Refinements to the linear velocity inversion theory

Geophysics ◽  
1984 ◽  
Vol 49 (2) ◽  
pp. 112-118 ◽  
Author(s):  
Frank G. Hagin ◽  
Jack K. Cohen
Keyword(s):  
Synthetic Data ◽  
Scattered Wave ◽  
Scattering Data ◽  
Inversion Method ◽  
Impedance Change ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Velocity Inversion ◽  
Linear Algorithm ◽  
Inversion Theory

The linear inversion method presented by Cohen and Bleistein in 1979 gives seriously degraded results when large reflectors are encountered. Obviously there is an irrecoverable loss of information when such a linear algorithm is applied to a nonlinear world. However, in many cases, excellent results can be achieved by suitable postprocessing of the output of the basic linear inversion algorithm. Although a certain degree of helpful postprocessing can be and has been performed by straightforward consideration of the linearization process, we present here a substantially improved postprocessing algorithm. The basis for these improvements is a more accurate scattering model due to Lahlou et al where, among other things, a WKB analysis of the wave equation led to a much more accurate accounting of the geometric spreading of the scattered wave. These notions plus an effective use of traveltime are used in the new algorithm to improve both the estimate of the reflector locations and the estimate of amplitude (velocity or acoustic impedance) change across the reflectors. The basic idea is to insert this idealized scattering data into the original linear algorithm, and then use the result of this computation as a guide in the interpretation of the numerical output of the algorithm. We demonstrate the result of computer implementation of this algorithm on synthetic data, with and without noise, and verify that the postprocessing algorithm produces dramatically improved reflector locations and speed estimates. Moreover, the new algorithm adds only very modest cost to the basic processing, which is, in turn, very competitive in cost to other multidimensional algorithms.

2-D and 3-D resistivity image reconstruction using crosshole data

Geophysics ◽  
1992 ◽  
Vol 57 (10) ◽  
pp. 1270-1281 ◽  
Author(s):  
Hiromasa Shima
Keyword(s):  
Field Test ◽  
Numerical Models ◽  
Electrical Potential ◽  
Nonlinear Least Squares ◽  
Three Dimensions ◽  
Inversion Method ◽  
Good Resolution ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Resistivity Model

Theoretical changes in the distribution of electrical potential near subsurface resistivity anomalies have been studied using two resistivity models. The results suggest that the greatest response from such anomalies can be observed with buried electrodes, and that the resistivity model of a volume between boreholes can be accurately reconstructed by using crosshole data. The distributive properties of crosshole electrical potential data obtained by the pole‐pole array method have also been examined using the calculated partial derivative of the observed apparent resistivity with respect to a small cell within a given volume. The results show that for optimum two‐dimensional (2-D) and three‐dimensional (3-D) target imaging, in‐line data and crossline data should be combined, and an area outside the zone of exploration should be included in the analysis. In this paper, the 2-D and 3-D resistivity images presented are reconstructed from crosshole data by the combination of two inversion algorithms. The first algorithm uses the alpha center method for forward modeling and reconstructs a resistivity model by a nonlinear least‐squares inversion. Alpha centers express a continuously varying resistivity model, and the distribution of the electrical potential from the model can be calculated quickly. An initial general model is determined by the resistivity backprojection technique (RBPT) prior to the first inversion step. The second process uses finite elements and a linear inversion algorithm to improve the resolution of the resistivity model created by the first step. Simple 2-D and 3-D numerical models are discussed to illustrate the inversion method used in processing. Data from several field studies are also presented to demonstrate the capabilities of using crosshole resistivity exploration techniques. The numerical experiments show that by using the combined reconstruction algorithm, thin conductive layers can be imaged with good resolution for 2-D and 3-D cases. The integration of finite‐element computations is shown to improve the image obtained by the alpha center inversion process for 3-D applications. The first field test uses horizontal galleries to evaluate complex 2-D features of a zinc mine. The second field test illustrates the use of three boreholes at a dam site to investigate base rock features and define the distribution of an altered zone in three dimensions.

Turning‐ray tomography

Geophysics ◽  
1995 ◽  
Vol 60 (6) ◽  
pp. 1917-1929 ◽  
Author(s):  
Joseph P. Stefani
Keyword(s):  
Velocity Structure ◽  
Initial Point ◽  
Real Data ◽  
Point Sources ◽  
Surface Velocity ◽  
Inversion Algorithm ◽  
Low Velocity ◽  
Linear Inversion ◽  
Near Surface ◽  
Velocity Inversion

Turning‐ray tomography is useful for estimating near‐surface velocity structure in areas where conventional refraction statics techniques fail because of poor data or lack of smooth refractor/velocity structure. This paper explores the accuracy and inherent smoothing of turning‐ray tomography in its capacity to estimate absolute near‐surface velocity and the statics times derived from these velocities, and the fidelity with which wavefields collapse to point diffractors when migrated through these estimated velocities. The method comprises nonlinear iterations of forward ray tracing through triangular cells linear in slowness squared, coupled with the LSQR linear inversion algorithm. It is applied to two synthetic finite‐ difference data sets of types that usually foil conventional refraction statics techniques. These models represent a complex hard‐rock overthrust structure with a low‐velocity zone and pinchouts, and a contemporaneous near‐shore marine trench filled with low‐ velocity unconsolidated deposits exhibiting no seismically apparent internal structure. In both cases velocities are estimated accurately to a depth of one‐ fifth the maximum offset, as are the associated statics times. Of equal importance, the velocities are sufficiently accurate to correctly focus synthetic wavefields back to their initial point sources, so migration/datuming applications can also use these velocities. The method is applied to a real data example from the Timbalier Trench in the Gulf of Mexico, which exhibits the same essential features as the marine trench synthetic model. The Timbalier velocity inversion is geologically reasonable and yields long and short wavelength statics that improve the CMP gathers and stack and that correctly align reflections to known well markers. Turning‐ray tomography estimates near‐surface velocities accurately enough for the three purposes of lithology interpretation, statics calculations, and wavefield focusing for shallow migration and datuming.

Computational and asymptotic aspects of velocity inversion

Geophysics ◽  
1985 ◽  
Vol 50 (8) ◽  
pp. 1253-1265 ◽  
Author(s):  
Norman Bleistein ◽  
Jack K. Cohen ◽  
Frank G. Hagin
Keyword(s):  
Asymptotic Analysis ◽  
Synthetic Data ◽  
Small Variation ◽  
Computer Time ◽  
Velocity Variation ◽  
Inversion Method ◽  
Singular Function ◽  
Velocity Inversion ◽  
Dirac Delta ◽  
Peak Value

We discuss computational and asymptotic aspects of the Born inversion method and show how asymptotic analysis is exploited to reduce the number of integrations in an f-k like solution formula for the velocity variation. The output of this alternative algorithm produces the reflectivity function of the surface. This is an array of singular functions—Dirac delta functions which peak on the reflecting surfaces—each scaled by the normal reflection strength at the surface. Thus, imaging of a reflector is achieved by construction of its singular function and estimation of the reflection strength is deduced from the peak value of that function. By asymptotic analysis of the application of the algorithm to the Kirchhoff representation of the backscattered field, we show that the peak value of the output estimates the reflection strength even when the condition of small variation in velocity (an assumption of the original derivation) is violated. Furthermore, this analysis demonstrates that the method provides a migration algorithm when the amplitude has not been preserved in the data. The design of the computer algorithm is discussed, including such aspects as constraints due to causality and spatial aliasing. We also provide O‐estimates of computer time. This algorithm has been successfully implemented on both synthetic data and common‐midpoint stacked field data.

scholarly journals PEMODELAN INVERSI DATA MAGNETOTELLURIK 1-D MENGGUNAKAN METODA GENETIC ALGORITHM (GA) DENGAN POPULASI MICRO GENETIC ALGORITHM KASUS 3 LAYER DAN 5 LAYER

2019 ◽  
Vol 8 (2) ◽  
pp. 167-180
Author(s):  
Nia Maharani
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Selection Process ◽  
Synthetic Data ◽  
Inversion Method ◽  
Earth Model ◽  
Observation Data ◽  
Linear Inversion ◽  
Synthetic Model ◽  
Micro Genetic Algorithm

This paper discusses non-linear inversion method with Genetic Algorithm (GA) which inspired by natural selection process (survival for the fittest) and genetic using 20 populations (micro genetic algorithm). The method is applied to 1-D magnetotelluric inverted data with model parameter is resistivity as a function of depth. This research only uses synthetic data obtained from synthetic model. The model is homogeneous earth model with 3 and 5 layers. Perturbation of model is performed until minimum misfit between theoritical and observation data achieved. The 3 layers and 5 layers inversion processes are applied to 3 layers and 5 layers earth model respectively, with satisfactory results in other words it can reproduce the synthetic model.

Analysis of prior models for a blocky inversion of seismic AVA data

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. C25-C35 ◽  
Author(s):  
Ulrich Theune ◽  
Ingrid Østgård Jensås ◽  
Jo Eidsvik
Keyword(s):  
Synthetic Data ◽  
Iterative Solution ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Optimization Approach ◽  
Additional Parameter ◽  
Inversion Algorithm ◽  
Borehole Data ◽  
Solution Approach ◽  
Exploration And Production

Resolving thinner layers and focusing layer boundaries better in inverted seismic sections are important challenges in exploration and production seismology to better identify a potential drilling target. Many seismic inversion methods are based on a least-squares optimization approach that can intrinsically lead to unfocused transitions between adjacent layers. A Bayesian seismic amplitude variation with angle (AVA) inversion algorithm forms sharper boundaries between layers when enforcing sparseness in the vertical gradients of the inversion results. The underlying principle is similar to high-resolution processing algorithms and has been adapted from digital-image-sharpening algorithms. We have investigated the Cauchy and Laplace statistical distributions for their potential to improve contrasts betweenlayers. An inversion algorithm is derived statistically from Bayes’ theorem and results in a nonlinear problem that requires an iterative solution approach. Bayesian inversions require knowledge of certain statistical properties of the model we want to invert for. The blocky inversion method requires an additional parameter besides the usual properties for a multivariate covariance matrix, which we can estimate from borehole data. Tests on synthetic and field data show that the blocky inversion algorithm can detect and enhance layer boundaries in seismic inversions by effectively suppressing side lobes. The analysis of the synthetic data suggests that the Laplace constraint performs more reliably, whereas the Cauchy constraint may not find the optimum solution by converging to a local minimum of the cost function and thereby introducing some numerical artifacts.

Source signature determination from ministreamer data

Geophysics ◽  
1994 ◽  
Vol 59 (8) ◽  
pp. 1261-1269 ◽  
Author(s):  
Martin Landrø ◽  
Jan Langhammer ◽  
Roger Sollie ◽  
Losse Amundsen ◽  
Eivind Berg
Keyword(s):  
Point Sources ◽  
Inversion Method ◽  
Far Field ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Linear Inversion ◽  
Air Bubble ◽  
Air Gun ◽  
Error Energy ◽  
Effective Source

Two methods for estimating the pressure wavefield generated by a marine airp‐gun array are tested. Data have been acquired at a ministreamer located below the source array. Effective source signatures for each air gun are estimated. In the first method a nonlinear inversion algorithm is used, where the forward modeling scheme is based upon a physical modeling of the air bubble generated by each air gun. In the second method a linear inversion method is used, with the assumption that the physics in the problem can be described by the acoustic wave equation with explosive point sources as the driving term. From the estimated effective source signatures, far‐field signatures have been calculated for both methods and compared with measured far‐field signatures. The error energy between the measured and estimated far‐field signatures was approximately 8 percent for both methods.

Free-surface multiple attenuation for blended data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V227-V233
Author(s):  
Jitao Ma ◽  
Xiaohong Chen ◽  
Mrinal K. Sen ◽  
Yaru Xue
Keyword(s):  
Free Surface ◽  
Seismic Data ◽  
Synthetic Data ◽  
Primary Energy ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Crosstalk Noise ◽  
Multiple Attenuation ◽  
Blended Data ◽  
Value Decomposition

Blended data sets are now being acquired because of improved efficiency and reduction in cost compared with conventional seismic data acquisition. We have developed two methods for blended data free-surface multiple attenuation. The first method is based on an extension of surface-related multiple elimination (SRME) theory, in which free-surface multiples of the blended data can be predicted by a multidimensional convolution of the seismic data with the inverse of the blending operator. A least-squares inversion method is used, which indicates that crosstalk noise existed in the prediction result due to the approximate inversion. An adaptive subtraction procedure similar to that used in conventional SRME is then applied to obtain the blended primary — this can damage the energy of primaries. The second method is based on inverse data processing (IDP) theory adapted to blended data. We derived a formula similar to that used in conventional IDP, and we attenuated free-surface multiples by simple muting of the focused points in the inverse data space (IDS). The location of the focused points in the IDS for blended data, which can be calculated, is also related to the blending operator. We chose a singular value decomposition-based inversion algorithm to stabilize the inversion in the IDP method. The advantage of IDP compared with SRME is that, it does not have crosstalk noise and is able to better preserve the primary energy. The outputs of our methods are all blended primaries, and they can be further processed using blended data-based algorithms. Synthetic data examples show that the SRME and IDP algorithms for blended data are successful in attenuating free-surface multiples.

A simultaneous inversion for background velocity and impedance maps

Geophysics ◽  
1990 ◽  
Vol 55 (4) ◽  
pp. 458-469 ◽  
Author(s):  
D. Cao ◽  
W. B. Beydoun ◽  
S. C. Singh ◽  
A. Tarantola
Keyword(s):  
Traditional Approach ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Seismic Velocities ◽  
Inversion Algorithm ◽  
Seismic Reflection Data ◽  
Simultaneous Inversion ◽  
Highly Nonlinear

Full‐waveform inversion of seismic reflection data is highly nonlinear because of the irregular form of the function measuring the misfit between the observed and the synthetic data. Since the nonlinearity results mainly from the parameters describing seismic velocities, an alternative to the full nonlinear inversion is to have an inversion method which remains nonlinear with respect to velocities but linear with respect to impedance contrasts. The traditional approach is to decouple the nonlinear and linear parts by first estimating the background velocity from traveltimes, using either traveltime inversion or velocity analysis, and then estimating impedance contrasts from waveforms, using either waveform inversion or conventional migration. A more sophisticated strategy is to obtain both the subsurface background velocities and impedance contrasts simultaneously by using a single least‐squares norm waveform‐fit criterion. The background velocity that adequately represents the gross features of the medium is parameterized using a sparse grid, whereas the impedance contrasts use a dense grid. For each updated velocity model, the impedance contrasts are computed using a linearized inversion algorithm. For a 1-D velocity background, it is very efficient to perform inversion in the f-k domain by using the WKBJ and Born approximations. The method performs well both with synthetic and field data.

scholarly journals Application of an Improved Ultrasound Full-Waveform Inversion in Bone Quantitative Measurement

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 260
Author(s):  
Meng Suo ◽  
Dong Zhang ◽  
Yan Yang
Keyword(s):  
Nonlinear Equations ◽  
Waveform Inversion ◽  
Optimal Solution ◽  
Inversion Method ◽  
Full Waveform Inversion ◽  
Inversion Algorithm ◽  
Elastic Wave Equation ◽  
Full Waveform ◽  
Ultrasonic Propagation ◽  
Joint Velocity

Inspired by the large number of applications for symmetric nonlinear equations, an improved full waveform inversion algorithm is proposed in this paper in order to quantitatively measure the bone density and realize the early diagnosis of osteoporosis. The isotropic elastic wave equation is used to simulate ultrasonic propagation between bone and soft tissue, and the Gauss–Newton algorithm based on symmetric nonlinear equations is applied to solve the optimal solution in the inversion. In addition, the authors use several strategies including the frequency-grid multiscale method, the envelope inversion and the new joint velocity–density inversion to improve the result of conventional full-waveform inversion method. The effects of various inversion settings are also tested to find a balanced way of keeping good accuracy and high computational efficiency. Numerical inversion experiments showed that the improved full waveform inversion (FWI) method proposed in this paper shows superior inversion results as it can detect small velocity–density changes in bones, and the relative error of the numerical model is within 10%. This method can also avoid interference from small amounts of noise and satisfy the high precision requirements for quantitative ultrasound measurements of bone.

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