Seismic Wavelet Research Articles

The Influence of Interference Layer on the Prediction of Seismic Information of Channel Sedimentary Reservoir

Channel sedimentary reservoirs are the main oil and gas accumulation sites at home and abroad. With the deepening of oil and gas exploration, channel-related oil and gas reservoirs with obvious seismic characteristics have been mostly discovered, and the rest are basically oil and gas reservoirs with unobvious seismic characteristics and difficult to be discovered. The difficulty and risk of discovering these oil and gas reservoirs are on the increase. In order to reduce the exploration risk, it is necessary to form a more reliable time window control theory with a better seismic interpretation technique for channel reservoir reliability. In this paper, 2D forward modeling was used to study the influence of the upper and lower interference layers on the imaging of channel reservoir. When the lithological composition is relatively stable, the degree of interference of the interference layer is closely related to the seismic wavelet, and it is especially susceptible to the main frequency of the seismic wavelet. The lower the main frequency of seismic wavelet, the greater the degree of interference of interference layers on the imaging of channel reservoir, and vice versa. Further analysis on the characteristics of the Ricker wavelet was made and showed that the influence of the upper and lower interference layers on the imaging information of the channel reservoir is concentrated in half apparent major period. Then, considering the dynamic change of distance between the interference layers and the top and bottom interfaces of channel reservoir, the influence of interference layers on the imaging of channel reservoir was studied through 1D forward modeling. The interference area was divided into maximum interference area and main interference area depending on the degree of influence. On this basis, the time window control theory of channel reservoir prediction was clarified. The seismic information which reflects the channel reservoir reliably can be obtained by avoiding the maximum interference area, especially the main interference area.

Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with increasing medium velocity and compresses with decreasing medium velocity. The commonly used convolutional model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We have developed a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well-log data. Our method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolutional model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. In addition, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares (LS)-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with LS-based methods. The results indicate that our method is effective and stable even for data with a low signal-to-noise ratio.

Super-Resolution Optimal Basic Wavelet Transform and Its Application in Thin-Bed Thickness Characterization

Continuous wavelet transform (CWT) is often used to extract the peak frequency attribute for characterizing the thin-bed thickness. Good joint time-frequency (TF) resolution is beneficial for the extraction of peak frequency. However, due to the Heisenberg’s uncertain principle, the time and frequency resolution of CWT cannot be obtained simultaneously. In this paper, combining the adaptive superlet transform and the optimal basic wavelet, a super-resolution optimal basic wavelet transform (SROBWT) is proposed to obtain the best joint TF resolution. The optimal basic wavelet matching the seismic wavelet is constructed as a basic wavelet of the adaptive superlet transform. Herein, taking the best joint TF resolution of the seismic wavelet as the target, a parameter selection method is proposed for the adaptive superlet transform. Furthermore, based on the proposed SROBWT and wedge model, a workflow is proposed to characterize the thin-bed thickness. The synthetic and field seismic data are employed to demonstrate the validity of the proposed methods. All the corresponding results show that the SROBWT has a better joint TF resolution than the conventional methods and the proposed workflow can correctly characterize the spatial variation of the thin-bed thickness, which is beneficial for further sediment sources analysis and reservoir prediction.

Seismic Acoustic Impedance Inversion via Optimization-Inspired Semisupervised Deep Learning

Seismic acoustic impedance inversion (SAII) aims at recovering the subsurface impedance to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great challenge to find an optimal solution. Regularization is an effective method to solve SAII by imposing prior information, but it suffers from high computational complexity and limited inversion performance. To mitigate the above limitations, we propose an optimization-inspired semisupervised deep learning SAII approach that incorporates the advantages between the model-driven optimization algorithm and the data-driven deep learning method. Specifically, it is implemented by parameterizing the alternating iterative method (AIM) by splitting it into two parts where the convolutional neural networks are adopted to learn the regularization terms and a nonlinear mapping and thus called the proposed network as AIM-SAIINet. The proposed method can not only simultaneously invert the seismic wavelet and impedance but also obtain high-resolution data as an intermediate product to facilitate the training of AIM-SAIINet and enhance the inversion accuracy. In addition, we introduce a joint semisupervised training scheme in which the network is first jointly pretrained in a supervised manner using the synthetic training data to provide good initial values, and then, a semisupervised training scheme is adopted to fine-tune it using few labeled data pairs to achieve high inversion accuracy. The synthetic and field data examples are conducted to validate the effectiveness of AIM-SAIINet, which achieves higher inversion accuracy at a fast computational speed compared with the traditional methods.

Data-Driven Time-Frequency Method and Its Application in Detection of Free Gas Beneath a Gas Hydrate Deposit

The time-frequency (TF) analysis method plays a significant role in the detection of natural gas hydrates. As a data-driven method, compressed sensing (CS) has been widely used in the TF methods due to the sparsity of the TF representation. This study proposes a data-driven TF method based on the CS theory and a non-convex regularization. In the implementation, a continuous wavelet transform (CWT) with a generalized beta wavelet (GBW) is formulated as an inverse problem based on the CS theory. The selection of appropriate parameters enables the GBW to match the seismic wavelets better than the widely used Morlet wavelet. The GBW can constitute a tight frame to reduce calculation time, particularly for large-scale field data processing. Additionally, the proposed TF method introduces the generalized minimax concave (GMC) penalty function as a non-convex regularization term. Compared with the classical sparse approximation method with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1} $ </tex-math></inline-formula> regularization, the GMC regularization term can enhance the sparsity in sparse inverse problems and ensure the convexity of sparse inversions. This article also presents an exponentially decreasing threshold scheme to adaptively select the regularization parameters. Three synthetic examples are investigated to demonstrate the performance of the proposed sparse TF representation with GMC regularization. Finally, the proposed TF method’s performance in detecting free gas of gas hydrates is validated using field seismic data obtained from the Blake Ridge.

A Multitask Learning-Based Dynamic Wavelet Amplitude Spectra Extraction Method and Its Application in Q Estimation

Dynamic wavelet amplitude spectra extraction (DWASE), which is an ill-posed problem, is of great importance for nonstationary seismic data processing. The most difficult challenge is how to decouple the dynamic wavelets and reflection coefficients. The traditional DWASE methods solve the ill-posed problem depending on some prior information, such as the piecewise stationary hypothesis or estimation of the attenuation factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> . In this article, we propose a multitask learning-based DWASE method and apply the method for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> estimation. Our proposed method can reduce the multiplicity of the ill-posed problem by estimating the logarithmic time–frequency amplitude spectrum (logarithmic TFAS) of both reflection coefficients and dynamic seismic wavelets, simultaneously. In our method, a parameter-sharing U-net is used to extract the logarithmic TFAS of the reflection coefficients and dynamic wavelets from the logarithmic TFAS of the nonstationary seismic data. To verify the accuracy of the DWASE results of our method, we make a quantitative analysis of the synthetic seismic data, which are obtained by our method and some traditional methods. We also apply the DWASE results of our method for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> estimation and attenuation compensation in both synthetic and field seismic data, to prove the effectiveness of the method. Also, comparisons with some traditional methods are given.

Prediction Technique of Thin Sandstone Reservoir Affected by Dual Coal Seam

The reservoir thickness of sub-member of Lower Permain in the south of the eastern margin of Ordos Basin is thin and large variable i laterally. And the seismic response characteristics of sandstone reservoir are doubly interfered by the strong reflection sidelobes of overlying 5# coal seam and underlying 8# coal seam. In view of this situation, in order to better eliminate the influence of upper and lower coal seams and improve the accuracy of reservoir prediction, the strategy of de-strong seismic reflection and de-logging (curve) interference is adopted to carry out the matching of logging and seismic, and on this basis, uses the seismic waveform indication inversion to predict the thin sandstone. The steps are as follows: firstly, by using wavelet decomposition and reconstruction technology, select the seismic wavelet component that can better characterize the reservoir or oil & gas to reasonablely reconstruct seismic data, weaken the influence of the strong reflection from overlying 5# coal seam and underlying 8# coal seam, and highlight the response of weak seismic reflection of tight sandstone in Shan 23 sub-member; Then, the acoustic value of coal seam is corrected to that of mudstone on the logging curve to maintain the consistency of logseismic data. This method realizes the effective identification of sandstone reservoir in Shan 23 sub-member, and greatly improves the target-penetrated rate and sandstone-penetrated rate of horizontal wells.

Seismic Attenuation Estimation via Unscaled Time–Frequency Representation and Divergence

Time-frequency (TF) representations achieve better TF localization properties compared with Fourier transform (FT) and perform well for Q estimation via methods such as spectral ratio (SR), centroid frequency shift (CFS), and peak frequency shift (PFS). However, these attenuation estimation methods all require a given frequency band or certain source wavelet assumptions, also heavily interfered by noise. In addition, the resolution and accuracy of TF spectrum also determine whether the extracted spectrum can accurately characterize the frequency properties of seismic wavelets, thus affecting the accuracy of seismic estimation results. In this study, we propose an unscaled generalized S-transform (UGST), which achieves better TF localization while avoiding the dominant frequency shift of S-transform (ST). Next, we build a Q estimation method based on the weighted Kullback-Leibler (WKL) divergence and then give an estimation workflow combined with the proposed UGST. Note that the proposed workflow does not need to make specific wavelet assumptions or consider the frequency band selection, which is also proved to be not sensitive to noise. Finally, to demonstrate the effectiveness of the proposed workflow, we apply it to synthetic and field data. Compared with the contrastive methods, our proposed workflow can achieve more accurate estimation results and show better noise immunity, which can benefit delineating seismic hydrocarbon reservoirs.

Transient Electromagnetic Machine Learning Inversion Based on Pseudo Wave Field Data

Machine learning inversion (ML-inversion) has been widely used in the interpretation of geophysical data. However, because the transient electromagnetic (TEM) is not sensitive to the response of the small-size or large-depth abnormal body, there are some problems such as poor imaging accuracy and low-resolution when directly processing raw TEM data with ML method. To solve this problem, we propose to use the pseudo seismic wavelet (PSW) data obtained from TEM wave field transformation to perform TEM ML-inversion. Simulation and example application verify that compared with the TEM data, the PSW data has higher recognition sensitivity to the electrical changes of underground anomalies, and the ML-inversion based on the PSW data can significantly improve the TEM imaging accuracy. Our study demonstrates that the PSW data can be used to achieve TEM ML-inversion, which may provide a new way for the further combination of electromagnetic data processing and ML technology.

Seismic Resolution Enhancement by Spectral Shaping Using Shaping-Regularized Inversion

Seismic deconvolution aims to improve the vertical seismic resolution by compressing the seismic wavelet and extending the seismic frequency bandwidth. Deconvolution in the frequency domain is normally implemented in three steps: wavelet estimation, deconvolution operator construction, and data filtering. We propose a seismic resolution enhancement approach by spectral shaping using shaping-regularized inversion. Instead of designing a deconvolution operator based on the extracted wavelet, we formulate an inverse problem using the seismic spectrum as the diagonal kernel matrix and a predefined filter as the expected spectrum. Assuming the randomness in the reflectivity and smoothness of the spectral shaping operator, we estimate the spectral shaping operator by inversion via a shaping regularization scheme, which imposes constraints by shaping the estimated spectral shaping operator model to the admissible model space. The role of the shaping regularization in inversion is to ensure the continuity and smoothness of the spectral shaping operator. We use a synthetic model and real land seismic data to demonstrate the effectiveness of the proposed approach in seismic resolution enhancement.

Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery

Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, the low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we have investigated a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudowell impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. The broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. Our approach is fully data driven and does not involve the initial model, seismic wavelet, and model-driven operator. Tests on the Marmousi model illustrate that our double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and we determine that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set indicate that the predicted impedance results not only reveal a classic tectonic sedimentation history but also match the corresponding results measured at the locations of two wells.

First-order multiples imaging aided by water bottom

Surface-related multiples frequently propagate into the subsurface and contain abundant information on small reflection angles. Compared with the conventional migration of primaries, migration of multiples offers complementary illumination and a higher vertical resolution. However, crosstalk artifacts caused by unrelated multiples during reverse time migration (RTM) using multiples severely degrade the reliability and interpretation of the final migration images. Therefore, we proposed RTM using first-order receiver-side water-bottom-related multiples for eliminating crosstalk artifacts and enhancing vertical resolution. We first backward propagate the first-order receiver-side water-bottom-related multiples using a water-layer model, followed by saving the upper boundary wavefield. Then we produce the source wavefield using a seismic wavelet and the receiver wavefield by back-extrapolating the saved boundary. Finally, the cross-correlation imaging condition is applied to generate the final image. This method transforms the receiver-side multiples into primaries, followed by the conventional migration processing procedures. Numerical examples using synthetic datasets demonstrate that our method significantly enhances the imaging quality by eliminating crosstalk artifacts and improving the resolution.

Sparse inversion-based seismic random noise attenuation via self-paced learning

Seismic random noise reduction is an important task in seismic data processing at the Chinese loess plateau area, which benefits the geologic structure interpretation and further reservoir prediction. The sparse inversion is one of the widely used tools for seismic random noise reduction, which is often solved via the sparse approximation with a regularization term. The ℓ1 norm and total variation (TV) regularization term are two commonly used regularization terms. However, the ℓ1 norm is only a relaxation of the ℓ0 norm, which cannot always provide a sparse result. The TV regularization term may provide unexpected staircase artifacts. To avoid these disadvantages, we proposed a workflow for seismic random noise reduction by using the self-paced learning (SPL) scheme and a sparse representation (i.e. the continuous wavelet transform, CWT) with a mixed norm regularization, which includes the ℓp norm and the TV regularization. In the implementation, the SPL, which is inspired by human cognitive learning, is introduced to avoid the bad minima of the non-convex cost function. The SPL can first select the high signal-to-noise ratio (SNR) seismic data and then gradually select the low SNR seismic data into the proposed workflow. Moreover, the generalized Beta wavelet (GBW) is adopted as the basic wavelet of the CWT to better match for seismic wavelets and then obtain a more localized time-frequency (TF) representation. It should be noted that the GBW can easily constitute a tight frame, which saves the calculation time. Synthetic and field data examples are adopted to demonstrate the effectiveness of the proposed workflow for effectively suppressing seismic random noises and accurately preserving valid seismic reflections.

Multichannel adaptive deconvolution based on streaming prediction-error filter

Abstract Deconvolution mainly improves the resolution of seismic data by compressing seismic wavelets, which is of great significance in high-resolution processing of seismic data. Prediction-error filtering/least-square inverse filtering is widely used in seismic deconvolution and usually assumes that seismic data is stationary. Affected by factors such as earth filtering, actual seismic wavelets are time- and space-varying. Adaptive prediction-error filters are designed to effectively characterise the nonstationarity of seismic data by using iterative methods, however, it leads to problems such as slow calculation speed and high memory cost when dealing with large-scale data. We have proposed an adaptive deconvolution method based on a streaming prediction-error filter. Instead of using slow iterations, mathematical underdetermined problems with the new local smoothness constraints are analytically solved to predict time-varying seismic wavelets. To avoid the discontinuity of deconvolution results along the space axis, both time and space constraints are used to implement multichannel adaptive deconvolution. Meanwhile, we define the parameter of the time-varying prediction step that keeps the relative amplitude relationship among different reflections. The new deconvolution improves the resolution along the time direction while reducing the computational costs by a streaming computation, which is suitable for handling nonstationary large-scale data. Synthetic model and field data tests show that the proposed method can effectively improve the resolution of nonstationary seismic data, while maintaining the lateral continuity of seismic events. Furthermore, the relative amplitude relationship of different reflections is reasonably preserved.

Examination of the Seismic Characteristics of Onshore Reservoirs for Dom Gas Production Potentials in the Niger Delta Region of Nigeria

The objective of this research work is to examine two prominent dom gas reservoirs H1000 and H4000 in onshore Niger Delta region of Nigeria for potential production activities using seismic wavelet and well-to-seismic tie process to facilitate interpretation and evaluate dom gas (hydrocarbon) bearing formation. Well log and sidewall samples analyses show that the velocity attributes, high P-wave, S-wave and Vp/Vs ratio characterize sediments with high concentration of dom gas. The wells validated the presence of dom gas with total gas in place on the field estimated at 4.017 trillion ft3, GIIP 5,228.683 bscf, CIIP 445.136 MMstb, and STOIIP 34.647 MMstb, URg 4,120.494 bscf and URc 219.208 MMstbl, 90 % of which resides in the two reservoirs. The hydrocarbon intervals are estimated at H1000 (8864 ft, TVDSS) and H4000 (9577 ft, TVDSS). The borehole information was combined with seismic data to confirm lateral continuity of the dom gas reservoirs.

Seismic estimation of fluid saturation based on rock physics: A case study of the tight-gas sandstone reservoirs in the Ordos Basin

Tight-gas sandstone reservoirs of the Ordos Basin in China are characterized by high rock-fragment content, dissimilar pore types, and a random distribution of fluids, leading to strong local heterogeneity. We model the seismic properties of these sandstones with the double-double porosity theory, which considers water saturation, porosity, and the frame characteristics. A generalized seismic wavelet is used to fit the real wavelet, and the peak frequency-shift method is combined with the generalized S-transform to estimate attenuation. Then, we establish rock-physics templates (RPTs) based on P-wave attenuation and impedance. We use the log data and related seismic traces to calibrate the RPTs and generate a 3D volume of rock-physics attributes for the quantitative prediction of saturation and porosity. The predicted values are in good agreement with the actual gas production reports, indicating that the method can be effectively applied to heterogeneous tight-gas sandstone reservoirs.

Simultaneously computing the 3D dip attributes for all the time samples of a seismic trace

Abstract The seismic dip attribute is regularly used to aid structural interpretation and is commonly adopted as a compulsory input for computing other seismic geometric attributes. One disadvantage of current dip computation algorithms is that interpreters compute the dip attribute time sample by time sample and do not consider the relationship between dip values of nearby samples. The classic convolution theory suggests one formation boundary should have the corresponding seismic event. However, the seismic wavelet always has a certain time duration. As a result, one formation boundary has a corresponding seismic event that consists of several time samples. Ideally, the time samples, which belong to the same boundary, should have approximately the same dip attributes. In this research, a sample by sample computation procedure is treated as an independent optimisation procedure. Then, simultaneously computing the seismic dip of time samples of one seismic trace can be regarded as a multi-objective optimisation procedure. The proposed method is based on analysing features of seismic waveform within user-defined windows. Considering that nearby time samples should have continuous dip values, we the dynamic time warping to simultaneously compute seismic reflectors’ dip values of a seismic trace. We applied our method to a field seismic data to demonstrate its effectiveness.

Poststack seismic inversion using a patch-based Gaussian mixture model

Seismic inversion is a severely ill-posed problem because of noise in the observed record, band-limited seismic wavelets, and the discretization of a continuous medium. Regularization techniques can impose certain characteristics on inversion results based on prior information to obtain a stable and unique solution. However, it is difficult to find an appropriate regularization to describe the actual subsurface geology. We have developed a new acoustic impedance inversion method via a patch-based Gaussian mixture model (GMM), which is designed using available well logs. In this method, first, the nonlocal means method estimates acoustic impedance around wells in terms of the similarity of local seismic records. The extrapolated multichannel impedance is then decomposed into impedance patches. Using patched data rather than a window or single trace for training samples to obtain the GMM parameters, which contain local lateral structural information, can provide more impedance structure details and enhance the stability of the inversion result. Next, the expectation maximization algorithm is used to obtain the GMM parameters from the patched data. Finally, we apply the alternating direction method of multipliers to solve the conventional Bayesian inference illustrating the role of regularization and construct the objective function using the GMM parameters. Therefore, the inversion results are compliant with the local structural features extracted from the borehole data. The synthetic and field data tests validate the performance of our method. Compared with other conventional inversion methods, our method shows promise in providing a more accurate and stable inversion result.

A new denoising strategy and the X-shaped supplement denoising operator targeting time-reversed mirror imaging technique

Abstract Imaging of vertical structures is a challenge in the seismic imaging field. The conventional imaging methods for vertical structures are highly dependent on the reference model or boreholes. Time-reversed mirror imaging can effectively image the vertical structures based on the multiples and a smoothed velocity model without the need of accurate seismic wavelet estimation. Although the Laplacian operator is applied in time-reversed mirror imaging, there still exists severe residual noise. In this study, we developed a new imaging denoising strategy and an X-shaped supplement denoising operator for time-reversed mirror imaging based on the geometric features of the image and the causes of imaging noise. Synthetic results for the single- and double-staircase model prove the powerful denoising capacity of the X-shaped supplement denoising operator. In addition, the results of a Marmousi model prove that the X-shaped denoising operator can also effectively suppress the noise when applying time-reversed mirror imaging method to image complex inclined structures. However, the X-shaped denoising operator still contains some limitations, such as non-amplitude-preserving.

Direct depth-domain Bayesian amplitude-variation-with-offset inversion

We have developed a new approach to perform Bayesian linearized amplitude-variation-with-offset (AVO) inversion in the depth domain using nonstationary wavelets. Bayesian linearized AVO inversion, a hybrid approach combining physics-based models with statistical learning, has gained immense popularity because of its superior computational speed and ability to estimate uncertainties in inverted model parameters. Bayesian linearized AVO inversion is performed on time-domain seismic data; therefore, depth-imaged seismic cannot be inverted directly using this method and would require depth-to-time conversion before AVO inversion can be done. Subsequently, time-to-depth conversion of the inverted volumes would be required for reservoir modeling and well placement. Domain conversions introduce additional uncertainty in geophysical workflows. In conventional AVO inversion, the seismic wavelet is assumed to be stationary, and this assumption leads to a restriction in the length of the time window over which the inversion can be performed. Therefore, AVO inversion is usually restricted to a narrow time window around the target of interest, and if multiple targets are present at different depths, multiple inversions must be run on the same volume. Depth-domain amplitude inversion is a recent development and has been previously presented in an iterative formulation. Implementing linearized Bayesian inversion directly in the depth domain using nonstationary wavelets is a convenient new approach that takes advantage of superior computational speed and uncertainty quantification without compromising the accurate spatial location that depth imaging provides. Combining these two ideas creates a novel, unique, and powerful seismic inversion technique that can be useful for quantitative interpretation and reservoir characterization.