Seismic fault detection is one of the key steps for seismic structure interpretation, potential reservoir prediction, and geological hazard forecasting. Currently, the most prevalent methods involve training deep learning (DL) models to accelerate seismic fault detection. Although many DL-based fault detection methods have been proposed, most rely solely on seismic amplitude to train convolutional neural networks (CNNs). Consequently, they often struggle to accurately interpret faults in regions with low data quality or complicated structures. We propose the Dip integrated Spatial-Spectral UFormer (DSSUF) with the Auxiliary Semi-DSSUF Module (ASM) to improve the fault detection performance by integrating seismic reflector dip as an extra input. The proposed DSSUF is embedded with the Spatial-Spectral Augmentation Transformer (SSAT) block, the pixel shuffle and unshuffle, and a Dual-Dimension Reduction Module (DRM). By integrating DSSUF with ASM, our model can effectively extract features from seismic data and dip. The proposed method fuses features extracted from two input data by defining the hybrid loss function containing a supervised constraint and a feature constraint. We then evaluate the proposed DSSUF with ASM by applying it to synthetic blind test data and two 3-D field data. Quantitative and qualitative comparisons between baseline methods and our method indicate that the DSSUF with ASM provides accurate and continuous fault detection results. Applications on two field data also illustrate the excellent generalization performance of the proposed method.

Deep learning (DL) has been widely used to enhance the efficiency and accuracy of seismic facies classification. However, most DL-based seismic facies classification methods rely only on seismic amplitudes and require substantial labeled training data. We propose the Unconformity-enhanced Hierarchical Context Fusion Network (UHCFNet), which uses seismic unconformity attribute to increase the accuracy of seismic facies classification and reduce the amount of data required for model training. We first calculate the seismic unconformity attribute to highlight potential seismic facies boundaries. We then propose the UHCFNet by combining the Hierarchical Context Fusion Network (HCFNet) with an unconformity guiding branch to integrate the unconformity attribute as an additional constraint. The unconformity guiding branch incorporates the unconformity-aware Transformer block (UTB) to extract unconformity features. Next, we validate the performance of our proposed UHCFNet by applying it and two baseline DL models to the Netherlands F3 field data. Quantitative and qualitative comparisons illustrate that our proposed model provides more accurate seismic facies classification results than baseline DL models, especially for regions with complicated structures and minority facies with fewer seismic samples. Moreover, to further demonstrate the robustness of our UHCFNet, we train the proposed model and baseline DL models using only 5% of the original training dataset. Comparisons between different models indicate that our proposed UHCFNet has a lower dependency on large datasets, as the UHCFNet still provides accurate seismic facies classifications for regions with complicated structures with limited training data.

Seismic fault delineation and parameter prediction, including fault dip and strike, are vital for subsurface structure modeling, hazard assessment, and resource exploration. Although numerous deep learning-based methods have been developed for fault interpretation, there are few methods designed for fault parameter interpretation. The main reason is that extracting accurate fault parameter labels from the fault location label is a difficult task, especially for conjugate faults and dense fault systems. To address this issue, we propose a workflow for automatically generating 3-D synthetic seismic datasets with fault location, dip, and strike labels by using seismic reflection features from field data to enhance the realism of synthetic data. Afterward, we build the Dual U-shape Domain Fusion-Net (DUDF-Net), which is embedded with a multi-scale feature fusion block that integrates Haar wavelet transform and 3-D axial convolution to effectively fuse voxel and frequency-domain features, overcoming the limitations of spatial-domain-only approaches. Finally, we train the DUDF-Net using the generated synthetic dataset, and apply the well-trained model to the blind test synthetic dataset and two field seismic surveys. Both quantitative and qualitative comparisons between our method and baseline models illustrate that the DUDF-Net generates more accurate and robust results for fault delineation and parameter interpretation, especially for conjugate faults and dense fault systems.

The existence of coal seams suppresses other useful information, especially the below-thin layers, and is unfavorable for delineating the target reservoirs beneath them. The matching pursuit (MP) based methods are commonly used for removing strong reflections caused by coal seams. They first decompose a seismic trace into several wavelets based on a user-defined wavelet dictionary and then separate the most similar wavelet with the coal seam. However, how to define a complete wavelet dictionary and how to maintain horizontal continuity are two unsolved issues. We propose a multi-scale attention deep learning (MSADL) model for separating and removing seismic strong reflections. First, we suggest a workflow to generate a synthetic data set for model training based on the characteristics of field data and well logs. Next, we build an MSADL model by integrating the discrete wavelet transform (DWT) and convolutional block attention module (CBAM) into the widely used Unet. After model training, we apply the well-trained MSADL model to 3D field data in the Sichuan Basin, China for the separation and removal of strong reflections and characterization of the beneath target thin layers.

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.

Suppressing random noise is an effective way to improve the signal-to-noise ratio (SNR) of seismic data. Supervised deep learning methods have recently been widely applied to seismic image denoising. However, these methods require a large amount of noise-free data to train the network, which is unavailable in practical applications. Moreover, most of these denoising methods focus on removing random noise, assuming that the noise is zero-mean and independent of seismic signals. In field applications, real seismic noise often exhibits band-limited and spatial correlations. We propose an unsupervised learning method to train a denoising network using only noisy images, termed Pixel-shuffle Down-sampling and Visible Blind-Spots (PD-VBS). First, we propose to utilize Pixel-shuffle Down-sampling (PD) to destroy the spatial correlation of real seismic noise, followed by feeding the data into the blind-spots network. Next, we introduce additional input derived from the input data with a finer stride PD to compensate for the information loss induced by both the blind-spots network and PD mechanisms. Experimental results on both synthetic and field data show that our proposed PD-VBS can effectively remove noise while preserving valid signals, compared with traditional denoising methods and state-of-the-art deep learning models.

Seismic fault interpretation is a crucial task for hydrocarbon reservoir characterization, CO2geological storage, and geothermal energy evaluation. Deep learning (DL)-based methods have been conducted to accelerate seismic fault interpretation and studies have shown that the most practicable way is to train neural networks using synthetic data with ground truth labels. However, synthetic and field data are different in local seismic structures, seismic reflection characteristics, and seismic fault features. These differences would lead to the poor generalization of DL-based methods and unreliable fault predictions. We propose an automatic fault interpretation method with the aid of the Data-, Physics-, and Math-assisted synthetic data generation, including the Data-assisted module, the Physics-assisted module, and the Math-assisted module. The Data-assisted module provides structural features and reflection characteristics of seismic events. The Physics-assisted module provides seismic fault features from physical experiments. The Math-assisted module generates realistic synthetic data and ground truth fault labels based on the extracted features. We then propose the Multi-scale Attention-based Convolutional Neural Network (MSACNN), by combining a simplified deeplab module and attention mechanism. Finally, we train the MSACNN using the generated synthetic dataset. To illustrate the validity and generalization of the proposed model, we apply it to synthetic data and two 3D real seismic volumes. The superiority of the proposed method is experimentally demonstrated with the qualitative and quantitative comparisons of fault interpretation results using different methods.

Deep learning (DL) has been widely used for various geological tasks but not much for seismic unconformity estimation because of the limited labeled training data. We propose the physical knowledge constrained synthetic data generation workflow for building the synthetic training dataset, which mainly contains the Data-assisted module, the Physics-assisted module, and the Math-assisted module. The Data-assisted module is used to extract geometry and reflection features of seismic events from field data. The Physics-assisted module is used to provide geological knowledge related to seismic unconformity and define the unconformity geometry criteria for simulating unconformities in synthetic seismic images. The Math-assisted module is used to modify extracted features, create synthetic seismic images, and generate unconformity labels with the aid of the Data- and Physics-assisted modules. Therefore, incorporating these three modules makes the synthetic seismic datasets closely resemble field data. The synthetic datasets are used to train the DL model that we proposed, named ECA-UNet++, which integrates the UNet++ with the Transformer block and efficient channel attention (ECA). To validate the performance of our proposed method, we apply it to synthetic data and two 3-D field data. The results demonstrate the effectiveness of our proposed method for accurate and efficient seismic unconformity estimation, even when trained exclusively on synthetic data. Furthermore, we integrate the estimated unconformity results into seismic facies analysis, indicating that our method benefits other seismic interpretation tasks.

Geologic structure characterization is a key step for seismic structure interpretation, such as fluvial channels, faults, and fractures. The coherence attribute is a widely used tool for describing seismic discontinuities, which is usually calculated based on the similarity and dissimilarity of the adjacent seismic traces. However, accurately extracting coherence attribute is a difficult task in field data applications because seismic signal is one of the typical nonstationary, non-Gaussian, and wideband signals. To describe seismic discontinuities at different scales, we propose a workflow to extract the multiscale coherence (MSC) attribute. We first decompose seismic data into several band-limited intrinsic mode functions (IMFs) with different dominant frequencies via the multichannel variational mode decomposition (MVMD). Afterward, we develop a Cauchy kernel correlation-based coherence algorithm to extract the coherence attributes at different scales based on the decomposed IMFs. Finally, we can compute the MSC attribute by utilizing the calculated coherence attributes. Field data applications demonstrate that the proposed MSC attribute characterizes seismic discontinuities, such as faults and fluvial channels, more accurately and more clearly than the traditional coherence attribute and the 1-D variational mode decomposition (VMD)-based coherence attribute.

Random noise elimination acts as an important role in the seismic data processing. Moreover, protecting and recovering useful subsurface structure information are also significant. In this study, the S-mean that can obtain the geometric mean of the seismic traces on the symmetric positive definite (SPD) matrix manifold is adopted as a nonlinear filter for seismic denoising. Furthermore, S-mean has the best correlation with other elements based on the S-divergence due to the optimization of finding the S-mean on the SPD manifold. Therefore, the broken correlation features in noisy seismic data are compensated and maintained well, which can be conducive to describe the subsurface structures. Synthetic examples and field data applications qualitatively and quantitatively demonstrate the validity and effectiveness of the proposed workflow.

Seismic image denoising is essential to enhance signal-to-noise ratio (SNR) of seismic images and facilitate seismic processing and geological structure interpretation. With the development of deep learning (DL), several DL based models have been proposed for seismic image denoising. However, the commonly used supervised DL based denoising models require noise-free data as training labels, yet noise-free data is often difficult to be obtained in field application scenarios. By considering the similarity of seismic images, we propose a similarity informed self-learning (SISL) to address seismic image denoising in the absence of noise-free seismic images. To accurately preserve valid seismic signals when constructing training pairs, we develop a specialized workflow, termed the similar image sampler. In this way, we can fully use the self-similarity of noisy seismic images to build training pairs and then train a denoising model. Moreover, to effectively attenuate random noise, we propose a hybrid loss function with a regularization constraint to availably retain valid seismic events. After comparing with traditional denoising methods and several state-of-the-art unsupervised DL models, the experiment results from synthetic and field data quantitatively and qualitatively demonstrate the effectiveness and the stability of the proposed SISL model for seismic image denoising.

Attenuation of incoherent noise is an effective way to improve signal-to-noise ratio (SNR) of seismic data. Recently, supervised deep learning based methods have been widely utilized for seismic image denoising, which often need plenty of noise-free data as training labels. However, noise-free seismic data are often unavailable in field applications. We propose an unsupervised learning method (NS2NS) to train a denoising network by using single noisy seismic data. The proposed model is based on two basic truths of seismic data: (1) High self-similarity of seismic data; (2) Spatially independence of incoherent noise in seismic data. To implement the proposed method, we first build a sampling workflow to generate paired noisy images based on single noisy seismic image. Moreover, we create similar noisy images that are similar but different with the original noisy image by using the proposed self-similar sampler. The original noisy images and generated similar noisy images are then fused by using a suggested Bernoulli sampler to create new paired noisy images. These new paired noisy images are used as the input and target of the denoising model, respectively. Next, an end-to-end convolutional neural network (CNN) is built for seismic image denoising, which aims to learn features of valid signals and suppress unpredictable random noise. Finally, we apply the proposed NS2NS method to both synthetic and field data. The results show that our proposed method can effectively suppress incoherent noise while preserving valid signals.

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.

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.

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.

Traffic noise is often coupled with seismic signals when seismic data are acquired close to the road. The traffic noise usually exhibits high amplitudes in the recorded seismic profile, and it is a challenging task to remove it. In this article, we propose a workflow to attenuate seismic traffic noise using the${l}_{p}$-norm robust principal component analysis (RPCA). This method is implemented in the frequency domain, where the${l}_{p}$-norm RPCA is applied to each frequency slice and results in a low-rank approximation of seismic reflections. The alternating direction method of multipliers (ADMM) algorithm is included in the implementation for efficiency. The proposed workflow is demonstrated on a 3-D field shot gather contaminated by complex traffic noise. The performance indicates that our method can remove strong traffic noise effectively and, in turn, accentuate the seismic reflections with balanced amplitudes.