Bayesian survey design to optimize resolution in waveform inversion

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. R81-R93 ◽  
Author(s):  
Hugues A. Djikpesse ◽  
Mohamed R. Khodja ◽  
Michael D. Prange ◽  
Sebastien Duchenne ◽  
Henry Menkiti
Keyword(s):  
Seismic Data ◽  
Survey Design ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Region Of Interest ◽  
Model Parameters ◽  
Design Algorithm ◽  
Seismic Surveys ◽  
Optimal Experiment ◽  
Confidence Ellipsoid

We describe a Bayesian methodology for designing seismic experiments that optimally maximize model-parameter resolution for imaging purposes. The proposed optimal experiment design algorithm finds the measurements that are likely to optimally reduce the expected uncertainty on the model parameters. This Bayesian [Formula: see text]-optimality-based algorithm minimizes the volume of the expected confidence ellipsoid and leads to the maximization of the expected resolution of the model parameters. Computational efficiency is achieved by a greedy algorithm in which the design is sequentially improved. In contrast to minimizing the uncertainty volume over the entire subsurface simultaneously, a refinement of the algorithm minimizes the marginal uncertainties in a region of interest. Minimizing marginal uncertainties simultaneously accounts for quantitative prior model uncertainties while honoring a qualitative focus on particular regions of interest. The benefits of the proposed method over traditional non-Bayesian ones are demonstrated with several geophysical examples. These include reducing large seismic data volumes for real-time imaging and solving the problem of designing seismic surveys that account for source bandwidth, signal-to-noise ratio, and attenuation.

scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

Imaging passive seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. SI177-SI187 ◽  
Author(s):  
Brad Artman
Keyword(s):  
Water Table ◽  
Seismic Data ◽  
Survey Design ◽  
Signal To Noise Ratio ◽  
Multiple Sources ◽  
Depth Migration ◽  
Reflection Seismic ◽  
Ambient Sound ◽  
Passive Seismic ◽  
Passive Data

Imaging passive seismic data is the process of synthesizing the wealth of subsurface information available from reflection seismic experiments by recording ambient sound using an array of geophones distributed at the surface. Crosscorrelating the traces of such a passive experiment can synthesize data that are identical to actively collected reflection seismic data. With a correlation-based imaging condition, wave-equation shot-profile depth migration can use raw transmission wavefields as input for producing a subsurface image. Migration is even more important for passively acquired data than for active data because with passive data, the source wavefields are likely to be weak compared with background and instrument noise — a condition that leads to a low signal-to-noise ratio. Fourier analysis of correlating long field records shows that aliasing of the wavefields from distinct shots is unavoidable. Although this reduces the order of computations for correlation by the length of the original trace, the aliasing produces an output volume that may not be substantially more useful than the raw data because of the introduction of crosstalk between multiple sources. Direct migration of raw field data still can produce an accurate image, even when the transmission wavefields from individual sources are not separated. To illustrate direct migration, I use images from a shallow passive seismic investigation targeting a buried hollow pipe and the water-table reflection. These images show a strong anomaly at the 1-m depth of the pipe and faint events that could be the water table at a depth of around [Formula: see text]. The images are not clear enough to be irrefutable. I identify deficiencies in survey design and execution to aid future efforts.

2.5D multifocusing imaging of crooked-line seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Hossein Jodeiri Akbari Fam ◽  
Mostafa Naghizadeh ◽  
Oz Yilmaz
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Line Geometry ◽  
Data Set ◽  
Seismic Surveys ◽  
Seismic Image ◽  
New Formulation ◽  
Optimization Search

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

Full-waveform inversion of intensity-focused seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. R77-R97
Author(s):  
Miguel Bosch ◽  
Solange Mijares ◽  
Roxana Panchano ◽  
Francis Peña
Keyword(s):  
Seismic Data ◽  
Superposition Principle ◽  
Waveform Inversion ◽  
Seismic Survey ◽  
Model Parameters ◽  
Target Area ◽  
Problem Solution ◽  
Full Waveform Inversion ◽  
Target Zone ◽  
Full Waveform

Full-waveform inversion (FWI) is a promising tool for the comprehensive analysis of seismic data because it involves modeling the complete elastodynamic phenomena in 3D for the estimation of medium parameters. However, the application for reservoir characterization is still limited in achieving the inverse problem solution using reasonable computational resources and the required model resolution. Following a data-driven approach, we have implemented the isotropic elastic FWI with sensitivity emphasis on a prescribed target zone of the propagation volume, showing that the method is useful in improving the estimation over the target zone compared with nonlocalized methods. Our method uses the superposition principle to construct multipoint sources that focus the primary wave intensity (seismic illumination) over the specific target area. Thus, the secondary wavefield scattered from the target area to the receivers increases, providing improved information on the model parameters that control the property heterogeneity within the target zone. We design two types of focused macrosource (FMS) configurations that accomplish this objective: the convergent FMS and the combined beam FMS. Although multipoint seismic sources are not implemented in the field for practical reasons, they can be synthetized computationally using the superposition principle, interpolation, and the current seismic survey data (point-source) to compose the FMS observed gathers, which are then used as input data for the FWI. We perform elastic FWI synthetic tests in a 2D modified Marmousi model to compare the inversion performance with focused and nonfocused data, under the same computational conditions, with and without the presence of noise. Our tests show faster convergence and improved estimation in the target zone with the use of the intensity-focused seismic data for FWI; improved focusing effects are expected in 3D. Being target-oriented, the method is suitable for reducing computational requirements in elastic FWI application for reservoir description.

Seismic Soundoff

2020 ◽  
Vol 39 (4) ◽  
pp. 296-296
Author(s):  
Andrew Geary
Keyword(s):  
Survey Design ◽  
New Technology ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Short Course ◽  
Seismic Acquisition

The following is an excerpt from SEG's podcast, Seismic Soundoff. In this episode, host Andrew Geary previews Dave Monk's upcoming Distinguished Instructor Short Course and book titled, “Survey design and seismic acquisition for land, marine, and in-between in light of new technology and techniques.” In this engaging conversation, Dave and Andrew discuss how full-waveform inversion impacts survey design, the research breakthroughs needed for the next evolution of seismic surveys, and one group that may not realize that this course is for them. Listen to the full episode at https://seg.org/podcast/post/8946 .

Multiparameter estimation with acoustic vertical transverse isotropic full-waveform inversion of surface seismic data

2016 ◽  
Vol 4 (4) ◽  
pp. SU1-SU16 ◽  
Author(s):  
Xin Cheng ◽  
Kun Jiao ◽  
Dong Sun ◽  
Denes Vigh
Keyword(s):  
Vertical Velocity ◽  
Seismic Data ◽  
Joint Inversion ◽  
Waveform Inversion ◽  
Real Data ◽  
Vertical Axis ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Wavefield Simulation

Obtaining accurate depth-migrated images demands an anisotropic representation of the earth. As a prominent tool for building high-resolution earth models, full-waveform inversion (FWI) therefore must not only account for anisotropy during wavefield simulation but also reconstruct the anisotropy fields. We have developed an inversion strategy to perform acoustic multiparameter FWI of surface seismic data in transversely isotropic media with a vertical axis of symmetry (VTI). During the early era of FWI practice, most studies only invert for the most dominant parameter, that is, the vertical velocity, and the rest of the model parameters are either ignored or kept constant. Recently, more and more emphases focus on inverting for more parameters, such as for the vertical velocity and the anisotropy fields; these are referred to as multiparameter inversion. Due to the dominant influence of the vertical velocity on the kinematics of surface seismic data, we have developed a hierarchical approach to invert for the vertical velocity first, but we kept the anisotropy fields unchanged and only switched to joint inversion of the vertical velocity and the anisotropy fields when the inversion for the vertical velocity approaches convergence. In addition, we have illustrated the necessity of incorporating the diving and reflection energy during inversion to mitigate the nonuniqueness of the solutions caused by the coupling between the vertical velocity and the anisotropy fields. We also demonstrate the success of our method for VTI FWI using synthetic and real data examples based on marine surface seismic acquisition. Our results show that incorporation of multiparameter anisotropy inversion produced better focused migration images.

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.

scholarly journals Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

2021 ◽  
Author(s):  
Ehsan Jamali Hondori ◽  
Chen Guo ◽  
Hitoshi Mikada ◽  
Jin-Oh Park
Keyword(s):  
Seismic Data ◽  
Initial Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Control Measures ◽  
Japan Trench ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Time Migration ◽  
Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

A new parameter set for anisotropic multiparameter full-waveform inversion and application to a North Sea data set

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. U25-U38 ◽  
Author(s):  
Nuno V. da Silva ◽  
Andrew Ratcliffe ◽  
Vetle Vinje ◽  
Graham Conroy
Keyword(s):  
North Sea ◽  
Waveform Inversion ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Radiation Patterns ◽  
Data Set ◽  
Full Waveform ◽  
Seismic Image ◽  
Central North Sea

Parameterization lies at the center of anisotropic full-waveform inversion (FWI) with multiparameter updates. This is because FWI aims to update the long and short wavelengths of the perturbations. Thus, it is important that the parameterization accommodates this. Recently, there has been an intensive effort to determine the optimal parameterization, centering the fundamental discussion mainly on the analysis of radiation patterns for each one of these parameterizations, and aiming to determine which is best suited for multiparameter inversion. We have developed a new parameterization in the scope of FWI, based on the concept of kinematically equivalent media, as originally proposed in other areas of seismic data analysis. Our analysis is also based on radiation patterns, as well as the relation between the perturbation of this set of parameters and perturbation in traveltime. The radiation pattern reveals that this parameterization combines some of the characteristics of parameterizations with one velocity and two Thomsen’s parameters and parameterizations using two velocities and one Thomsen’s parameter. The study of perturbation of traveltime with perturbation of model parameters shows that the new parameterization is less ambiguous when relating these quantities in comparison with other more commonly used parameterizations. We have concluded that our new parameterization is well-suited for inverting diving waves, which are of paramount importance to carry out practical FWI successfully. We have demonstrated that the new parameterization produces good inversion results with synthetic and real data examples. In the latter case of the real data example from the Central North Sea, the inverted models show good agreement with the geologic structures, leading to an improvement of the seismic image and flatness of the common image gathers.

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