The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

Gravity inversion is the pioneer in exploring the structural characteristics of the Earth, the Moon, and other celestial bodies. Classical gravity inversion methods aim to estimate the 3D subsurface density distribution from the observed 2D surface gravity anomalies, which is an ill-posed problem. Constraints can provide vertical resolution and reduce uncertainty. However, these methods significantly increase the cost of data acquisition. This manuscript presents a novel joint inversion method to estimate subsurface density anomaly via a physics-inspired neural network. The observed signals in the proposed method are the gravity anomalies on multiple altitudes and their vertical gradients, which provide vertical resolution for gravity inversion. The proposed joint inversion method contains two stages. The proposed inversion model is initially pre-trained on the synthetic data. Self-supervised transform learning with a closed loop between inversion and forward models is applied to the target gravity anomalies and their gradients. The loss function is defined by mean absolute error, cross-gradient loss, and total variation. Experiments on independently and identically distributed synthetic data, as well as out-of-distribution field data, demonstrate the effectiveness of the proposed method.

Pre-stack seismic inversion is an effective approach to obtain elastic parameters for reservoir characterization in seismic exploration. However, the difficulty in achieving reliable inversion results in pre-stack seismic inversion remains due to the strong nonlinearity of the problem and the ambiguity of solutions. Deep learning (DL) excels at mapping the complex nonlinear relationship, and thus various DL-based methods have been used in seismic inversion. To address challenges posed by the nonlinearity and ambiguity in seismic inversion, a novel DL-based pre-stack seismic inversion constrained by amplitude versus offset (AVO) attributes is proposed. In this approach, the multitask learning strategy is adopted to construct a deep neural network that allows for the simultaneous processing of multiple related tasks through information shared between tasks. Moreover, the theoretical seismic forward modeling is integrated with the neural network training, enabling semisupervised learning and utilizing unlabeled data. Additionally, To mitigate the ambiguity of solutions, AVO attributes including intercept P and gradient G are introduced as constraints in the neural network training process. Experimental analyses show that the proposed method can obtain superior inversion results on both synthetic and real examples. Compared with other DL-based methods, the mean squared error of the proposed method’s inversion results on examples drops by at least 30%. Besides, the proposed method can effectively improve spatial continuity and preserve more details laterally in the field data example.

Magnetic inversion aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning (DL) methods have been widely utilized in lots of geophysical fields including magnetic inversion. However, these methods rely heavily on synthetic training data, whose performance is limited since the synthetic data is not independently and identically distributed with the field data. Thus, we proposed to realize magnetic inversion by self-supervised learning. The proposed self-supervised knowledge-driven method for 3D magnetic inversion (SSKMI) learns on the target field data by a closed loop of the inversion and forward models. Given that the parameters of the forward model are preset, SSKMI can optimize the inversion model by minimizing the difference between observed and re-estimated surface magnetic anomalies. Besides, there is a knowledge-driven module in the proposed inversion model, which makes the DL-based method more explicable. Meanwhile, comparative experiments demonstrate that the knowledge-driven module can accelerate the training and achieve better results. Since magnetic inversion is an ill-pose task, SSKMI proposed to constrain the inversion model by a guideline from a well log, seismic waves, or electromagnetic signals. The experimental results demonstrate that the proposed method is a reliable magnetic inversion method with outstanding performance.

As an important elastic parameter, seismic acoustic impedance is usually obtained through poststack inversion. However, there are usually two problems that limit the quality of the inversion results. First, conventional inversion methods typically use regularization terms to enhance the stability of the inversion results, and effective regularization terms are particularly important for accurately inverting seismic impedance. Second, most inversion methods adopt a trace-by-trace inversion strategy, resulting in poor lateral continuity when connecting the inversion results of all traces into a 2-D profile, especially for processing noisy data. To address these two problems, we propose a structure-oriented minimum entropy stabilizer for acoustic impedance inversion that enhances the lateral continuity of the inversion results while restoring the blocky structures of the strata and improving the resolution of the inversion results. The stabilizer consists of a structure-oriented regularization (SOR) operator and the minimum entropy norm. The SOR operator is constructed using the local dip estimated from the seismic data by the plane-wave destruction (PWD) algorithm and constrains the inverted impedance along the structural trend, making it more consistent with geological features. The minimum entropy norm restores the blocky structures and enhances resolution by imposing sparse constraints on the temporal and spatial derivatives of the impedance. Based on synthetic and field seismic data, we compare the inversion results of the proposed method with those of conventional Tikhonov regularization and total variation (TV) regularization methods. The results show that the proposed method exhibits superior performance, especially in processing noisy data.

Seismic data acquisition is an indispensable step in seismic exploration, whose cost takes up a large proportion of seismic exploration. The cost of seismic data acquisition has limited the development of industrial manufacturing. The compressed sensing method can obtain high-quality seismic data with less random sampling. Recently, deep learning (DL) based compressed sensing methods have achieved outstanding performance in the reconstruction of seismic data with randomly missing traces. However, most existing DL-based methods focus on the 2D seismic data. The obstacle to applying deep learning to the reconstruction of 3D seismic data is the lack of high-quality training data. Self-supervised learning can overcome the lack of high-quality training data. Nevertheless, the time cost is the biggest obstacle preventing the application of self-supervised learning methods. To solve the above issues, we propose a fast self-supervised learning method for the reconstruction of 3D seismic data. The proposed method learns from the observed seismic data directly by sub-sampling. Besides, 3D lightweight gated convolution layers are utilized for highly efficient reconstruction of the input seismic data with randomly missing traces. Meanwhile, the proposed method employs a global waveform extractor based on a fast Fourier transform to extract global waveform. The synthetic and field experiments have demonstrated that the proposed method has a remarkable reconstruction performance with high efficiency.

Seismic inversion is a critical research area in seismic data interpretation. Given the powerful feature extraction and representation capabilities of deep neural network (DNN), it has been widely adopted in the seismic reservoir parameters inversion. However, the majority of DNN-based inversion methods use 1-D models due to the scarcity of well-logging labels, which are only 1-D time series. The performance of higher-dimensional DNN-based inversion methods depends on the quality of the initial inversion results, leading to an interdependence between the model and data in the time and space dimensions. Here, we propose a model and data co-evolution method for seismic reservoir parameters inversion. It employs a 1-D DNN model-based closed-loop model to generate initial reservoir inversion results. Then, the evolutionary 2-D model learns spatial structural features constrained by the initial reservoir inversion results to improve the spatial continuity. We tested the proposed method on synthetic seismic data with multiple fault structures, achieving the lowest inversion error and highest inversion accuracy. It also exhibits the highest accuracy in real seismic data with the structural features of underground rivers being more pronounced.

Seismic stratigraphic interpretation plays an important role in geophysics and geosciences. Recently, deep learning has been explored for seismic stratigraphic interpretation. However, deep learning-based interpretation methods usually require sufficient labeled samples. This is often too hard to be satisfied in field seismic interpretation. In this paper, we propose a deep active learning-based method to address this issue. Active learning typically exploits prediction uncertainty to reduce labeling effort. We found that uncertainty of prediction is easily obtained in the field of seismic interpretation. Since adjacent seismic images are very similar, they should have similar predictions. When the model performs poorly, the predictions of adjacent images will differ significantly. Thus, the uncertainty can be easily obtained by measuring the similarity of the predictions of adjacent seismic images. Then, data with the highest uncertainty is annotated by geological expert and used for the next round of training. For few-shot active learning, initial models obtained by different initial training sets are quite different. We combine deep clustering and uncertainty sampling to select initial training datasets, with which a good initial model can be obtained. To improve generalization, we introduce a random thin plate spine transformation to simulate changes of terrain. We apply the proposed method to the F3 field seismic data. The results demonstrated that the proposed method can effectively improve performance of learned seismic interpretation network with very limited labeled samples.

Seismic elastic parameters inversion is a method to predict geophysical reservoir parameters, including P-wave velocity, S-wave velocity and density, by using pre-stacked seismic data. Deep learning (DL) techniques have been utilized to establish complex and nonlinear inversion model. However, these DL-based inversion methods have some limitations. For instance, they often overlook the complementary observation distance information in pre-stacked seismic data at different incident angels, and they do not always consider the spatial structure and physical information conditions. As a result, the inversion solutions may be prone to falling into local minima. In order to alleviate this issue, we propose a spatial pattern learning method for pre-stacked seismic inversion. First, multi-view convolutional neural network is used to extract more complementary high-dimensional features of pre-stacked seismic data, which implies the spatial observation distance pattern of the input data. Second, the dip structure loss item is used to ensure the structural consistency between inverted results and seismic data, which constrains the spatial continuity. Third, forward physical constraint item improves the physical interpretability of inversion results. In addition, forward reconstruction result and estimated dip structure result can be used to automatically evaluate inversion results on unlabeled data. The proposed approach has been proven in enhancing the inversion accuracy and spatial continuity based on experimental results from both synthetic pre-stacked seismic data and real pre-stacked seismic data.

Reservoir prediction is a significant issue in seismic interpretation, and it is difficult to reach a tradeoff point for the reservoir prediction accuracy and spatial continuity. Nowadays, though numerous machine learning methods have been widely applied in reservoir prediction, so few available well-logging labels are still a major obstacle for improving prediction performance. Considering for such a critical factor, we propose a semisupervised deep-learning framework, in which the closed-loop convolutional neural network (CNN). and virtual well-logging labels are used. The closed-loop CNN, which is consisting of the predictive and generative subnetworks, can be trained directly by using the seismic attribute data not only with well-logging labels but also without well-logging labels. The virtual well-logging labels (Vl) are generated by fusing the results of two existing reservoir predicting methods, one based on polynomial linear regression and the other based on CNN. Vl contributes to improve the spatial continuity and accuracy of the predicted reservoir as constraint items in network training process. Finally, cross-validation experiments on real-field data are carried out, and 3-D field reservoir prediction results show that the proposed method outperforms several existing machine-learning-based methods.

Deep learning (DL) methods have been widely applied in seismic inversion. However, one of the major challenges for DL-based seismic inversion is the time-shifted well-logging labels, which is resulted by the inaccurate time–depth relationship estimation during seismic well tie. Also, time-indexed phenomena of time-shifted well-logging labels may be squeezed, stretched, time ahead, or time lag, which can be considered as a typical noisy label problem in the DL field. In order to tackle the problem, we propose a dynamic time warping (DTW) loss-based closed-loop convolutional neural network (CNN) for seismic impedance inversion. First, DTW loss and cycle-consistency loss together constrain the closed-loop CNN training to optimize the weights of neural network. Second, the well-logging label will be corrected by warping the original well-logging label with the aligned path matrix during the iteration learning procedure, and the iteration termination criterion is reached if the similarity between the corrected well-logging label of the last iteration and that of the current iteration is larger than a given threshold. Third, the DTW error is suggested as the reasonable evaluation index in the blind-well test due to the time shift phenomena inevitably existed in the blind well. The experimental results on both synthetic data and real data demonstrate that the proposed method can effectively improve the inversion accuracy and spatial continuity.