Seismic Dip Research Articles

Enhancing seismic feature orientations: A novel approach using directional derivatives and Hilbert transform of gradient structure tensor

Seismic dip calculation serves as a widely employed technique in the realms of seismic interpretation and reservoir characterization, strategically employed to highlight faults and attributes within the seismic volume. Among the various methodologies utilized for estimating structural dip and azimuth, the Gradient Structure Tensor (GST) stands out. This approach involves leveraging the dominant eigenvector of the positive definite GST matrix to ascertain the inline and crossline dip of seismic data.In the initial phase of our innovative proposal, we employed the spectral balancing technique to enhance the fidelity of seismic data. Subsequently, leveraging this groundwork, we introduced an Analytical Directional Gradient Structure Tensor technique, a distinctive adaptation of GST. This novel approach involves the calculation of directive derivatives in both perpendicular and parallel directions to seismic features. By incorporating directive derivatives, our method excels in capturing subtle stratigraphic nuances, particularly in the dipping direction of interest. To validate the accuracy and effectiveness of our approach, we present compelling evidence through the examination of synthetic and real-field seismic volume outcomes. This underscores the robustness and reliability of our proposed method in enhancing the precision of seismic dip calculations and providing valuable insights into subsurface geological features.

Structurally Constrained Initial Impedance Modeling for Poststack Seismic Inversion

The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Seismic Image Dip Estimation by Multiscale Principal Component Analysis

Dip estimation of geological structures plays an important role in geophysical applications. Principal component analysis (PCA) is a common approach to estimating local dips by decomposing the local gradients of a seismic migration image and obtaining its principal eigenvector. However, PCA is difficult to obtain robust and high-resolution dip estimations for low signal-to-noise ratio (SNR) migration images, while multiscale schemes in digital image processing can achieve a better compromise between noise robustness and dip resolution. Therefore, we propose to adopt a multiscale PCA (MPCA) method coupled with a propagation-weight-based fusion mechanism for seismic dip estimation of low SNR migration image. MPCA consists of three steps: 1) constructing an image pyramid by repeating the low-pass filter from fine to coarse scales; 2) estimating the dip using the PCA method at each scale of the image pyramid; and 3) fusing the multiscale dip estimations using propagation weights from coarse to fine scales. We test the MPCA method on an omnidirectional dip pattern and three seismic migration images and compare with the conventional PCA and multiscale methods. The results demonstrate that MPCA yields robust and high-resolution dip estimations for low SNR seismic migration images.

Seismic volumetric dip estimation via a supervised deep learning model by integrating realistic synthetic data sets

Accurately estimating seismic volumetric dip is a crucial task for subsequent seismic processing and interpretation. The traditional window based dip estimation methods, such as gradient structure tensor (GST) and semblance based multiple window scanning strategy, usually struggle with complicated geological structures containing multiple reflections with different dips in the analysis window. Recently, deep learning has been utilized to estimate seismic volumetric dips. However, dip labels used for current convolutional neural network (CNN) based methods are usually created using traditional dip estimation methods, which are not the ground truth, or from specific geological structure models, which only cover a part of typical geological structures. We propose a supervised deep learning model to improve the accuracy of seismic dip estimation by integrating realistic synthetic data sets. Firstly, we propose a synthetic seismic data generation workflow for seismic volumetric dip estimation, which aims to simulate geological features from real seismic data. We then create numerous unique synthetic seismic images with realistic and diverse structural features by using the proposed workflow. To ensure that generated dip labels are exactly accurate and can be regarded as the ground truth, we create synthetic seismic images by deforming horizontal reflection images according to corresponding dip labels. We finally train an end-to-end supervised deep learning model, which focuses on the parallel processing and the extraction of various feature maps simultaneously, utilizing the created realistic synthetic data. The applications on synthetic and 3D real seismic data (Netherlands F3 block) effectually demonstrate the validity and effectiveness of our proposed model.

Inversion-based attenuation compensation with dip constraint

Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme. However, the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip. Thus, the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio (S/N). To alleviate this issue, we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation (DCAC) algorithm. The seismic dip information, calculated from the poststack seismic data, is the key to construct a dip constraint term. Benefiting from the introduction of the seismic dip constraint, the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result. Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution, but also protect the continuity of seismic data.

Fine Complex Geological Structure Interpretation Based on Multiscale Seismic Dip Constraint.

With the development of seismic exploration technology, geological structure interpretation has become more and more refined, whereas random noise interference, subsalt weak seismic reflection signals, and other issues are also gradually emerged at the same time, which resulted in traditional geological structure interpretation accuracy reduction only relying on single seismic data. A novel technical process integrated time-frequency decomposition of seismic data, seismic dip constraints, and geological structure interpretation is proposed in this paper which is named multiscale seismic dip constraint geological structure interpretation. The technical process contains five steps which first use the basis tracking spectrum decomposition technology to convert the seismic data into the time-frequency domain and then decompose the raw seismic data into coarse scale, fine scale, and deliberate scale through window and threshold methods. Subsequently, execute local layer dip calculation with Hilbert transform and geological structure interpretation on seismic data of different scale, respectively. At last, perform geological structure attribute fusion to obtain fine geological structure interpretation. Synthetic data test and field data test show that through multiscale time-frequency decomposition, high-frequency noise interference can be removed and the subsalt seismic weak signal can be enhanced, and then, high-precision fine complex geological structure interpretation can be obtained with seismic dip constraint. Therefore, the technical process proposed in this paper is effective and can be widely applied in the interpretation of field seismic data.

Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model

Although there are plenty of approaches proposed for addressing seismic volumetric dip estimation, it still suffers from several limitations, for example, the expensive computation cost, the perturbations from sequence stratigraphic anomalies, and the difficulty for handling the complicated geologic structures. Recently, deep learning (DL) based models have been proposed for seismic dip estimation, which utilize seismic dips calculated by using the traditional methods as the training labels. Apparently, these DL based models can effectively improve the computational efficiency, however, it still subjects to the limitations of the traditional algorithms. We propose a multi-channel deep learning (MCDL) model for implementing seismic volumetric dip estimation, mainly including share module (SM), particular module (PM), and fused module (FM). First, we calculate seismic dips by using several traditional methods based on 3D real seismic data as the training labels, which are used to pre-train SM and PM. Then, we propose a workflow to create synthetic seismic data and ground truth dip labels, which are utilized to fine-tune SM/PM and train FM. In this way, we can obtain a DL model by considering both the features of synthetic ground truth dips and the calculated dips from real data. Moreover, we can effectively enhance the generalization ability of the MCDL by pre-training with the estimated dip volumes from real data. To demonstrate its validity and availability, we apply the MCDL to synthetic data and two 3D real seismic volumes. The qualitative and quantitative comparisons illustrate the superiority of the proposed model over the traditional methods.

Accelerating Seismic Dip Estimation With Deep Learning

The seismic volumetric dip is a crucial seismic geometric attribute, which can provide useful information for assisting subsequent processing and interpretation. Waveform similarity scanning-based dip estimation (WSSB) delivers reliable dip estimation but encounters problems of expensive computation. To improve computing efficiency, we use multitask deep learning to simultaneously estimate the inline dip and crossline dip directly from a 3-D field seismic dataset. Our method considers dip estimation as a regression problem and trains a multilayer convolutional neural network with dual-channel output. It aims to output continuous values of seismic apparent dip from two directions simultaneously. To train the network, we propose an effective and efficient workflow to create a training sample dataset, which consists of field seismic cubes and the corresponding dip labels estimated by WSSB. After training, the network automatically learns how to extract rich and proper features that are important for dip estimation. By sliding the extraction window within the full 3-D seismic data, the network can output many overlapping dip cubes that are stacked to get two complete 3-D volumes of seismic dip. The final results of dip estimation by our method are similar to those by WSSB. We further demonstrate the accuracy of our approach by comparing the structural curvature. However, the computation time of our method is much less than that of WSSB. The proposed method can accurately estimate seismic volumetric dips with high computational efficiency.

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.

Seismic Dip Estimation With a Domain Knowledge Constrained Transfer Learning Approach

Accurate estimation of volumetric seismic dip is of great significance for subsequent seismic processing and interpretation works. Recently, with the development of deep learning techniques, convolutional networks are also applied for seismic dip estimation. Compared with traditional approaches, estimating dips with convolutional networks is not only more efficient but also shows great promise in accuracy and robustness. However, if we take dips estimated by traditional approaches as labels and train networks on the field seismic data directly, the accuracy and robustness of learned networks are influenced due to the error in dip labels. An alternative solution is synthesizing realistic seismic samples with accurate dip labels. However, we find that due to the differences in seismic responses and structural patterns between the synthetic and field seismic data, networks directly learned from synthetic samples cannot guarantee their generalization on the field seismic data. To overcome these drawbacks, we develop a transfer learning approach for improvement. The proposed approach pretrains the dip estimation network on synthetic seismic samples at first and then transfers it to the targeted field seismic data with a domain knowledge-inspired fine-tuning process. Moreover, the proposed approach also highlights the combination of deep learning techniques and domain knowledge in seismic processing—several subtle realizations, such as knowledge-driven sample augmentation, knowledge constrained loss function, and knowledge motivated transfer learning strategy, are introduced, which greatly enhance the learning of the seismic dip estimation network. The advantages of the proposed approach in accuracy, robustness, and resolution are validated by applying the estimated dips for structural filtering and curvature extraction on the Netherlands F3 and Kerry3D seismic data, which further confirms its practicality in the real-world application. We believe that the proposed approach has provided an effective improved way for further seismic dip estimation practices, and the present domain knowledge constrained deep learning case will also inspire researchers in the same discipline.

Basin margin sediment wedge build out of the Eastern Niger Delta: application of shelf-edge trajectory pattern studies

The understanding of how basin margin sediment wedge builds out causes shelf-edge migration with time is approached based on shelf-edge trajectory pattern analysis using a high-resolution mega-merge seismic data from the eastern Niger Delta, Nigeria. The study focuses on a seismic dip transect traversing the Greater Ughelli, Central Swamp, Coastal Swamp and the Shallow Offshore Depobelts of the Niger Delta. On the regional dip transects, shelf-edge sediments occur as clinoform-bearing wedges at and immediately updip of the shelf-slope break. The shelf edge is deeply buried (> 2–4 s, twt), around the Greater Ughelli and Central Swamps. But with changing structural style, sudden change of ascending shelf edge around the Central Swamp was observed. The huge listric growth fault in the Coastal Swamp; around Bonny area, once again cut the shelf edge into half, rotated it along the listric fault and buried it distally. Several depositional packages show low to moderate ascending shelf-edge trajectory with progradational to aggradational clinoform growth that is characterized by thin sand sheets across most of the shelf and upper slope, though few are also characterized by progradational clinoform growth with thick sand on the shelf, upper-tolower slope and basin floor. The deposition is usually on the Outer Shelf Terrace (OST) which is regressive in a flat and rising trajectory style. This study has demonstrated that accommodation and sediment flux are the dominant controls on how the study basin’s sediment wedge built out, whereby limited accommodation promotes sediments with significant shelf-edge advance and descending trajectories, while increasing accommodation promotes ascending trajectories and increased deposition on the outer shelf. The greater sediments on the Outer Shelf Terrace and the shelf margin than on the slope gives more hydrocarbon prospectivity search around the outer shelf and shelf margin.

Simulating the procedure of manual seismic horizon picking

Manual seismic horizon picking is the least efficient interpretation technique in terms of time and effort. The loop-tie is a key “element” and the most time-consuming task in manual horizon picking, which ensures the accuracy of horizon picking. Autopicking techniques have been used since the early 1980s. However, there are few studies simulating the procedure of manual seismic horizon picking and quantitatively evaluating the autopicked horizons. In our proposed method, we perform autopicking on inline and crossline seismic vertical slices independently, similar to the manual horizon picking procedure. We then evaluate the picked horizons using a loop-tie step similar to the loop-tie checking in manual horizon picking. To simulate the loop-tie step in manual picking, we define two dip attributes for each time sample of seismic traces, which are the “left” and “right” reflector dips. We only preserve the portions of the tracked horizons that meet the defined loop-tie checking. Next, we merge the tracked horizons centered at the seeded seismic traces. The two-way traveltime of the merged horizons functions as a “hard” control for the final step of autopicking. Finally, we use the seismic dip attribute to track the horizons over the seismic survey under the hard controls. The real data demonstrate that our algorithm can extract accurate horizons near discontinuities such as faults and unconformities.

Tracking 3D seismic horizons with a new hybrid tracking algorithm

We have developed a new algorithm for tracking 3D seismic horizons. The algorithm combines an inversion-based, seismic-dip flattening technique with conventional, similarity-based autotracking. The inversion part of the algorithm aims to minimize the error between horizon dips and computed seismic dips. After each cycle in the inversion loop, more seeds are added to the horizon by the similarity-based autotracker. In the example data set, the algorithm is first used to quickly track a set of framework horizons, each guided by a small set of user-picked seed positions. Next, the intervals bounded by the framework horizons are infilled to generate a dense set of horizons, also known as HorizonCube. This is done under the supervision of a human interpreter in a similar manner. The results show that the algorithm behaves better than unconstrained flattening techniques in intervals with trackable events. Inversion-based algorithms generate continuous horizons with no holes to be filled posttracking with a gridding algorithm and no loop skips (jumping to the wrong event) that need to be edited as is standard practice with autotrackers. Because editing is a time-consuming process, creating horizons with inversion-based algorithms tends to be faster than conventional autotracking. Horizons created with the adopted algorithm follow seismic events more closely than horizons generated with the inversion-only algorithm, and the fault crossings are sharper.

Generating Seismic Horizon Using Multiple Seismic Attributes

Today’s 3-D seismic surveys usually contain hundreds of inline and crossline vertical seismic slices. Seismic interpreters usually need to spend weeks or even months for manually picking horizons on vertical seismic slices. Researchers have developed algorithms to accelerate horizon picking and most algorithms employ the seismic reflector’s dip as the input. However, the computed seismic reflector’s dip is usually inaccurate near and across the discontinuities in the seismic images. Note that the time samples which belong to the same horizon should have approximate similar seismic instantaneous phase values. We propose to automatically track the seismic horizon simultaneously considering the seismic reflector’s dip and instantaneous phase attributes. Our algorithm aims to achieve three objectives: 1) minimizing the difference between the dip computed using tracked horizon and seismic dip attribute, 2) minimizing the difference among instantaneous phase value of the time samples on the tracked horizon, and 3) the tracked horizon exactly passes through user-defined control points (seeds). A constrained conjugate least-square algorithm is employed to solve our optimization problem. The applications show that the tracked horizon which only uses dip attribute would “jump” from one seismic event to another seismic event near the unconformity zone. However, the horizon tracked using the proposed method strictly follows the same seismic event over the whole seismic survey.

Accurate seismic dip and azimuth estimation using semblance dip guided structure tensor analysis

Seismic volumetric dip and azimuth are widely used in assisting seismic interpretation to depict geologic structures such as chaotic slumps, fans, faults, and unconformities. Current popular dip and azimuth estimation methods include the semblance-based multiple window scanning (MWS) method and gradient structure tensor (GST) analysis. However, the dip estimation accuracy using the semblance scanning method is affected by the dip of seismic reflectors. The dip estimation accuracy using the GST analysis is affected by the analysis window centered at the analysis point. We have developed a new algorithm to overcome the disadvantages of dip estimation using MWS and GST analysis by combining and improving the two methods. The algorithm first obtains an estimated “rough” dip and azimuth for reflectors using the semblance scanning method. Then, the algorithm defines a window that is “roughly” parallel to the local reflectors using the estimated rough dip and azimuth. The algorithm next estimates the dip and azimuth of the reflectors within the analysis window using GST analysis. To improve the robustness of GST analysis to noise, we used analytic seismic traces to compute the GST matrix. The algorithm finally uses the Kuwahara window strategy to determine the dip and azimuth of local reflectors. To illustrate the superiority of this algorithm, we applied it to the F3 block poststack seismic data acquired in the North Sea, Netherlands. The comparison indicates that the seismic volumetric dips estimated using our method more accurately follow the local seismic reflectors than the dips computed using GST analysis and the semblance-based MWS method.

‘Ductile v. Brittle’ – Alternative structural interpretations for the Niger Delta

Abstract A wealth of subsurface information gathered from over 60 years of hydrocarbon exploration offshore Nigeria provides a reference study area on the interaction between sedimentation, structure and overpressure in a large delta system. The current geological paradigm is that structuration and synkinematic sedimentation is governed by shale mobility from the deeper parts of the delta. This concept is largely the result of interpretations derived from vintage seismic data and insufficient calibration of the deeper parts of the delta. Long-cable seismic data are providing new insights into this interpretation conundrum. A first-order problem, which is of particular interest here, relates to the linkage between the extensional structures updip with the compressional structures downdip. The translational zone between extension and compression is key to unravelling the nature of this link and any associated structural material balance discrepancies. The primary focus of the current paper is to interrogate seismic data and to provide alternative interpretations to the accepted paradigm. Two end-member interpretations of the Niger Delta regional seismic dip lines – referred to here as the ‘ductile model’ and the ‘brittle model’ – are presented. Aside from their internal geometrical dissimilarities, these interpretations suggest fundamentally different kinematic and geomechanical models. The latter may offer a wider scope for deep – largely neglected – hydrocarbon exploration targets. Ultimately, these ideas could provide the conceptual framework that, in conjunction with improved seismic efforts, could lead to rejuvenated exploration portfolios.

The Application of Seismic Attributes and Wheeler Transformations for the Geomorphological Interpretation of Stratigraphic Surfaces: A Case Study of the F3 Block, Dutch Offshore Sector, North Sea

This study was carried out in the Pliocene interval of the southern North Sea F3 Block in the Netherlands. This research paper demonstrates how an integrated interpretation of geological information using seismic attributes, sequence stratigraphic interpretation and Wheeler transformation methods allow for the accurate interpretation of the depositional environment of a basin, as well as locating seismic geomorphological features. The methodology adopted here is to generate a 3D dip-steered HorizonCube followed by chronostratigraphic analysis, 3D Wheeler transformation, and system tract interpretation. A dip-steered seismic attribute (similarity, dip, and curvature) was performed on each stratigraphic surface of interest and the isopach maps were generated for each stratigraphic surface to help identify the maximum deposition. The results of this study show that the similarity attribute is able to identify distinct stratigraphic features such as sand-waves and deep marine meandering channels. However, its lateral continuity is poorly understood, as the similarity attribute does not take into account the true geological dip and curvature of the surfaces. Structural features such as faults are not easily recognizable due to these reasons. However, the dip-apparent attributes are found to be very useful in identifying both the structural and stratigraphic features. The seismic dip map is then improved by rotating the dip measurements to user-defined azimuths. Such optimization has revealed the structural and stratigraphic features that are not clearly evident on the similarity and curvature attributes. The maximum curvature attribute is found to be useful in delineating faults and predicting the orientation and distribution of fractures and also in subtle structural features.

A Method of Enhancing Fault Delineation Based on Reflection Strength AC Component Filtering

Ant tracking technique is a widely used seismic interpretation method of identifying faults in the field of oil and gas exploration and development. However, due to its poor noise immunity, the fault identification effect of ant tracking could be easily affected by the quality of seismic data. Usually, two types of methods can be used to improve the effect of ant tracking, to improve the algorithm of ant tracking or to remove the noise of the seismic data. The first method is usually carried out by the research personnel, and it will take quite a long time before it can be integrated into the software, therefore, the de-noising method is more realistic for the interpreters. This paper puts forward a method of improving the effect of ant tracking by using AC component filtering of reflected intensity. In this method, the structural orientation filtering of the original seismic data is carried out first, and then a coherence cube is calculated based on multiple seismic trace dip scanning. Next, a filtering will be carried out on the coherence cube by using the AC component of the reflected intensity, and then the positive value after the filtering will be set to zero. Finally, the ant tracking will be processed based on the data volume. The improved ant tracking has a better fault identification effect with a higher fault identification rate, which is more favorable for the detailed interpretation of faults.

Integrated Geophysical Analysis and Rock Physics Study to Confirm the Hydrocarbon Reservoir of the Bitrisim Area in Pakistan

Horst and graben structure is the symbolic depiction of extensional tectonics in Pakistan, which was our research study area with (Bitrisim) as a communal example. To carry out the structural and stratigraphic interpretation of our study area, we used two seismic dip lines and one strike line. Two way time and depth mapping helped in outlining the structural trend and understanding the tectonics of the area. Subsurface mapping indicated that the major fault trend was NNW-SSE. There were marks of faults breaking out, indicating the existence of various tectonic periods. The dominant structural trend of the area provided the basic components of a profile petroleum system. We also tried to find the porosity and volume of the shale. 1D modeling has been done for the wells of Fateh-01. The zone under observation was Lower Goru. Porosity calculations were made to determine the water and hydrocarbon saturation. The main elements of a petroleum system are presented and also proven by a number of oil and gas findings, but there is still a need for advanced techniques to improve seismic resolution and the excellence of interpretation.

New methods for slicing and dicing seismic volumes

G lobal seismic interpretation techniques aim to arrive at fully interpreted seismic volumes. ‘Fully’ in this context, however, is misleading as it gives the impression that we are dealing with an end product and that there is no more interpretation to be carried out. This is not the case. The correlated geologic timelines of these volumes open up new ways to analyse seismic data, thereby increasing our understanding of the depositional history and improving our ability to find stratigraphic traps and to build accurate geologic models. Geologic information in global seismic interpretation techniques can be unlocked through new methods for dicing and slicing seismic volumes. Using HorizonCube, this can be achieved through attributes and a 3D Slider in a workflow that combines 2D and 3D visualization techniques with interactive analysis. Examples of such techniques will be introduced. The HorizonCube consists of a dense set of horizons that are computed from the seismic dip field. The vertical separation between horizons in a HorizonCube varies spatially. This feature is exploited in a new set of attributes called HorizonCube attributes that capture local and global information. Examples are: HorizonCube density and HorizonCube thickness attributes. Both attributes can be highly effective in the interpretation of unconformities, condensed sections and sedimentation rates. For slicing and dicing seismic volumes, we use a 3D Slider in a workflow that combines 2D and 3D visualization techniques with interactive analysis. The workflow enables scanning thousands of auto-tracked horizons rapidly with the objective to identify pairs of horizons corresponding to top and base of depositional features of interest. In the next step, isochron thicknesses or attribute responses are computed and geobodies are extracted.