Structurally constrained impedance inversion

2016 ◽  
Vol 4 (4) ◽  
pp. T577-T589 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky
Keyword(s):  
Objective Function ◽  
Seismic Data ◽  
Field Data ◽  
A Priori ◽  
Structural Constraints ◽  
Inversion Algorithm ◽  
Data Set ◽  
Impedance Inversion ◽  
Single Trace ◽  
Complex Geology

In complex geology, the presence of highly dipping structures can complicate impedance inversion. We have developed a structurally constrained inversion in which a computationally well-behaved objective function is minimized subject to structural constraints. This approach allows the objective function to incorporate structural orientation in the form of dips into our inversion algorithm. Our method involves a multitrace impedance inversion and a rotation of an orthogonal system of derivative operators. Local dips used to constrain the derivative operators were estimated from migrated seismic data. In addition to imposing structural constraints on the inversion model, this algorithm allows for the inclusion of a priori knowledge from boreholes. We investigated this algorithm on a complex synthetic 2D model as well as a seismic field data set. We compared the result obtained with this approach with the results from single trace-based inversion and laterally constrained inversion. The inversion carried out using dip information produces a model that has higher resolution that is more geologically realistic compared with other methods.

scholarly journals Waveform inversion using a logarithmic wavefield

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R31-R42 ◽  
Author(s):  
Changsoo Shin ◽  
Dong-Joo Min
Keyword(s):  
Objective Function ◽  
Field Data ◽  
Velocity Structure ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Velocity Model ◽  
Matrix Formalism ◽  
Inversion Algorithm ◽  
Data Set ◽  
Short Period

Although waveform inversion has been studied extensively since its beginning [Formula: see text] ago, applications to seismic field data have been limited, and most of those applications have been for global-seismology- or engineering-seismology-scale problems, not for exploration-scale data. As an alternative to classical waveform inversion, we propose the use of a new, objective function constructed by taking the logarithm of wavefields, allowing consideration of three types of objective function, namely, amplitude only, phase only, or both. In our wave form inversion, we estimate the source signature as well as the velocity structure by including functions of amplitudes and phases of the source signature in the objective function. We compute the steepest-descent directions by using a matrix formalism derived from a frequency-domain, finite-element/finite-difference modeling technique. Our numerical algorithms are similar to those of reverse-time migration and waveform inversion based on the adjoint state of the wave equation. In order to demonstrate the practical applicability of our algorithm, we use a synthetic data set from the Marmousi model and seismic data collected from the Korean continental shelf. For noise-free synthetic data, the velocity structure produced by our inversion algorithm is closer to the true velocity structure than that obtained with conventional waveform inversion. When random noise is added, the inverted velocity model is also close to the true Marmousi model, but when frequencies below [Formula: see text] are removed from the data, the velocity structure is not as good as those for the noise-free and noisy data. For field data, we compare the time-domain synthetic seismograms generated for the velocity model inverted by our algorithm with real seismograms and find that the results show that our inversion algorithm reveals short-period features of the subsurface. Although we use wrapped phases in our examples, we still obtain reasonable results. We expect that if we were to use correctly unwrapped phases in the inversion algorithm, we would obtain better results.

A robust workflow for performing joint impedance inversion and its applications

2020 ◽  
Vol 8 (1) ◽  
pp. T141-T149
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry R. Lines
Keyword(s):  
Time Domain ◽  
Seismic Data ◽  
Sedimentary Basin ◽  
Vertical Component ◽  
Data Sets ◽  
Data Set ◽  
Matching Process ◽  
Multicomponent Seismic ◽  
Impedance Inversion ◽  
Straightforward Approach

Multicomponent seismic data offer several advantages for characterizing reservoirs with the use of the vertical component (PP) and mode-converted (PS) data. Joint impedance inversion inverts both of these data sets simultaneously; hence, it is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data are mapped on the PP time domain. Normally, this is attempted by performing well-to-seismic ties for PP and PS data sets and matching different horizons picked on PP and PS data. Although it seems to be a straightforward approach, there are a few issues associated with it. One of them is the lower resolution of the PS data compared with the PP data that presents difficulties in the correlation of the equivalent reflection events on both the data sets. Even after a few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data when mapped into PP time. We have evaluated such challenges using a data set from the Western Canadian Sedimentary Basin and then develop a novel workflow for addressing them. The importance of our workflow was determined by comparing data examples generated with and without its adoption.

L1–2 minimization for P- and S-impedance inversion

2020 ◽  
Vol 8 (2) ◽  
pp. T379-T390
Author(s):  
Wenliang Nie ◽  
Xiaotao Wen ◽  
Jixin Yang ◽  
Jian He ◽  
Kai Lin ◽  
...  
Keyword(s):  
Objective Function ◽  
Reservoir Characterization ◽  
A Priori ◽  
Low Frequency ◽  
Superior Performance ◽  
Minimization Method ◽  
Block Boundary ◽  
Avo Inversion ◽  
Impedance Inversion ◽  
The Difference

Amplitude variation with offset (AVO) inversion has been widely used in reservoir characterization to predict lithology and fluids. However, some existing AVO inversion methods that use [Formula: see text] norm regularization may not obtain the block boundary of subsurface layers because the AVO inversion is a severely ill-posed problem. To obtain sparse and accurate solutions, we have introduced the [Formula: see text] minimization method as an alternative to [Formula: see text] norm regularization. We used [Formula: see text] minimization for simultaneous P- and S-impedance inversion from prestack seismic data. We first derived the forward problem with multiangles and set up the inversion objective function with constraints of a priori low-frequency information obtained from well-log data. Then, we introduced minimization of the difference of [Formula: see text] and [Formula: see text] norms, denoted as [Formula: see text] minimization, to solve this objective function. The nonconvex penalty function of the [Formula: see text] minimization method is decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem is solved by the alternating direction method of multipliers. Compared to [Formula: see text] norm regularization, the results indicate that [Formula: see text] minimization has superior performance over [Formula: see text] norm regularization in promoting blocky/sparse solutions. Tests on synthetic and field data indicate that our method can provide sparser and more accurate P- and S-impedance inversion results. The overall results confirm that our method has great potential in the detection and identification of fluids.

Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Yonggyu Choi ◽  
Yeonghwa Jo ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Input Data ◽  
Field Data ◽  
A Priori ◽  
Resolution Enhancement ◽  
Training Data ◽  
A Priori Information ◽  
Spectral Enhancement ◽  
Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

Prestack structurally constrained impedance inversion

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R89-R103 ◽  
Author(s):  
Haitham Hamid ◽  
Adam Pidlisecky ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Local Structure ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Simultaneous Inversion ◽  
Common Depth Point ◽  
Inversion Methods ◽  
Impedance Inversion ◽  
Regularization Operator ◽  
Complex Geology

Classical prestack impedance inversion methods are based on performing a common-depth point (CDP) by CDP inversion using Tikhonov-type regularization. We refer to it as lateral unconstrained inversion (1D-LUI). Prestack seismic data usually have a low signal-to-noise ratio, and the 1D-LUI approach is sensitive to noise. The inversion results can be noisy and lead to an unfocused transition between vertical formation boundaries. The lateral constrained inversion (1D-LCI) can suppress the noise and provide sharp boundaries between inverted 1D models in regions where the layer dips are less than 20°. However, in complex geology, the disadvantage of using the 1D-LC approach is the lateral smearing of the steeply dipping layers. We have developed a structurally constrained inversion (1D-SCI) approach to mitigate the smearing associated with 1D-LCI. SCI involves simultaneous inversion of all seismic CDPs using a regularization operator that forces the solution to honor the local structure. The results of the 1D-SCI were superior compared with the 1D-LUI and 1D-LCI approaches. The steeply dipping layers are clearly visible on the SCI inverted results.

scholarly journals Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data

2019 ◽  
Vol 219 (3) ◽  
pp. 1866-1875 ◽  
Author(s):  
F M Wagner ◽  
C Mollaret ◽  
T Günther ◽  
A Kemna ◽  
C Hauck
Keyword(s):  
Electrical Resistivity ◽  
Liquid Water ◽  
Field Data ◽  
Joint Inversion ◽  
A Priori ◽  
Seismic Refraction ◽  
Rock Matrix ◽  
Data Set ◽  
Water Ice ◽  
Borehole Temperatures

SUMMARY Quantitative estimation of pore fractions filled with liquid water, ice and air is crucial for a process-based understanding of permafrost and its hazard potential upon climate-induced degradation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. We present a method to jointly estimate the volumetric fractions of liquid water, ice, air and the rock matrix from seismic refraction and electrical resistivity data. Existing approaches rely on conventional inversions of both data sets and a suitable a priori estimate of the porosity distribution to transform velocity and resistivity models into estimates for the four-phase system, often leading to non-physical results. Based on two synthetic experiments and a field data set from an Alpine permafrost site (Schilthorn, Bernese Alps and Switzerland), it is demonstrated that the developed petrophysical joint inversion provides physically plausible solutions, even in the absence of prior porosity estimates. An assessment of the model covariance matrix for the coupled inverse problem reveals remaining petrophysical ambiguities, in particular between ice and rock matrix. Incorporation of petrophysical a priori information is demonstrated by penalizing ice occurrence within the first two meters of the subsurface where the measured borehole temperatures are positive. Joint inversion of the field data set reveals a shallow air-rich layer with high porosity on top of a lower-porosity subsurface with laterally varying ice and liquid water contents. Non-physical values (e.g. negative saturations) do not occur and estimated ice saturations of 0–50 per cent as well as liquid water saturations of 15–75 per cent are in agreement with the relatively warm borehole temperatures between −0.5  and 3 ° C. The presented method helps to improve quantification of water, ice and air from geophysical observations.

Separation of regional and residual magnetic field data

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 431-439 ◽  
Author(s):  
Yaoguo Li ◽  
Douglas W. Oldenburg
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Power Spectra ◽  
Magnetic Data ◽  
Inversion Algorithm ◽  
Large Area ◽  
Data Set ◽  
Regional Sources ◽  
Magnetic Inversion ◽  
Susceptibility Model

We present a method for separating regional and residual magnetic fields using a 3-D magnetic inversion algorithm. The separation is achieved by inverting the observed magnetic data from a large area to construct a regional susceptibility distribution. The magnetic field produced by the regional susceptibility model is then used as the regional field, and the residual data are obtained by simple subtraction. The advantages of this method of separation are that it introduces little distortion to the shape of the extracted anomaly and that it is not affected significantly by factors such as topography and the overlap of power spectra of regional and residual fields. The proposed method is tested using a synthetic example having varying relative positions between the local and regional sources and then using a field data set from Australia. Results show that the residual field extracted using this method enables good recovery of target susceptibility distribution from inversions.

Robust deep learning seismic inversion with a priori initial model constraint

2021 ◽  
Author(s):  
Jian Zhang ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Yuanqiang Li ◽  
Guangtan Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
A Priori ◽  
Seismic Inversion ◽  
Training Dataset ◽  
Initial Model ◽  
Data Set ◽  
The Real ◽  
Model Constraint ◽  
Trained Network

Summary Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing deep learning-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labeled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training dataset is firstly generated based on forward modeling. The network is then pre-trained using the synthetic training dataset, which is further validated using synthetic data that has not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labeled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2D data including both post-stack and pre-stack examples, as well as a real 3D post-stack seismic data set from the western Canadian sedimentary basin.

3D marine magnetotelluric inversion: A hybrid impedance and direct-field approach

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. E335-E346
Author(s):  
Lutz Mütschard ◽  
Ketil Hokstad ◽  
Torgeir Wiik ◽  
Bjørn Ursin
Keyword(s):  
Electric Field ◽  
Real Data ◽  
Source Distribution ◽  
Impedance Tensor ◽  
Inversion Algorithm ◽  
Data Set ◽  
Wave Source ◽  
Field Approach ◽  
Impedance Inversion ◽  
Field Inversion

The measured electromagnetic field in magnetotellurics (MT) is composed of the natural source field and its subsurface response. Commonly, the data are represented as impedances, the complex ratio between the horizontal electric and magnetic fields. This measure is independent of the source distribution because the impedance-tensor estimation contains a deconvolution operator. We have used a Gauss-Newton-type 3D MT inversion scheme to compare impedance-data inversion with an inversion using the recorded electric field directly. The use of the observed electric field is beneficial to the inversion algorithm because it simplifies the estimation of the sensitivities. The direct-field approach permits the use of the observed data without processing, but it presumes knowledge of the source distribution. A method to estimate the time-variable strength and polarization of the incoming plane-wave source is presented and tested on synthetic and real-data examples. The direct-field inversion is successfully applied to a synthetic and a real data set within marine settings. A comparison with the conventional impedance inversion is conducted. The results of the synthetic data example are very similar, with a slightly more accurate reconstruction of the model in the impedance case, whereas the direct-field inversion produces a smoother inversion result when compared with the impedance case. The mapping of a resistive salt structure in the real-data example indicates deviations in the final conductivity models. The impedance inversion suggests a deeper rooted resistive structure, whereas the direct-field inversion predicts a more compact structure limited to the overburden. We have evaluated the advantages of the new approach like the simplification of the sensitivity calculation, limitations, and disadvantages like knowledge of the source distribution.

A method for adequate selection of training data sets to reconstruct seismic field data using a convolutional U-Net

Geophysics ◽  
2021 ◽  
pp. 1-103
Author(s):  
Jiho Park ◽  
Jihun Choi ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Young Kim
Keyword(s):  
Data Analysis ◽  
Seismic Data ◽  
Field Data ◽  
Training Data ◽  
Data Reconstruction ◽  
Data Sets ◽  
Training Set ◽  
Data Set ◽  
Seismic Data Reconstruction ◽  
Target Data

Deep learning (DL) methods are recently introduced for seismic signal processing. Using DL methods, many researchers have adopted these novel techniques in an attempt to construct a DL model for seismic data reconstruction. The performance of DL-based methods depends heavily on what is learned from the training data. We focus on constructing the DL model that well reflect the features of target data sets. The main goal is to integrate DL with an intuitive data analysis approach that compares similar patterns prior to the DL training stage. We have developed a two-sequential method consisting of two stage: (i) analyzing training and target data sets simultaneously for determining target-informed training set and (ii) training the DL model with this training data set to effectively interpolate the seismic data. Here, we introduce the convolutional autoencoder t-distributed stochastic neighbor embedding (CAE t-SNE) analysis that can provide the insight into the results of interpolation through the analysis of both the training and target data sets prior to DL model training. The proposed method were tested with synthetic and field data. Dense seismic gathers (e.g. common-shot gathers; CSGs) were used as a labeled training data set, and relatively sparse seismic gather (e.g. common-receiver gathers; CRGs) were reconstructed in both cases. The reconstructed results and SNRs demonstrated that the training data can be efficiently selected using CAE t-SNE analysis and the spatial aliasing of CRGs was successfully alleviated by the trained DL model with this training data, which contain target features. These results imply that the data analysis for selecting target-informed training set is very important for successful DL interpolation. Additionally, the proposed analysis method can also be applied to investigate the similarities between training and target data sets for another DL-based seismic data reconstruction tasks.

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