With the deepening of the Internet of Things (IOT) and industrial digital transformation, the core of positioning services is shifting from “serving people” to “connecting everything”, which poses a comprehensive challenge to indoor positioning technology in terms of high accuracy, low latency, low power consumption, and low cost. Traditional radio frequency positioning technology has shown many limitations in this context, while visible light positioning (VLP) technology has become a highly promising supplementary solution due to its unique advantages, such as the absence of authorized spectrum, no electromagnetic interference, high security, and the ability to balance lighting. However, traditional VLP methods heavily rely on accurate channel models and are difficult to cope with complex non-line-of-sight environments, resulting in increasingly prominent performance bottlenecks. In recent years, the rapid development of machine learning technology has provided a new paradigm for solving the above-mentioned problems. From the perspective of the IOT and edge computing, this paper systematically summarizes the latest progress of how machine learning can improve the performance of indoor VLP.We first elaborate on the architecture and basic principles of edge-oriented VLP systems. Then, a comprehensive review and comparison are performed on VLP methods based on traditional machine learning and deep learning. Moving on, we provide the analysis on how they improve system accuracy and robustness through data-driven approaches. Moreover, this article delves into the application and value of different learning paradigms, such as centralized learning, online learning, and federated learning in VLP systems. In addition, we have developed a multi-dimensional evaluation system that includes core positioning accuracy and edge performance indicators to comprehensively measure the feasibility of the system in practical deployment. Finally, we present the identified challenges and future research directions in this under-explored field from aspects of standardized scenario modeling, high generalization base models, dynamic environment robustness, heterogeneous terminal adaptation, and edge lightweight models.

The quality of seismic images is often affected by the limitation of acquisition conditions and the interference of noises, which causes the low resolution of seismic images and misleads the following geological interpretation. Although the super-resolution method for seismic images based on convolutional neural network (CNN) has behaved well, the quality of weak events especially deep events is still need to be improved, due to CNN is limited by the receptive fields, which results in weaker ability to perceive relationships among pixels far apart. In this letter, we solve this problem by designing a combination network of CNN and transformer (CNCT). CNCT consists of three parts, edge feature fusion block (EFB), deep feature mining block (DMB), and feature enhancement block (FEB). The EFB aims to fuse the input low-resolution (LR) image and the corresponding edges obtained by the Sobel algorithm and performs preliminary shallow feature extraction. DMB mines deeper features by stacking residual blocks, and each residual block makes full use of its excellent perception of global and local information by combining transformer and CNN. Finally, the FEB uses subpixel convolution for upsampling to expand the size of feature maps. The experimental results on synthetic data and field data show that CNCT not only behaves better on perception effect and texture details than that of other deep learning (DL) methods but also can suppress noise and improve the dominant frequency.

The seismic data reconstruction techniques are primarily used to address data missing or damaged due to human or environmental factors under restricted acquisition conditions and thus enhance the accuracy of obtained stratigraphic information. Hence, seismic data reconstruction stands as a crucial preprocessing step and is necessary for the effective exploration of subsurface resources. The existing reconstruction methods often fall short of fully utilizing the information existed in seismic data, thereby impacting reconstruction accuracy of effective signals. To overcome the aforementioned limitation, we propose a hierarchical guided multicascade feedback network (HGMFN), which facilitates comprehensive interaction of seismic data across different resolutions by learning intricate features from clean and complete seismic data at various scales. The proposed network achieves progressive integration of features along with layer-by-layer guidance and multilevel feedback mechanisms, accomplishing the reconstruction task and improving the processing precision. Experiments conduct with both synthetic and field data have confirmed the accuracy and performance of HGMFN in complex seismic data reconstruction.

Distributed optical fiber acoustic sensing (DAS) is a rapidly developed seismic acquisition technology with the advantages of low cost, high resolution, high sensitivity, small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by the DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning (DL) seismic interpolation methods based on convolutional neural networks (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this article, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer (UST) blocks. In the encoder and decoder part, the multihead self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The UST blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we introduce the SSIM loss to optimize the training process of SIT. In experiments, this proposed SIT outperforms A-Net and swin-transformer (ST). Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function.

Full waveform inversion (FWI) is currently the most accurate technique for obtaining the properties of subsurface media. The absence of low frequencies in the observed data caused cycle-skipping phenomenon and poor initial model which affect the convergence of FWI. We propose a global-feature-fusion and multi-scale network (GM-Net) in a way of supervised learning to compensate for the absent low frequency components in the observed data trace by trace. The difficulty of extrapolating frequency is to achieve smoothness and continuity when changing from high frequency signals to low frequency signals, which is visually shown in the reduction and movement of the sidelobes in high-frequency signals and the overall oscillation of the signals is slowed down. For achieving better extrapolation, the encoder-decoder architecture with multi-scale feature extraction is designed as the backbone of the network. For avoiding the loss of information, we propose to perform 1/2 down-sampling on the original input signal separately based on the odd and even time samples, and then concatenate them along the channel dimension. Since 1-dimensional (1D) seismic data is a type of time-series signal and the wavelengths of low frequencies are long, we pay more attention to the relevance of contextual information. Thus, dilated convolution layers, gridding convolution blocks and non-local attention blocks are used to enlarger the receptive field both in time and channel dimensions to extract and fuse global features. Numerical tests both on synthetic data and different types of field marine data demonstrate the feasibility and generalization of our method.

Recorded seismic data often is characterized by a low signal-to-noise ratio (SNR) that can hinder subsequent imaging and interpretation tasks. Thus, it is necessary to explore a method to recover weak signals from strong background noise. While numerous studies have demonstrated the effectiveness of deep-learning methods in seismic noise attenuation, enhancing their capability to recover weak signals under low SNR conditions remains an area for further exploration. To address this issue, we propose an edge-feature-guided wavelet U-Net (EFGW-UNet). In this novel architecture, we utilize the discrete wavelet transform to replace the pooling operation deployed in the conventional U-Net, thereby maintaining more detailed information of effective signals. Meanwhile, we also design a dual decoder for edge detection to obtain the shape and edge information on the effective signals. Finally, to fuse multi-level image features and edge features, an attention feature fusion module is deployed. In the experimental part, we use synthetic and real data to illustrate the effectiveness of EFGW-UNet. Our results suggest better denoising performance than competitive methods, especially for weak signal recovery submerged in heavy noise.

In marine exploration, conventional migration algorithms based on primary reflections often have poor angular illumination, especially in shallow areas and complex salt boundaries. Source density is the primary controller of angular illumination. It is well-known that source density is relatively sparse in a typical marine seismic acquisition, such as in a towed streamer survey. In contrast, imaging of multiples treats each receiver as a virtual source, mimicking a high-density source survey. Imaging of multiples can provide additional angular illumination and help to improve subsurface imaging. The extra illumination provided by multiple reflections enhances angle-domain common-image gathers (ADCIGs), a component of velocity model building and amplitude versus angle (AVA) analysis. Our main objective is to illustrate the advantages of angular illumination from imaging of multiples in the angular domain. To achieve this purpose, we propose a workflow for calculating high-quality ADCIGs for imaging of multiples. The workflow mainly contains up-going and down-going wavefield decomposition and the stabilized Poynting vectors for calculating more accurate imaging angles. To illustrate the accuracy of the proposed workflow, a simple model is used to calculate angle gathers for imaging of multiples. Then, the Sigsbee2b model and a field dataset from the Gulf of Mexico are used to compute angle gathers by imaging primaries and multiples to demonstrate the improved angular illumination achieved when multiples are also used for imaging. The comparison results show that the angle gathers obtained by imaging of multiple has better angular illumination, especially in shallow areas and complex salt boundaries.

In seismic exploration, erratic noise is a type of intense and complicated interference with large-amplitude and non-Gaussian distributions. The presence of erratic noise has been demonstrated to corrupt reflection events, adversely affecting the identification of effective signals. Nonetheless, conventional and time-frequency thresholding denoising methods based on the least-squares scheme usually assume that the seismic random noise has a Gaussian distribution, which is not the case for erratic noise. Therefore, the attenuation for erratic noise is challenging, owing to the deviation from the assumptions of the conventional methods. The recent application of convolutional neural networks (CNNs) to seismic data processing has yielded promising results. However, these CNN-based frameworks always have limited feature-interaction capability, resulting in the degeneration in denoising performance when coping with intense erratic noise. To address this issue, a novel joint-guided denoising network (JGD-Net) is proposed in this study. Unlike conventional CNN frameworks, JGD-Net uses a joint-guided scheme and attention mechanism to enhance the denoising capability. We generate synthetic records using published geological models such as Marmousi and salt dome to compose our training dataset. Furthermore, a novel loss function based on L1 norm and hyperbolic tangent function is designed to further ensure the optimization process of the training procedure and ease the influence of abnormal energy of erratic noise. Both synthetic and field data are processed sfor the evaluation of denoising performance. Compared with other popular methods, JDG-Net shows advantages in attenuating intense erratic noise, particularly under extremely low signal-to-noise ratio (SNR) conditions.

The super-resolution reconstruction for seismic images obtained by multistep processing of field data is essential due to the noise contamination, sparse geometry, and low dominant frequency of events, which impairs the subsequent seismic interpretation. Deep learning-based methods show strong potential in super-resolution through supervised learning. Generative adversarial networks (GANs) have shown capability in super-resolution of different kinds of images; however, it is limited in enhancing the detailed geological structures of seismic images that are fatal for interpretation. To address this issue, we propose a super-resolution hybrid bilateral attention GAN (SHBGAN) to improve the recovery of weak signals and the reconstruction of geological structures. Specifically, the generator employs hybrid and bilateral attention modules (BAMs) to enhance the capture ability of global and local features. Meanwhile, we use dilated convolutional layers instead of batch normalization (BN) layers in the residual block to improve the generalization ability of the trained model. Meanwhile, the discriminator employs global average pooling and convolutional layers to score the authenticity of seismic images rather than the probability to enhance the stability of training. In addition, we add the mean structural similarity (MSSIM) term to the loss function of generator to improve the perception quality of predictions. The numerical tests on both synthetic and field data show that SHBGAN is more effective than competing methods in recovering weak signals and reconstructing subtle faults.

Distributed optical fiber acoustic sensing (DAS) is an emerging acquisition technology in seismic exploration. However, DAS records are always affected by the complex background noise, resulting in a low signal-to-noise ratio (SNR). In addition, the DAS background noise has different properties from the noise existing in conventional seismic data. Thus, conventional denoising methods may degrade the record when dealing with complex DAS data. To improve the denoising capability, a novel denoising network, called residual modular cascaded heterogeneous network (RMCHN), is proposed. In general, the network is based on the idea of heterogeneous convolution and modular convolutional neural networks. Specifically, different modules are designed to extract the discriminatory features of the DAS data through effective information integration. On this basis, heterogeneous convolution combined with long and short path feature learning strategy is employed to fuse the captured features, thereby improving the feature expression capability and avoiding the information loss. Both synthetic and field denoising results indicate that RMCHN can suppress the DAS background noise with excellent performance in signal restoration, even for the weak signals form deep strata.

The formulation of conventional least-squares reverse time migration (LSRTM) starts with the forward modeling process; as a result, the migration operator of it presents as a migration process with a cross correlation imaging condition (CCIC). Since the imaging results produced by CCIC usually contain undesirable components (e.g., strong backscattering noise), it can be assumed that the primary target of the conventional LSRTM is to fit input data rather than produce high-quality imaging results; therefore, conventional LSRTM can be considered as a modeling-driven algorithm. To mitigate the desirable component in the imaging results, additional efforts should be spent in the process of modeling-driven LSRTM. To improve the performance of the LSRTM, we develop a migration-driven LSRTM by formulating the migration process using the inverse scattering imaging condition (ISIC) first. To guarantee the convergence of the algorithm, an adjoint modeling operator and a data precondition operator are incorporated in this migration-driven LSRTM. Since the ISIC can effectively eliminate the backscattering noise, this migration-driven LSRTM can produce high-quality images without the influence of that. After two synthetic data tests, this approach is applied to a 2-D streamer field dataset from the Gulf of Mexico. These tests indicate that, compared to the modeling-driven LSRTM, the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently.

Distributed acoustic sensing (DAS) has been considered as a breakthrough technique in seismic data collection owing to its advantages in acquisition cost and accuracy. However, the existence of complex background noise combined with a tough exploration environment always results in incomplete data with a low signal-to-noise ratio, posing a big challenge for the subsequent processing of DAS data. To improve the quality of DAS data, convolutional neural networks (CNN) have gradually been utilized to deal with the denoising and reconstruction tasks. Meanwhile, some successful applications have verified that CNN-based methods can significantly alleviate the impacts of DAS background noise and missing trace records, compared with conventional approaches. Nonetheless, in most researches, the denoising and reconstruction tasks are accomplished independently, severely affecting the processing efficiency. In this study, a multi-scale encoder-decoder network (MEDN) is proposed to simultaneously achieve the DAS background noise suppression and weak signal recovery through a unified model. Generally, MEDN can extract the different-scale features through both a multi-scale network architecture and a multi-scale residual (MSR) block. The captured different-scale features are then fused to enhance the effective feature. In addition, the encoder-decoder scheme is also utilized in the design of the network architecture to further enhance the reconstruction performance. Moreover, depthwise separable convolution (DSC) blocks are also utilized to ease the computational burden and improve the processing efficiency. Theoretical and field data processing results show that MEDN can provide better denoising and reconstruction performance than conventional methods and popular CNN-based frameworks.

Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.

Recently, distributed optical fiber acoustic sensing (DAS) is regarded as a transformative technology in seismic exploration. However, both various complex background noise and weak desired signals significantly limit its practical application. To explore an effective denoising method for the vertical seismic profile (VSP) record received by DAS, we propose an improved residual encoder–decoder deep neural network (RED-Net) enhanced by deep iterative memory block (DMB) and channel aggregation block (CAB), called residual channel aggregation encoder–decoder network (RCEN). Here, DMB uses the weight accumulation theory to improve the feature extraction ability and achieve accurate noise elimination. Meanwhile, CAB, using the multi-channel analysis architecture, enhances the weak signal retention performance. In addition, we leverage both the synthetic data obtained by forward modeling and real DAS noise data to construct a sufficient training dataset with high authenticity, thereby meeting the requirement of network training. Both the synthetic and field DAS-VSP data processing results demonstrate the advantage of RCEN compared with competing algorithms, including singular value decomposition (SVD), conventional RED-Net, and feed-forward denoising convolutional neural network (DnCNN).

Seismic prospecting is a common method used in oil and gas resource exploration. However, due to the limitations of current collection techniques, seismic records acquired in the field are typically contaminated by severe incoherent noise, which has negative implications for the subsequent processing and interpretation procedures. In addition, numerous traditional denoising algorithms have been applied in order to mitigate this problem, but further improvements are required, especially for the seismic data with spectral overlapping between effective signals and background noise. In recent years, feedforward denoising convolutional neural networks (DnCNNs) have been applied to suppress the complex random noise, and a series of essential insights have been gained. Nonetheless, conventional denoising networks always extract data features depending on single-scale information, resulting in impaired performance when coping with seismic records with a low signal-to-noise ratio (SNR). For solving this problem, a novel multiscale DnCNN (MSDCNN) is developed as an attempt for random noise suppression. Unlike conventional DnCNN, MSDCNN has a hierarchical structure capable of extracting features at different scales and capturing informative and discriminatory features through effective information integration. Meanwhile, the cross-scale feature interaction also increases the processing accuracy when confronted with weak reflection events. Experimental results derived from both synthetic and field data indicate that the proposed network can effectively suppress the random noise and accurately preserve reflection events, even under low SNR conditions.

Seismic background noise affects the recognition of reflection signals, thereby impeding the subsequent seismic data processing, such as seismic imaging and inversion. In addition, seismic background noise has relatively complex properties, such as non-stationarity and spectral aliasing, which can be further hampered with the deterioration of the exploration environment. Deep-learning methods have been successfully applied to effectively attenuate complex seismic noise and have shown significant improvements over conventional denoising methods. However, most denoising networks only utilize single-scale features, resulting in poor performance when confronted with seismic data in a low signal-to-noise ratio. To further enhance the denoising capability, a novel multiscale residual pyramid network (MRP-Net) was proposed to separate the desired signals and complex seismic noise. Compared with single-scale networks, MRP-Net can take advantage of the multiscale features, thereby improving noise attenuation capability. In general, the pyramid-like framework in MRP-Net can extract the potential features at different scales through down-sampling and up-sampling operations, and skip connections were applied to fuse the global-coarse and local-fine features. On this basis, a double-path spatial attention module was designed to enhance the desired features, further improving the processing performance of separating the desired signals from the intense seismic background noise. Comprehensive experiments on synthetic and field seismic data demonstrate that MRP-Net is effective for complex seismic noise attenuation, both for conventional geophone-acquired data and DAS records.

At present, almost no denoising method can effectively suppress the seismic random noise in different areas. This phenomenon is partially because of two reasons: 1) the variable dominant frequency (DF) distribution of random noise in different areas and 2) the different signal-to-noise ratios (SNRs) of the seismic data acquired from different areas. We have developed a deep-learning denoising method to suppress the random noise in different areas based on convolutional neural network (CNN). For a certain area, we leverage the wave equation and power spectrum analysis to construct a noise set whose DF distribution is close to that of the real random noise in this area, and then a CNN denoising model for this area can be obtained via the training of this noise set. In addition, an energy ratio factor is used to adjust the energy ratio of effective signal patch and noise patch in the training process, so as to improve the generalization ability of CNN denoising model to different SNRs. Experiments demonstrate that our method can effectively suppress the random noise in different areas and completely recover the effective events.st no denoising method can effectively suppress the seismic random noise in different areas. This phenomenon is partially because of two reasons: 1) the variable dominant frequency (DF) distribution of random noise in different areas and 2) the different signal-to-noise ratios (SNRs) of the seismic data acquired from different areas. We have developed a deep-learning denoising method to suppress the random noise in different areas based on convolutional neural network (CNN). For a certain area, we leverage the wave equation and power spectrum analysis to construct a noise set whose DF distribution is close to that of the real random noise in this area, and then a CNN denoising model for this area can be obtained via the training of this noise set. In addition, an energy ratio factor is used to adjust the energy ratio of effective signal patch and noise patch in the training process, so as to improve the generalization ability of CNN denoising model to different SNRs. Experiments demonstrate that our method can effectively suppress the random noise in different areas and completely recover the effective events.

Attenuating the random noise and improving the signal-to-noise ratio (SNR) for the seismic data are of great significance in industrial exploration. In recent years, fractal conservation law (FCL) has been proposed and applied to seismic random noise suppression successfully. However, in conventional FCL, the filtering parameter selection strategy is relatively simple and a fixed parameter set is always used for the whole seismic record. In addition, it is very difficult to make an excellent tradeoff in random noise attenuation and signal preservation only by fixed parameters especially under the low-SNR conditions. Thus, accurately recognizing the effective signals and adaptively choosing appropriate filtering parameters is a feasible approach to improve the performance of the conventional FCL. In this article, an adaptive FCL methodology is proposed by combining the seismic noise analyzing theory and stationarity testing techniques. It is known that the random noise and reflection signals have different properties in stationarity and thus, the signal and noise segments can be divided by stationarity testing. As a consequence, different filtering parameters can be adopted for signal and noise segments to achieve the noise suppression and signal preservation simultaneously. Synthetic and field data experiments demonstrate that the proposed method can remove the random noise from seismic record and effectively preserve the reflection events.

Lots of low-frequency noise including random noise and surface waves seriously reduces the quality of desert seismic data. However, the suppression for desert low-frequency noise faces three main problems: nonstationary and non-Gaussian of random noise; strong energy of low-frequency noise; a more serious frequency-band overlap between effective signals and low-frequency noise. Robust principal component analysis (RPCA) is a classical low-rank matrix (LM) recovery method which is very suitable for processing nonlinear noise. It can decompose noisy data to the optimal LM and sparse matrix (SM), which include most effective signals and noise, respectively. Therefore, the RPCA is introduced to suppress desert low-frequency noise. However, due to the low signal-to-noise ratio (SNR) and serious frequency-band overlap, much low-frequency noise still remains in the LM of desert seismic data after the decomposition of RPCA. Meanwhile, some nonnegligible effective signals are decomposed into the SM of desert seismic data. To solve this problem, the convolutional neural network (CNN) is introduced to extract effective signals from SM and LM. By constructing suitable training sets to guide the CNN's training, the CNN denoising models after training are used to predict the effective signals from these two matrices, respectively. In this article, to approach real desert seismic data, we use a variety of seismic wavelets to simulate different types of seismic events, and then use these synthetic seismic events and real desert low-frequency noise to construct training set. In experiments, our method can raise the SNR of synthetic noisy data from -8.69 to 9.63 dB.

We discuss several important issues specific to Web caching for content dynamically generated from database applications. We present the techniques employed by Oracle Web Cache to address these issues. They include: content disambiguation based on information in addition to the URL, transparent session management, partial-page caching for personalization, and broad-scope invalidation with performance assurance heuristics.