Seismic horizon picking is vital in seismic interpretation, forming the foundation for reservoir exploration and seismic inversion. Traditional horizon picking methods heavily depend on geologists’ experience, making the process time-consuming, labor-intensive, and prone to subjective interpretation. The emergence of deep learning (DL) offers new solutions for horizon picking. However, conventional DL models often struggle to segment seismic horizons accurately when faced with limited training samples and low-quality labels. To address these issues, we suggest a geological simulation modeling approach to generate synthetic data sets with field features for model training. Afterward, we suggest an adaptive weighted feature fusion multi-task LSTM (AWFF-MT-LSTM) network, which treats horizon picking as two tasks, i.e., stratigraphic boundary segmentation and target waveform detection. Moreover, we propose the AWFF layer that balances the relationship between two tasks and shares feature information across two tasks to enhance model convergence speed. Finally, we apply our methods to field data to validate their feasibility.

The stratigraphic sequence correlation of well logs is a crucial step for reservoir investigation. The manual interpretation is a commonly used method, and the experience of geophysicists influences it. Recently, deep learning (DL) has been introduced for automated stratigraphic correlation of well logs. However, building a complete training dataset with high-quality labels is challenging and time-consuming. In this study, we propose an automated stratigraphic sequence correlation method called OMP-KANFormer, which integrates the Kolmogorov-Arnold network with the transformer (KANFormer) model with orthogonal matching pursuit (OMP)-based data augmentation. To solve the problem of training dataset generation, we first propose a data augmentation algorithm based on OMP to simulate waveform features and generate a large number of synthetic well logs as training data. We suggest combining the KANFormer to accurately segment the stratigraphic. The KAN effectively models complex nonlinear relationships, while the transformer excels at capturing long-range dependencies in the features of well logs. Finally, we apply the proposed data augmentation method and KANFormer to a well-log dataset, demonstrating their validity and effectiveness via a comprehensive ablation study and comparisons with widely used SegNet and U-Net.

Horizon picking is of paramount importance in seismic interpretation because it has a significant impact on subsequent interpretation and inversion. Although manual and various automatic interpretation methods have been widely used for horizon picking, they still have several problems, such as being time-consuming and highly dependent on human experience. Recently, deep-learning (DL) methods have been implemented to solve these problems. However, traditional convolutional neural networks (CNNs) have a shortage of capturing global features, and vision transformers, recently proposed, aim to address this. To segment the seismic horizon accurately, we suggest a dense inception transformer (DIFormer) by combining the dense extreme inception network (DexiNet) and the inception transformer network. When implementing model training with the patch technique, the DIFormer can retrieve more information and interpret horizons smoothly. Furthermore, we utilize multiple attributes computed from seismic data to train basic DIFormer models and then adopt ensemble learning to obtain the fusion model, that is the ensemble DIFormer (EDIFormer). We implement qualitative and quantitative analyses to verify the effectiveness of the suggested model and compare it with the state-of-the-art (SOTA) SegFormer and basic DIFormer trained with a single attribute in trace- and patch-based modes.

With the rise of deep learning (DL), there are kinds of DL-based models proposed for addressing seismic data reconstruction, especially convolutional neural network (CNN)-based methods. However, most of these models are supervised, which require a large amount of training data and ground-truth labels that are difficult to obtain in real applications. We propose the polarity-constrained dual-cycle generative adversarial networks (PCDC-GANs) for reconstructing the irregularly sampled seismic data. We first adopt a traditional generative adversarial networks (GANs) framework as the base model, integrating with a dual-cycle mechanism. Instead of the conventional generator network, we suggest a self-prior generator that uses the prior information extracted by the generator in the first stage to enhance the reconstruction performance, i.e., the mask data of the irregularly sampled seismic data. In addition, we introduce polarity constraints to promote the recovery of sampled seismic traces, amplifying the model’s sensitivity to the locations of missing traces. Furthermore, our model incorporates various loss functions to promote its convergence. Numerical experiments on synthetic and field data show that our PCDC-GANs can reconstruct irregularly sampled seismic data more accurately than the SuperstarGAN and CycleGANs, and achieve comparable performance to the supervised U-Net.

The stratigraphic correlation of well logs is crucial for characterizing subsurface reservoirs. However, due to the complexity of well logs and the huge amount of well data, manual correlation is time- and resource-intensive. Hence, various computerized stratigraphic correlation methods have been developed, especially regarding convolutional neural networks (CNNs). Recently, Transformer, a self-attention system that evolved from Natural Language Processing (NLP), has attained state-of-the-art performance over CNNs in a variety of domains because of its ability to perceive global features. We propose the Axial and Sliding window based attention with High-resolution Transformer (ASHFormer), combining the High-Resolution Network (HRNet) with an Axial and Sliding window self-attention Block (ASBlock) intended for stratigraphic correlation of well logs. ASBlock includes three different forms of Multi-Head Self-Attentions (MHSA), including sliding-window attention, horizontal-axis attention, and vertical-axis attention, therefore, it is possible to retrieve well logs’ long-range and local information. The experiments show that ASHFormer predicts more accurate stratigraphic correlation results than HRNet and CMT (a Transformer combining CNN and self-attention). The usefulness of the Transformer for well log feature extraction and automatic stratigraphic correlation is demonstrated by ASHFormer’s 9.74% improvement in correlation accuracy over HRNet with the same architecture.

Microseismic event picking is one of the key steps in seismic processing and imaging. Manually picking is a widely used way to pick the microseismic events, which is time-consuming. The standard short-term average/long-term average (STA/LTA) is a traditional method to pick the microseismic first arrivals, which would lead to inaccurate first-arrival picks in case of low signal-to-noise ratio (SNR). We developed a workflow to automatically pick the microseismic first arrivals by using the feature pyramid networks (FPNs). To train the proposed model, we first randomly select part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment every selected trace into two parts based on the time index of the manual picking and then assign each part a label. Afterward, we train the proposed fine-tuning FPN model by using the training data and the corresponding labels. It should be noticed that we proposed a loss function, named the point-aware loss, for solving the microseismic first-arrival picking issue. Finally, we predict the microseismic first arrivals by using the well-trained fine-tuning FPN model. The numerical examples demonstrate that our proposed model successfully identifies the microseismic first arrivals. The microseismic first arrivals predicted by using our proposed model are more robust and more accurate than those obtained by using the STA/LTA and the encoder–decoder network.

Seismic data reconstruction is one of the essential steps in the seismic data processing. Recently, the deep learning (DL) models have attracted huge attention in seismic exploration, which has been applied to seismic data reconstruction, especially the convolutional neural network (CNN)-based methods. However, the general CNN-based models only consider seismic features in the time domain and do not take into account the frequency features. Moreover, there are detailed features lost due to the downsampling scheme. We propose a wavelet-based residual DL (WRDL) network to reconstruct the incomplete seismic data. By selecting the U-Net as the backbone, we introduce the discrete wavelet transform (DWT) to replace the pooling operations, whose invertibility property benefits reserving the detailed features. Furthermore, the inverse wavelet transform (IWT) with the expansion convolutional layer is introduced to restore the feature maps. In addition, we adopt the residual blocks into the proposed model to promote the training accuracy and avoid the overfitting issue. To accurately and effectively reconstruct the missing seismic data, we propose a hybrid loss function based on the structural similarity (SSIM) loss and the Huber loss. Numerical experiments on synthetic data and field data show that the WRDL model reconstructs the missing seismic data more accurately than the U-Net and MWCNN models, including the irregularly missing seismic data and the consecutively missing seismic data with a big gap. Furthermore, the qualitative and quantitative results demonstrate the advantages of the proposed hybrid loss function over the commonly used traditional loss for seismic data reconstruction.

Instantaneous seismic attributes are commonly used in assisting seismic interpretation and stratigraphy analysis. We compute the instantaneous seismic attributes using 1-D seismic traces and the corresponding quadrature (Hilbert transformed) traces. However, the 1-D seismic trace and the corresponding quadrature trace are sensitive to noise and seismic processing artifacts. To improve the lateral continuity of instantaneous seismic attributes, we propose to compute instantaneous attributes using multichannel seismic traces. Dynamic time warping (DTW) is used to align the seismic traces centered at the analysis point which mitigates the effect of structure dip on the multichannel complex seismic trace analysis (MCSTA). We fine interpolate the 1-D seismic traces to minimize the possible error in the computation of the error matrix of DTW. We also define a constraint in the backtracking of DTW to avoid severe strain (stretch or squeeze) between seismic traces. We obtain the “robust” 1-D complex seismic trace by applying Gaussian smoothing to the aligned complex seismic traces. We finally obtain instantaneous seismic attributes from the smoothed 1-D seismic trace and the corresponding quadrature trace. We show the superiority of new instantaneous attributes by applying our method to real seismic data.

To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

In[1], the grant number in the first footnote for the National Postdoctoral Program for Innovative Talents should be BX20190279.