The time-frequency (TF) analysis (TFA) tools are usually used to analyze the seismic reflection signals to assist the sedimentary cycle division. The high-resolution TFA results are beneficial to characterize the dominant frequency changes caused by the variations of the stratum thickness. The multisynchrosqueezing transform (MSST) is an iterative version of the synchrosqueezing transform (SST), which provides a more concentrated TF representation than the SST. So, the MSST is suitable for the sedimentary cycle characterization. However, it is a hard task to construct an appropriate basic wavelet. In this study, by combining the MSST and optimal basic wavelet (OBW), we proposed a multisynchrosqueezing OBW transform (MSOBWT) to help the sedimentary cycle division. The proposed method first defines the dominant frequency location condition (DFLC) that ensures the MSST to reassign the TF spectrum to the dominant frequencies position. Further, the OBW is introduced to construct the basic wavelet that satisfies the DFLC. Finally, the synthetic traces and field data are used to testify its effectiveness for the sedimentary cycle division.

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.

Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log interpretation is usually with low efficiency and bad consistency. Therefore, the automatic well log interpretation tools based on machine learning and deep learning have been developed. Although the state-of-the-art sophisticated models can show fine interpretation performance with acceptable accuracies, “blind” tests do not always exhibit satisfactory results because of the complexity of lithology interpretation with respect to subsurface rock properties and the data-labeling quality. To solve this generalization challenge, we propose to leverage the parameterized quantum circuits in the deep-learning model. The quantum computing takes advantages of the superposition and entanglement quantum systems, which could potentially endow the generalization power or capability to the deep-learning model. Using the proposed quantum-enhanced deep-learning (QEDL) model, we have tested the model performance on field well log data from different wells. Compared with the classic fine convolutional neural network (CNN) model and the long short-term memory (LSTM) model, the proposed QEDL model achieves comparable model performance with a clearly improved generalization power for interpreting both thin and thick lithology layers. In addition, because of the quantum circuit structure, the QEDL model needs much fewer model parameters than LSTM and CNN models, i.e., the QEDL parameter number in our study can be approximately 75% less than that of LSTM and 89% less than that of CNN.

Sedimentary cycle division is an important step for sequence stratigraphy analysis. For the division of sedimentary cycle using seismic data, a key issue is characterizing the changes of dominant frequencies caused by the changes of stratum thickness with high accuracy and high resolution. The synchrosqueezing transform (SST) can provide a time–frequency (TF) representation with high resolution by synchrosqueezing the TF spectrum, which helps the sedimentary cycle identification. Unfortunately, it is a hard task to choose an appropriate basic wavelet, which influences the accuracy of the SST to characterize the sedimentary cycle. To solve this issue, we construct a synchrosqueezing optimal basic wavelet transform (SOBWT) to optimally characterize the sedimentary cycle. We first propose a criterion to construct the basic wavelet of SST by deriving the dominant frequency location condition and defining the similarity coefficient condition of the basic wavelet. Then, we introduce the optimal basic wavelet (OBW) to construct the basic wavelet that satisfies the dominant frequency location condition and the similarity coefficient condition. Note that we term the SST with a basic wavelet that satisfies the dominant frequency location condition and the similarity coefficient condition as the SOBWT. Finally, we apply the proposed SOBWT to synthetic and field data to testify its validity and effectiveness and compare it with conventional SST-based methods. The application results illustrate that it is much more convenient and easier for the sedimentary cycle division based on the SOBWT results.

Continuous wavelet transform (CWT) is often used to extract the peak frequency attribute for characterizing the thin-bed thickness. Good joint time-frequency (TF) resolution is beneficial for the extraction of peak frequency. However, due to the Heisenberg’s uncertain principle, the time and frequency resolution of CWT cannot be obtained simultaneously. In this paper, combining the adaptive superlet transform and the optimal basic wavelet, a super-resolution optimal basic wavelet transform (SROBWT) is proposed to obtain the best joint TF resolution. The optimal basic wavelet matching the seismic wavelet is constructed as a basic wavelet of the adaptive superlet transform. Herein, taking the best joint TF resolution of the seismic wavelet as the target, a parameter selection method is proposed for the adaptive superlet transform. Furthermore, based on the proposed SROBWT and wedge model, a workflow is proposed to characterize the thin-bed thickness. The synthetic and field seismic data are employed to demonstrate the validity of the proposed methods. All the corresponding results show that the SROBWT has a better joint TF resolution than the conventional methods and the proposed workflow can correctly characterize the spatial variation of the thin-bed thickness, which is beneficial for further sediment sources analysis and reservoir prediction.

Wavelet and reflectivity inversion (WRI) is an important issue in seismic data processing. To overcome the ill-posedness of WRI inversion with more efficient parameter selection and better lateral continuity of reflectivities, we propose a new WRI algorithm named compact smoothness and relative sparsity (CSRS) algorithm, where a normalized compact constraint and a normalized smooth regularization is proposed for the wavelet inversion, and a relative sparsity constraint is proposed for the reflectivity inversion. The proposed constraints and regularization make the parameters of WRI easy to be selected. The proposed relative sparsity constraint can lead to a reflectivity profile with good lateral continuity as it can be suitable for various seismic data with a fixed sparsity parameter. We also propose an efficient algorithm for solving corresponding WRI optimization problem. The whole WRI problem is divided into reflectivity inversion subproblem and wavelet inversion subproblem by using alternating iterative method, where the initial wavelet is estimated by smoothing the absolute amplitude spectrum of averaged seismic data. The proximal algorithm is applied to solve both reflectivity inversion subproblem and wavelet inversion subproblem. By replacing Toeplitz matrix multiplication with fast Fourier transform (FFT) and using compact wavelet, our algorithm can be efficient for 3D seismic data. The numerical examples on 2D synthetic data, 2D offshore field data and 3D onshore field data demonstrate that, compared to Toeplitz-sparse matrix factorization (TSMF) algorithm, the CSRS algorithm with fixed default parameters can get high-resolution reflectivities with better lateral continuity, and requires much less computational time.

Traditional ensemble learning approaches explore the feature space and the sample space, respectively, which will prevent them to construct more powerful learning models for noisy real-world dataset classification. The random subspace method only search for the selection of features. Meanwhile, the bagging approach only search for the selection of samples. To overcome these limitations, we propose the hybrid incremental ensemble learning (HIEL) approach which takes into consideration the feature space and the sample space simultaneously to handle noisy dataset. Specifically, HIEL first adopts the bagging technique and linear discriminant analysis to remove noisy attributes, and generates a set of bootstraps and the corresponding ensemble members in the subspaces. Then, the classifiers are selected incrementally based on a classifier-specific criterion function and an ensemble criterion function. The corresponding weights for the classifiers are assigned during the same process. Finally, the final label is summarized by a weighted voting scheme, which serves as the final result of the classification. We also explore various classifier-specific criterion functions based on different newly proposed similarity measures, which will alleviate the effect of noisy samples on the distance functions. In addition, the computational cost of HIEL is analyzed theoretically. A set of nonparametric tests are adopted to compare HIEL and other algorithms over several datasets. The experiment results show that HIEL performs well on the noisy datasets. HIEL outperforms most of the compared classifier ensemble methods on 14 out of 24 noisy real-world UCI and KEEL datasets.