Automatic early arrival traveltime tomography and its applications

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. U1-U11 ◽  
Author(s):  
Chengbin (Chuck) Peng ◽  
Jun Tang
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Velocity Model ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Data Set ◽  
Early Arrival ◽  
Head Waves ◽  
Synthetic Test ◽  
Anisotropy Parameters

We have developed a method of macrovelocity inversion that does not require explicit picking of either common-image point gathers or first breaks. The method uses head waves, diving waves, and wide-angle reflections in seismic data (collectively early arrival energies) for accurate estimation of velocity and anisotropy parameters. In this method, seismic data are first decomposed into Gaussian packets. Packets associated with early arrival energies are selected and used as input to a tomography solver. The outputs of the solver are velocity and Thomsen’s anisotropy parameters, or any of their combinations. Using information contained in the packets, we can correctly model the early arrival energies (first breaks and/or other refractions). The workflow is fully automatic and can be used in a batch processing environment with minimum human intervention. We have tested the method on synthetic and field data sets. In one synthetic test, we were able to reduce traveltime residuals of diving waves from 400 to 5 ms and recover anisotropic model parameters that are sensitive to early arrival traveltimes. In another synthetic test, we were able to recover a large shallow low-velocity anomaly with a very simple starting velocity model. The first field data set was for a shallow marine seismic data project. We were able to obtain a better shallow velocity model using our method than when using a legacy approach. In the second field data test, we applied our method on a deepwater data set from a dual-coil acquisition, with full-azimuth and long-offset coverage. Our method can correctly model early arrival energies recorded at long offsets and use them in the iterative inversion such that better estimation of velocities and anisotropy parameters in shallow sediments can be achieved. We have tested different starting models for the inversion. We are able to get very similar results, suggesting that our method is not sensitive to the accuracy of a starting model.

scholarly journals Assessing estimated velocity-depth models: Finding error bars in tomographic inversion

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE223-VE233 ◽  
Author(s):  
D. A. Chiţu ◽  
M. N. Al-Ali ◽  
D. J. Verschuur
Keyword(s):  
Statistical Analysis ◽  
Seismic Data ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Second Step ◽  
Model Parameters ◽  
Data Set ◽  
Geometry Design ◽  
Statistical Measures ◽  
Error Bars

In conventional migration velocity analysis methods, a velocity model is estimated that results in flattened events in common-image gathers. However, after this process, no information is available on the accuracy of this velocity model. A statistical analysis of velocity-model parameters is very difficult because of the integrated nature of the process. In common-focus-point technology, velocity estimation is split into two processes: a first step to estimate one-way focusing operators from the seismic data and a second step to translate these one-way propagation operators into a velocity-depth model. Because the second step does not involve seismic data and uses a hands-off model parameterization, a statistical analysis of the inversion result becomes rather straightforward. We developed a methodology for obtaining a suite of possible solutions, from which statistical measures can be extracted. By varying initial settings, the inversion of one-way traveltimes provides a space of solutions. Rather than having a single estimated model, we can obtain an ensemble of models. By performing statistical analysis of this ensemble, the error bars of the estimated velocity model can be retrieved. The procedure was tested for a 2D synthetic and field data set, for which the latter compares favorably to a conventional two-way traveltime tomography approach. The information provided by such an analysis is important because it shows the reliability of the final estimated model and could provide feedback for acquisition geometry design. More or better data might be needed to obtain a model to which a smaller degree of ambiguity is associated.

Integrated prestack depth migration of vertical seismic profile and surface seismic data from the Rocky Mountain Foothills of southern Alberta, Canada

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1782-1791 ◽  
Author(s):  
M. Graziella Kirtland Grech ◽  
Don C. Lawton ◽  
Scott Cheadle
Keyword(s):  
Seismic Data ◽  
Rocky Mountain ◽  
Velocity Model ◽  
Seismic Profile ◽  
Vertical Seismic Profile ◽  
Prestack Depth Migration ◽  
Data Set ◽  
Depth Migration ◽  
Southern Alberta ◽  
Vsp Data

We have developed an anisotropic prestack depth migration code that can migrate either vertical seismic profile (VSP) or surface seismic data. We use this migration code in a new method for integrated VSP and surface seismic depth imaging. Instead of splicing the VSP image into the section derived from surface seismic data, we use the same migration algorithm and a single velocity model to migrate both data sets to a common output grid. We then scale and sum the two images to yield one integrated depth‐migrated section. After testing this method on synthetic surface seismic and VSP data, we applied it to field data from a 2D surface seismic line and a multioffset VSP from the Rocky Mountain Foothills of southern Alberta, Canada. Our results show that the resulting integrated image exhibits significant improvement over that obtained from (a) the migration of either data set alone or (b) the conventional splicing approach. The integrated image uses the broader frequency bandwidth of the VSP data to provide higher vertical resolution than the migration of the surface seismic data. The integrated image also shows enhanced structural detail, since no part of the surface seismic section is eliminated, and good event continuity through the use of a single migration–velocity model, obtained by an integrated interpretation of borehole and surface seismic data. This enhanced migrated image enabled us to perform a more robust interpretation with good well ties.

Traveltime approximation for converted waves in elastic orthorhombic media

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. C229-C237 ◽  
Author(s):  
Shibo Xu ◽  
Alexey Stovas
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Processing Parameters ◽  
Model Parameters ◽  
Seismic Data Processing ◽  
Converted Wave ◽  
Rational Form ◽  
Time Migration ◽  
Taylor Series Approximation ◽  
Anisotropy Parameters

The moveout approximations are commonly used in seismic data processing such as velocity analysis, modeling, and time migration. The anisotropic effect is very obvious for a converted wave when estimating the physical and processing parameters from the real data. To approximate the traveltime in an elastic orthorhombic (ORT) medium, we defined an explicit rational-form approximation for the traveltime of the converted [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-waves. To obtain the expression of the coefficients, the Taylor-series approximation is applied in the corresponding vertical slowness for three pure-wave modes. By using the effective model parameters for [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-waves, the coefficients in the converted-wave traveltime approximation can be represented by the anisotropy parameters defined in the elastic ORT model. The accuracy in the converted-wave traveltime for three ORT models is illustrated in numerical examples. One can see from the results that, for converted [Formula: see text]- and [Formula: see text]-waves, our rational-form approximation is very accurate regardless of the tested ORT model. For a converted [Formula: see text]-wave, due to the existence of cusps, triplications, and shear singularities, the error is relatively larger compared with PS-waves.

Using orthorhombic depth imaging to image fractures in tight reservoirs of the RDG Field, Barmer Basin, India

2019 ◽  
Vol 38 (4) ◽  
pp. 268-273
Author(s):  
Maheswara Phani ◽  
Sushobhan Dutta ◽  
Kondal Reddy ◽  
Sreedurga Somasundaram
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Substantial Improvement ◽  
Natural Fractures ◽  
Data Set ◽  
Depth Migration ◽  
Velocity Modeling ◽  
Barmer Basin ◽  
Induced Fractures

Raageshwari Deep Gas (RDG) Field is situated in the southern part of the Barmer Basin in Rajasthan, India, at a depth of 3000 m. With both clastic and volcanic lithologies, the main reservoirs are tight, and hydraulic fracturing is required to enhance productivity, especially to improve permeability through interaction of induced fractures with natural fractures. Therefore, optimal development of the RDG Field reservoirs requires characterization of faults and natural fractures. To address this challenge, a wide-azimuth 3D seismic data set over the RDG Field was processed to sharply define faults and capture anisotropy related to open natural fractures. Anisotropy was indicated by the characteristic sinusoidal nature of gather reflection events processed using conventional tilted transverse imaging (TTI); accordingly, we used orthorhombic imaging to correct for these, to quantify fracture-related anisotropy, and to yield a more correct subsurface image. During prestack depth migration (PSDM) processing of the RDG data, TTI and orthorhombic velocity modeling was done with azimuthal sectoring of reflection arrivals. The resultant PSDM data using this velocity model show substantial improvement in image quality and vertical resolution at the reservoir level compared to vintage seismic data. The improved data quality enabled analysis of specialized seismic attributes like curvature and thinned fault likelihood for more reliable characterization of faults and fractures. These attributes delineate the location and distribution of probable fracture networks within the volcanic reservoirs. Interpreted subtle faults, associated with fracture zones, were validated with microseismic, production, and image log data.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

Random objective waveform inversion of surface waves

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. EN49-EN61
Author(s):  
Yudi Pan ◽  
Lingli Gao
Keyword(s):  
Objective Function ◽  
Surface Waves ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Initial Model ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Synthetic Test ◽  
Ground Penetrating

Full-waveform inversion (FWI) of surface waves is becoming increasingly popular among shallow-seismic methods. Due to a huge amount of data and the high nonlinearity of the objective function, FWI usually requires heavy computational costs and may converge toward a local minimum. To mitigate these problems, we have reformulated FWI under a multiobjective framework and adopted a random objective waveform inversion (ROWI) method for surface-wave characterization. Three different measure functions were used, whereas the combination of one measure function with one shot independently provided one of the [Formula: see text] objective functions ([Formula: see text] is the total number of shots). We have randomly chose and optimized one objective function at each iteration. We performed a synthetic test to compare the performance of the ROWI and conventional FWI approaches, which showed that the convergence of ROWI is faster and more robust compared with conventional FWI approaches. We also applied ROWI to a field data set acquired in Rheinstetten, Germany. ROWI successfully reconstructed the main geologic feature, a refilled trench, in the final result. The comparison between the ROWI result and a migrated ground-penetrating radar profile further proved the effectiveness of ROWI in reconstructing the near-surface S-wave velocity model. We also ran the same field example by using a poor initial model. In this case, conventional FWI failed whereas ROWI still reconstructed the subsurface model to a fairly good level, which highlighted the relatively low dependency of ROWI on the initial model.

Automatic, geologic layer-constrained well-seismic tie through blocked dynamic warping

2017 ◽  
Vol 5 (3) ◽  
pp. SJ81-SJ90 ◽  
Author(s):  
Kainan Wang ◽  
Jesse Lomask ◽  
Felix Segovia
Keyword(s):  
Seismic Data ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Velocity Model ◽  
Data Set ◽  
Oil And Gas Exploration ◽  
Interval Velocity ◽  
Velocity Changes ◽  
Dynamic Warping

Well-log-to-seismic tying is a key step in many interpretation workflows for oil and gas exploration. Synthetic seismic traces from the wells are often manually tied to seismic data; this process can be very time consuming and, in some cases, inaccurate. Automatic methods, such as dynamic time warping (DTW), can match synthetic traces to seismic data. Although these methods are extremely fast, they tend to create interval velocities that are not geologically realistic. We have described the modification of DTW to create a blocked dynamic warping (BDW) method. BDW generates an automatic, optimal well tie that honors geologically consistent velocity constraints. Consequently, it results in updated velocities that are more realistic than other methods. BDW constrains the updated velocity to be constant or linearly variable inside each geologic layer. With an optimal correlation between synthetic seismograms and surface seismic data, this algorithm returns an automatically updated time-depth curve and an updated interval velocity model that still retains the original geologic velocity boundaries. In other words, the algorithm finds the optimal solution for tying the synthetic to the seismic data while restricting the interval velocity changes to coincide with the initial input blocking. We have determined the application of the BDW technique on a synthetic data example and field data set.

3D ray+Born migration/inversion—Part 2: Application to the SEG/EAGE overthrust experiment

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1357-1370 ◽  
Author(s):  
Stéphane Operto ◽  
Gilles Lambaré ◽  
Pascal Podvin ◽  
Philippe Thierry
Keyword(s):  
Spatial Resolution ◽  
Seismic Data ◽  
Velocity Model ◽  
Nonuniform Distribution ◽  
Data Sets ◽  
Data Set ◽  
Quantitative Estimates ◽  
3D Processing ◽  
Type Data ◽  
New Formula

The SEG/EAGE overthrust model is a synthetic onshore velocity model that was used to generate several large synthetic seismic data sets using acoustic finite‐difference modeling. From this database, several realistic subdata sets were extracted and made available for testing 3D processing methods. For example, classic onshore‐type data‐acquisition geometries are available such as a swath acquisition, which is characterized by a nonuniform distribution of long offsets with azimuth and midpoints. In this paper, we present an application of 2.5D and 3D ray‐Born migration/inversion to several classical data sets from the SEG/EAGE overthrust experiment. The method is formulated as a linearized inversion of the scattered wavefield. The method allows quantitative estimates of short wavelength components of the velocity model. First, we apply a 3D migration/inversion formula formerly developed for marine acquisitions to the swath data set. The migrated sections exhibit significant amplitude artifacts and acquisition footprints, also revealed by the shape of the local spatial resolution filters. From the analysis of these spatial resolution filters, we propose a new formula significantly improving the migrated dip section. We also present 3D migrated results for the strike section and a small 3D target containing a channel. Finally, the applications demonstrate, that the ray+Born migration formula must be adapted to the acquisition geometry to obtain reliable estimates of the true amplitude of the model perturbations. This adaptation is relatively straightforward in the frame of the ray+Born formalism and can be guided by the analysis of the resolution operator.

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.

Case Study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Guofeng Liu ◽  
Xiaohong Meng ◽  
Johanes Gedo Sea
Keyword(s):  
Wave Equation ◽  
Image Quality ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Mineral Exploration ◽  
Velocity Model ◽  
Image Point ◽  
Single Shot ◽  
Data Set ◽  
Depth Migration

Seismic reflection is a proven and effective method commonly used during the exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we employed the attribute migration method. This worked by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponded to each image point was obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function was proposed to map every image point to a certain range of offsets. This helped improve the signal-to-noise ratio, which was especially important in handing low quality seismic data obtained during mineral exploration. Residual velocity analysis was applied to these gathers to update the velocity model and improve image quality. The offset-domain common-image gathers were also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, the proposed procedure achieved an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

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