernels. These tools are the core of full-waveform inversion and by our efforts we aim to incorporate more parts of the waveform in such inversion schemes. We have shown that targeted stacking of good quality waveforms arriving from various directions highlights the weak precursor waves. It additionally makes their traveltime finite frequency sensitivity prominent. This shows that we can benefit from using these techniques and exploit rather difficult parts of the seismogram.  It was also shown that wave interference is not easily avoided, but coherent phases arriving before the main phase also stack well and show on the sensitivity kernels. This does not hamper the evaluation of waveforms, as in a misfit measurement process one can exploit more phases on the body wave parts of seismograms.</p><p>In this study, we go a step forward and present recent developments of the approach relating to the effects of noise and a real data experiment. Realistic noise is added to synthetic waveforms in order to assess the methodology in a more pragmatic scenario. The addition of noise shows that stacking of coherent seismic phases is still possible and the sensitivity kernels of their traveltimes are not largely distorted, the precursor waves contribute sufficiently to their traveltime finite-frequency sensitivity kernels.<br>Using a well-located seismic array, we apply the method to real data and try to examine the possibilities of using non-ideal waveforms to perform imaging of the mantle discontinuity structure on the specific areas. In order to make the most out of the dense array configuration, we try subgroups of receivers for the targeted stacking and by moving along the array we aim at creating a cluster of stacks. The main idea is to use the subgroups as single receivers and create an evaluation of seismic discontinuity structure using information from each stack belonging to a subgroup. <br>Ideally, we aim at improving the tomographic images of discontinuities of selected regions by exploiting weaker seismic waves, which are nonetheless very informative.</p>

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