The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults.

In contrast to marine deposits, continental deposits in China are characterized by diverse sedimentary types, rapid changes in sedimentary facies, complex lithology, and thin, small sand bodies. In seismic sedimentology studies on continental lacustrine basins, new thinking and more detailed and effective technical means are needed to generate lithological data cubes and conduct seismic geomorphologic analyses. Based on a series of tests and studies, this paper presents the concepts of time-equivalent seismic attributes and seismic sedimentary bodies and a “four-step approach” for the seismic sedimentologic study of continental basins: Step 1, build a time-equivalent stratigraphic framework based on vertical analysis and horizontal correlation of lithofacies, electrofacies, seismic facies, and paleontological combinations; Step 2, further build a sedimentary facies model based on the analysis of single-well facies with outcrop, coring, and lab test data; Step 3, convert the seismic data into a lithological data cube reflecting different lithologies by means of seismic techniques; and Step 4, perform a time-equivalent attribute analysis and convert the planar attribute into a sedimentary facies map under the guidance of the sedimentary facies model. The whole process, highlighting the verification and calibration of geological data, is an iteration and feedback procedure of geoseismic data. The key technologies include the following: (1) a seismic data-lithology conversion technique applicable to complex lithology, which can convert the seismic reflection from interface types to rock layers; and (2) time-equivalent seismic unit analysis and a time-equivalent seismic attribute extraction technique. Finally, this paper demonstrates the validity of the approach with an example from the Qikou Sag in the Bohai Bay Basin and subsequent drilling results.

Accurate and efficient seismic horizon interpretation is important for seismic geomorphology study. By integrating the improved density-based clustering method to generate horizon patches and a heuristic combining strategy to merge horizon patches, we have developed a novel data mining approach to automatically extract globally optimal horizons for detailed geomorphologic interpretation. First, the application of improved density-based clustering method has distinct merits in calculation speed and avoiding the phenomenon of mis-ties. We design a heuristic combining strategy to effectively combine the horizon patches. It is also able to ameliorate the problem of mis-ties that frequently occurs in horizon picking. Second, the proposed algorithm can identify abnormal unit in terms of independent horizon fragments. Furthermore, the introduced method is capable of detecting small-scale seismic geomorphologic features. The applications indicate good real-time performance of our new global interpretation algorithm in automated-tracking speed and quality. Our method can resolve the problem of mis-ties in cases of complex seismic reflection to a certain extent. Besides, not only are a series of channels separately recognized, but also small-scale meandering rivers are clearly mapped. Our algorithm is capable of adding more geologic information and realizing a better showcase of geomorphologic features.

In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To investigate the potential of SBL-EM in a seismic spectral sparse reflectivity inversion, we evaluate the inversion results by comparing them with those of a SBL-SA-based approach in multiple aspects, including the sensitivity to different frequency bands, the robustness to data noise, the lateral continuity of the final profiles and so on. Furthermore, we apply the mean square error (MSE), residual variance (RV) of seismograms and residual energy (RE) between the frequency spectra of the true and inverted reflectivity model to highlight the advantages of the proposed method over the SBL-SA-based approach in terms of spectral sparse reflectivity inversion within a sparse Bayesian learning framework. Multiple examples, including both numerical and field experiments, are carried out to validate the proposed method.

Seismic attributes are critical in understanding geologic factors, such as sand body configuration, lithology, and porosity. However, existing attributes typically reflect the combined response of multiple geologic factors. The interplay between these factors can obscure the features of the target factor, posing a challenge to its direct seismic characterization, particularly when the factor is subtle. To address this, we develop an innovative neural network designed to disentangle and characterize the individual geologic factors within seismic data. Our approach divides the geologic information in the seismic data into two categories: a single geologic factor of interest and an aggregate of all other information. A novel feature-swapping mechanism within our network facilitates the disentanglement of these two categories, providing an interpretable representation. We use a triplet loss function to differentiate data samples with similar waveforms but varying subtle geologic details, thus enhancing the extraction of distinct features. In addition, our network uses a cotraining strategy to integrate the synthetic and actual field data during the training process. This strategy helps mitigate the potential performance degradation arising from the discrepancies between simulated and actual field data. We apply our method to synthetic data experiments and field data from two geologically distinct areas. Current results indicate that our method surpasses traditional approaches, such as a deep autoencoder and a convolutional neural network classifier, in extracting seismic attributes with more explicit geophysical implications.