Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging

Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in the form of diffracted waves. Generally, seismic data contain both reflected and diffracted events because of the physical phenomena in the subsurface and due to the recording system. Seismic diffractions are produced once the acoustic impedance contrast appears, including faults, fractures, channels, rough edges of structures, and karst sections. In this study, a double square root (DSR) equation is used for modeling of the diffraction hyperbola with different velocities and depths of point diffraction to elaborate the diffraction hyperbolic pattern. Further, we study the diffraction separation methods and the effects of the velocity analysis methods (semblance vs. hybrid travel time) for velocity model building for imaging. As a proof of concept, we apply our research work on a steep dipping fault model, which demonstrates the possibility of separating seismic diffractions using dip frequency filtering (DFF) in the frequency–wavenumber (F-K) domain. The imaging is performed using two different velocity models, namely the semblance and hybrid travel time (HTT) analysis methods. The HTT method provides the optimum results for imaging of complex structures and imaging below shadow zones.

Download Full-text

Deep learning-driven velocity model building workflow

The Leading Edge ◽

10.1190/tle38110872a1.1 ◽

2019 ◽

Vol 38 (11) ◽

pp. 872a1-872a9 ◽

Cited By ~ 4

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.

Download Full-text

Diffraction imaging and depth-velocity inversion with 3D P-Cable seismic data

10.5194/egusphere-egu21-12578 ◽

2021 ◽

Author(s):

Alexander Bauer ◽

Benjamin Schwarz ◽

Dirk Gajewski

Keyword(s):

Seismic Data ◽

Model Building ◽

Computational Cost ◽

Velocity Model ◽

Data Cube ◽

Velocity Inversion ◽

Velocity Models ◽

North East ◽

3D Velocity Model ◽

Zero Offset

Most established methods for the estimation of subsurface velocity models rely on the measurements of reflected or diving waves and therefore require data with sufficiently large source-receiver offsets. For seismic data that lacks these offsets, such as vintage data, low-fold academic data or near zero-offset P-Cable data, these methods fail. Building on recent studies, we apply a workflow that exploits the diffracted wavefield for depth-velocity-model building. This workflow consists of three principal steps: (1) revealing the diffracted wavefield by modeling and adaptively subtracting reflections from the raw data, (2) characterizing the diffractions with physically meaningful wavefront attributes, (3) estimating depth-velocity models with wavefront tomography. We propose a hybrid 2D/3D approach, in which we apply the well-established and automated 2D workflow to numerous inlines of a high-resolution 3D P-Cable dataset acquired near Ritter Island, a small volcanic island located north-east of New Guinea known for a catastrophic flank collapse in 1888. We use the obtained set of parallel 2D velocity models to interpolate a 3D velocity model for the whole data cube, thus overcoming possible issues such as varying data quality in inline and crossline direction and the high computational cost of 3D data analysis. Even though the 2D workflow may suffer from out-of-plane effects, we obtain a smooth 3D velocity model that is consistent with the data.

Download Full-text

Seismic Velocity Model Building Using Neural Networks: Training Data Design and Learning Generalization

Geophysics ◽

10.1190/geo2020-0547.1 ◽

2021 ◽

pp. 1-73

Author(s):

Hani Alzahrani ◽

Jeffrey Shragge

Keyword(s):

Seismic Data ◽

Model Building ◽

Seismic Velocity ◽

Velocity Model ◽

Training Data ◽

Training Process ◽

Training Models ◽

Velocity Models ◽

Velocity Model Building ◽

Layer Velocity

Data-driven artificial neural networks (ANNs) offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We ask the following question: what type of velocity model structures need be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geological setting? To address this question, we create four sets of training models: geologically inspired and purely geometrical, both with and without background velocity gradients. We find that using geologically inspired training data produce models with well-delineated layer interfaces and fewer intra-layer velocity variations. The absence of a certain geological structure in training models, though, hinders the ANN's ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intra-layer velocity variations and non-physical artifacts. Overall, the results successfully demonstrate the use of ANNs in recovering accurate velocity model estimates, and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.

Download Full-text

Deep-learning inversion: A next-generation seismic velocity model building method

Geophysics ◽

10.1190/geo2018-0249.1 ◽

2019 ◽

Vol 84 (4) ◽

pp. R583-R599 ◽

Cited By ~ 38

Author(s):

Fangshu Yang ◽

Jianwei Ma

Keyword(s):

Deep Learning ◽

Seismic Data ◽

Model Building ◽

Seismic Velocity ◽

Velocity Model ◽

Computational Time ◽

Learning Methods ◽

Velocity Models ◽

Velocity Model Building ◽

Training Stage

Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We have investigated a novel method based on the supervised deep fully convolutional neural network for velocity-model building directly from raw seismograms. Unlike the conventional inversion method based on physical models, supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multishot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multilayer useful features without the need for human-curated activities and an initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performance of our proposed method is shown in comparison with conventional FWI even when the input data are in more realistic scenarios. We have also evaluated deep-learning methods, the training data set, the lack of low frequencies, and the advantages and disadvantages of our method.

Download Full-text

Application of diffraction wavefront tomography to GPR data from a glacier

10.5194/egusphere-egu2020-17349 ◽

2020 ◽

Author(s):

Alexander Bauer ◽

Benjamin Schwarz ◽

Richard Delf ◽

Dirk Gajewski

Keyword(s):

Seismic Data ◽

Velocity Model ◽

High Amplitude ◽

Small Scale ◽

Coherence Analysis ◽

Raw Data ◽

Seismic Reflection Data ◽

Velocity Models ◽

Passive Seismic ◽

Zero Offset

In the recent years, the diffracted wavefield has gained increasing attention in the field of applied seismics. While classical seismic imaging and inversion schemes mainly focus on high-amplitude reflected measurements, the faint and often masked diffracted wavefield is neglected or even treated as noise. In order to be able to extract depth-velocity models from seismic reflection data, sufficiently large source-receiver offsets are needed. However, the acquisition of such multi-channel seismic data is expensive and often only feasible for the hydrocarbon industry, while academia has to cope with low-fold or zero-offset data. The diffracted wavefield is the key for extracting depth velocities from such data, as the moveout of diffractions &#8211; in contrast to reflections &#8211; can be measured in the zero-offset domain. Recently, we have demonstrated on multi-channel, single-channel and passive seismic data that by means of wavefront tomography depth-velocity models can be retrieved solely based on diffractions or passive seismic events along with the localizations of these scatterers. The input for wavefront tomography are so-called wavefront attributes, which can be extracted from the data in an unsupervised fashion by means of coherence analysis. In order to obtain the required diffraction-only data, we use a recently proposed scheme that adaptively subtracts the high-amplitude reflected wavefield from the raw data. Due to their most common acquisition geometry, most ground-penetrating-radar (GPR) data inherently lack offsets. In addition, GPR data generally contain a rich diffracted wavefield, which in turn contains information about sought-after structures, as diffractions are caused by small-scale heterogeneities such as faults, tips or edges. In this work, we show an application of the suggested workflow &#8211; coherence analysis, diffraction separation and diffraction wavefront tomography &#8211; to GPR data acquired at a glacier, resulting in a depth-velocity model and the localizations of the scatterers, both obtained in a fully unsupervised fashion. While the resulting&#160; velocity model may be used for depth migration of the raw data, the localizations of the scatterers may in addition provide important information on the inner structure of the glacier in order to, for instance, localize water intrusions or fractures.

Download Full-text

Seismic diffraction imaging - a case study from the Southern Aegean Sea

10.5194/egusphere-egu2020-538 ◽

2020 ◽

Author(s):

Jonas Preine ◽

Benjamin Schwarz ◽

Alexander Bauer ◽

Dirk Gajewski ◽

Christian Hübscher

Keyword(s):

Seismic Data ◽

Academic Research ◽

Structural Complexity ◽

Aegean Sea ◽

Velocity Model ◽

Small Scale ◽

Acoustic Basement ◽

Tectonic Zone ◽

Velocity Models ◽

Zero Offset

The active seismic method is a standard tool for studying the Earth&#8217;s lithosphere. On scales from centimetres to kilometres, academic research is generally interested in highly complex geological targets such as volcanic edifices, crustal faults or salt environments. In order to properly image these structures, large and expensive multichannel acquisitions with a high offset-to-target depth ratio are required. In practice, however, these are often hardly affordable for academic institutions, with the result that reflections often only poorly illuminate laterally variable structures, which in turn compromises imaging and interpretation. As in common practice, most of the processing and interpretational steps are tailored to the reflected wavefield, faint diffracted contributions are typically considered as an unwanted by-product.In recent works, however, it has been shown that diffractions possess unique properties which bear the potential to overcome the aforementioned limitations. Wave diffraction occurs at geodynamically important features like faults, pinch-outs, erosional surfaces or other small-scale scattering objects and encodes sub-wavelength information on the scattering geometry. Since diffracted waves do not obey Snell&#8217;s Law, they provide superior illumination compared to reflected waves. Moreover, due to their passive-source like radiation, they encode their full multichannel response in prominent data subsets like the zero-offset section. In order to explore what can be learned from the faint diffracted wavefield, we use academic seismic data from the Santorini-Amorgos Tectonic Zone (SATZ) in the Southern Aegean Sea. This is an area well known for its local complexity, indicated by the occurrence of extended fault systems and volcanic edifices as well as a complex acoustic basement. As the available seismic data in this region were acquired using a relatively short streamer, the SATZ represents a classical example for the need of innovative methods for seismic processing and interpretation.By means of a robust and computationally efficient scheme for the extraction of diffractions that models and adaptively subtracts the reflected wavefield from the data, we reveal a rich diffracted wavefield from zero-offset data. On the one hand, we use the diffraction-only sections for analysing the small-scale structural complexity and demonstrate that the geological interpretation can benefit from these observations. On the other hand, we use the diffractions to estimate insightful wavefront attributes in the zero-offset domain. Based on these attributes, we perform wavefront tomography to obtain depth-velocity models. Compared to depth-velocity models derived from the reflected contributions, the diffraction-based velocity model fits the data significantly better. After refining this velocity model, we perform prestack depth migration and obtain highly valuable depth converted seismic sections. Concluding our results, we strongly encourage the incorporation of diffractions in standard processing and interpretational schemes.

Download Full-text

Interferometric imaging condition for wave-equation migration

Geophysics ◽

10.1190/1.2838043 ◽

2008 ◽

Vol 73 (2) ◽

pp. S47-S61 ◽

Cited By ~ 14

Author(s):

Paul Sava ◽

Oleg Poliannikov

Keyword(s):

Large Scale ◽

Wigner Distribution ◽

Random Noise ◽

Velocity Model ◽

Distribution Functions ◽

Small Scale ◽

Interferometric Imaging ◽

Imaging Data ◽

Imaging Condition ◽

Velocity Models

The fidelity of depth seismic imaging depends on the accuracy of the velocity models used for wavefield reconstruction. Models can be decomposed in two components, corresponding to large-scale and small-scale variations. In practice, the large-scale velocity model component can be estimated with high accuracy using repeated migration/tomography cycles, but the small-scale component cannot. When the earth has significant small-scale velocity components, wavefield reconstruction does not completely describe the recorded data, and migrated images are perturbed by artifacts. There are two possible ways to address this problem: (1) improve wavefield reconstruction by estimating more accurate velocity models and image using conventional techniques (e.g., wavefield crosscorrelation) or (2) reconstruct wavefields with conventional methods using the known background velocity model but improve the imaging condition to alleviate the artifacts caused by the imprecise reconstruction. Wedescribe the unknown component of the velocity model as a random function with local spatial correlations. Imaging data perturbed by such random variations is characterized by statistical instability, i.e., various wavefield components image at wrong locations that depend on the actual realization of the random model. Statistical stability can be achieved by preprocessing the reconstructed wavefields prior to the imaging condition. We use Wigner distribution functions to attenuate the random noise present in the reconstructed wavefields, parameterized as a function of image coordinates. Wavefield filtering using Wigner distribution functions and conventional imaging can be lumped together into a new form of imaging condition that we call an interferometric imaging condition because of its similarity to concepts from recent work on interferometry. The interferometric imaging condition can be formulated both for zero-offset and for multioffset data, leading to robust, efficient imaging procedures that effectively attenuate imaging artifacts caused by unknown velocity models.

Download Full-text

Practical approaches for subsalt velocity model building

Geophysics ◽

10.1190/1.2969084 ◽

2008 ◽

Vol 73 (5) ◽

pp. VE183-VE194 ◽

Cited By ~ 3

Author(s):

Junru Jiao ◽

David R. Lowrey ◽

John F. Willis ◽

Ruben D. Martínez

Keyword(s):

Software Package ◽

Model Building ◽

Velocity Model ◽

Fine Tuning ◽

Inherent Difficulty ◽

Velocity Models ◽

First Approximation ◽

Velocity Model Building ◽

Trade Offs ◽

Efficient Performance

Imaging sediments below salt bodies is challenging because of the inherent difficulty of estimating accurate velocity models. These models can be estimated in a variety of ways with varying degrees of expense and effectiveness. Two methods are commercially viable trade-offs. In the first method, residual-moveout analysis is performed in a layer-stripping mode. The models produced with this method can be used as a first approximation of the subsalt velocity field. A wave-equation migration scanning technique is more suitable for fine-tuning the velocity model below the salt. Both methods can be run as part of a sophisticated interactive velocity interpretation software package that makes velocity interpretation efficient. Performance of these methods has been tested on synthetic and field data examples.

Download Full-text

Full-waveform inversion for full-wavefield imaging: Decades in the making

The Leading Edge ◽

10.1190/tle40050324.1 ◽

2021 ◽

Vol 40 (5) ◽

pp. 324-334

Author(s):

Rongxin Huang ◽

Zhigang Zhang ◽

Zedong Wu ◽

Zhiyuan Wei ◽

Jiawei Mei ◽

...

Keyword(s):

Model Building ◽

Seismic Imaging ◽

Structural Information ◽

Waveform Inversion ◽

Velocity Model ◽

Data Sets ◽

Full Waveform Inversion ◽

Data Types ◽

Velocity Models ◽

Full Waveform

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.

Download Full-text

Full Wavefield Redatuming: Accurate Velocity Modelling for Imaging Beneath Complex Overburden

10.2523/iptc-21771-ms ◽

2021 ◽

Author(s):

Farah Syazana Dzulkefli ◽

Kefeng Xin ◽

Ahmad Riza Ghazali ◽

Guo Qiang ◽

Tariq Alkhalifah

Keyword(s):

Wave Propagation ◽

Seismic Data ◽

Model Building ◽

Velocity Model ◽

Surrounding Rock ◽

Target Area ◽

Low Density ◽

Velocity Model Building ◽

Seismic Image ◽

Seismic Wavefield

Abstract Salt is known for having a generally low density and higher velocity compared with the surrounding rock layers which causes the energy to scatter once the seismic wavefield hits the salt body and relatively less energy is transmitted through the salt to the deeper subsurface. As a result, most of imaging approaches are unable to image the base of the salt and the reservoir below the salt. Even the velocity model building such as FWI often fails to illuminate the deeper parts of salt area. In this paper, we show that Full Wavefield Redatuming (FWR) is used to retrieved and enhance the seismic data below the salt area, leading to a better seismic image quality and allowing us to focus on updating the velocity in target area below the salt. However, this redatuming approach requires a good overburden velocity model to retrieved good redatumed data. Thus, by using synthetic SEAM model, our objective is to study on the accuracy of the overburden velocity model required for imaging beneath complex overburden. The results show that the kinematic components of wave propagation are preserved through redatuming even with heavily smoothed overburden velocity model.

Download Full-text