Seismic noise suppression plays a crucial role in seismic data processing and geological structure interpretation. Distributed acoustic sensing (DAS) is widely applied in acquisition of vertical seismic profiling (VSP) data, but the collected seismic data often contains various complex noise with strong energy such as background noise, single-trace low-frequency interference, single-trace random interference, and coupled noise. Therefore, how to effectively suppress the diverse noise for DAS VSP data is important in subsequent seismic data processing. At present, deep learning methods based on convolutional neural network (CNN) have been extensively employed in seismic denoising and have achieved remarkable results, but they often exhibit poor ability to capture global information. Subsequently, deep learning methods based on Transformer are developed, which excel at capturing global features but exhibit high computational complexity and weak ability to capture local information. However, extracting both global and local features is crucial for seismic noise suppression. Global features provide insight into the overall structure of seismic data, while local features capture detail features containing rich geological structure information. Therefore, we propose a deep learning approach based on selective kernel feature fusion Uformer (SKUformer), which combines the advantages of Transformer and CNN to simultaneously capture global and local information. First, we replace the convolutional layer in U-net with the parallel dilated convolution locally enhanced shifted window (PDC-LeSwin) Transformer block, enhancing the capability of network to capture global information and acquiring multiscale information for VSP data. Moreover, we introduce the PDC module and locally enhanced feed-forward (LeFF) module to extract rich detailed information in VSP data. Additionally, we change the skip connection to the selective kernel feature fusion (SKFF) module to selectively fuse multiscale features, including low-frequency features and detailed features in VSP data. Finally, we validate the effectiveness of our method by applying it to the synthetic and the field DAS VSP data, comparing it with three other methods, thus demonstrating superiority in removing complex noise while preserving effective seismic signals.

Seismic forward modeling plays a crucial role in Earth science, particularly in seismic exploration. It is essential for seismic data acquisition, inversion, and interpretation. Traditional numerical simulation methods necessitate grid partitioning of the computational domain and discrete approximations of time and space derivatives, which can lead to numerical dispersion and algorithmic instability. In recent years, the application of data-driven methods in seismic simulations has attracted significant attention and is expected to provide an effective alternative to traditional approaches. These methods such as neural operators (NOs), notably Fourier NO (FNO), enable rapid computations and reduce the time needed for resimulation due to changes in source and model parameters. However, in complex models, a single FNO struggles to accurately learn the wavefield solution. To enhance the accuracy and generalization performance of FNO in learning seismic wavefield information in complex geological models, we propose a novel model called multiscale feature extraction and aggregation embedded with Fourier neural operator deep network (MFEAFNet) for intelligent elastic wavefield modeling. Our proposed method integrates the feature extraction modules of the multiaxis feature extraction (MAFE), the convolutional block attention module (CBAM), and the multiscale cross-feature aggregation (MCFA) with FNO, allowing these modules to automatically learn the most representative features for wavefield data and, thereby, ensuring that the combined features are effectively delivered to the FNO module for better learning of fine details of complex elastic wavefields. Numerical experiments demonstrate that our method has high accuracy in predicting wavefields across different medium models and source locations and in long-term predictions.

In the field of Unmanned Aerial Vehicle (UAV) technology, there has been a growing interest in the efficient management of energy consumption through strategic path planning in complex obstacle environments. This emerging trend aims to optimize the flight routes of UAVs in order to minimize their energy usage. Path planning is a key process to determine the trajectory of a UAV from its origin to its destination. However, many algorithms proposed for this task have proven to be inefficient in complex obstacle environments. For this reason, this paper proposes a Levy flight based multi-population gray wolf optimization (LM-GWO) algorithm. It combines multi-population ideas, and clusters individual gray wolves into different populations through the Bi-Kmeans clustering algorithm. It accelerates the algorithm’s convergence by allowing different gray wolf populations to complete different jobs during the training process. In addition, one of the limitations of the GWO algorithm is its susceptibility to getting trapped in local optima, which is solved by introducing the Levy flight mechanism, which ultimately makes the multi-UAVs cooperate to complete the path planning work. The results of simulation experiments demonstrate that the LM-GWO algorithm can get the flight path that satisfies the constraints. By comparing with the other three algorithms, the algorithm’s effectiveness in solving cooperative path planning to save energy consumption is verified.

Seismic wave forward simulation is the basis of seismic inversion and imaging. In recent years, deep learning methods represented by physics-informed neural networks (PINNs) have been widely applied in the field of scientific computing, and have obtained significant attention as mesh-free methods in seismic inversion. Currently, the training of PINN is usually constructed by fixed points in seismic wavefield simulation, and the large number of training points can result in high-computational costs. This letter proposes an adaptive strategy for PINN training point selection based on the velocity distribution and residuals of viscoacoustic wave equations. This strategy initializes the positions and quantities of training points based on the velocity distribution of the model and dynamically adjusts the training points based on the values of the loss function during the calculation process. The proposed approach reduces the number of training points, thereby improving the training efficiency of PINN. Numerical examples including the homogeneous model, the layered model with irregular topography, and the Marmousi2 model show that the proposed sampling strategy effectively reduces the calculation time by 54.37% while ensuring accuracy.

In recent years, significant progress has been made in self-supervised seismic denoising. However, most of these methods focus on random noise attenuation, which is of little practical use for distributed acoustic sensing (DAS) vertical seismic profile (VSP) data with a large amount of spatially correlated noise. In this article, we propose a new perspective to solve this problem, which is to redesign the masked spot training scheme by analyzing different texture features of effective signal and spatially correlated noise. Specifically, we fully analyze the correlation of different correlated noises in vertical and horizontal scales, and then use a novel masked spot masking strategy to carefully design the receptive field to expand the masked spot network (BSN) to neighborhood-mask network (NMN), which makes BSN still meet the assumption of noise pixel independence in the presence of a large amount of correlated noise. At the same time, we introduce a texture total variation (TTV) regularization to further eliminate correlated noise and preserve the local smooth structure of the effective signal. In order to maximize the texture difference between signal and noise, we propose asymmetric-dilated convolution (ADConv), which has a large receptive field in specific direction and acts as an information filter in the network. Our method, also called SelfTexture, only uses observed noisy DAS VSP data to remove correlated noise in a self-supervised manner and further demonstrates the great potential of BSN in dealing with correlated noise. Extensive experiments on synthetic and field DAS VSP data validate the superior performance of our SelfTexture.

A numerical approach based on Gaussian Process Regression (GPR) is presented to predict the wavefield and estimate the model parameters of two dimensional (2D) acoustic wave equation with the sparse and noisy data. Through discretizing the time derivatives of acoustic wave equation and placing the priors of the state variables as Gaussian process (GP), the model parameters and acoustic wave equation are encoded in the kernel function of a multi-output GP. The GP informed with the underlying physics such as wave equation is efficiently to solve forward and inverse problems in the field of geophysics, which is an efficient learning machine with only a small number of samples. The proposed approach is demonstrated through 2D acoustic wave equation in terms of wavefield prediction and velocity inversion in a homogeneous medium.

Physics-informed neural networks (PINNs) is a scientific machine learning technique for solving problems involving partial differential equations (PDEs). PINNs approximate PDE solutions by training a neural network to minimize a loss function. A major advantage of this method is that it provides a meshfree algorithm that is fundamentally different from traditional numerical methods such as finite element methods and finite difference methods. However, there are two limitations of PINNs. One is the accuracy of the solution, and the other is the training cost. We propose a new sampling strategy based on the errors of each iteration in the training, which generate more effective collocation points to refine the training set, thereby improving the accuracy of the current approximate solution and further reducing the time cost of training. Based on acoustic wave equation, we investigate the influence of different optimization methods on the accuracy of forward modeling and the influence of different sampling strategies on velocity inversion.

Seismic data are often contaminated by random noise or even more complex types of noise, resulting in poor quality of seismic data with low signal-to-noise. Seismic random noise suppression is a crucial procedure in seismic data processing. Deep learning methods have been successfully applied to suppress seismic random noise. In this study, we propose a U-net based deep learning method to suppress seismic random noise. We add several dropout layers to the U-net to effectively avoid overfitting and set the output of the network as the residual units to enhance the training efficiency of our network. Moreover, the cosine similarity index is incorporated into the loss function to reserve the lateral continuity of geological structures. The denoising results of synthetic seismic data and field VSP data demonstrate that the proposed network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.

Ordos basin in Northwest of China is rich in oil/gas, coal as well as unconventional oil/gas, coalbed gas, etc. One of big challenges in seismic exploration of this area is extreme energy attenuation due to thick and loose loess layer, which leads to poor seismic data with low signal-to-noise ratio (SNR). Seismic wave propagation in loess region depends on the petrophysical properties of loess medium. We firstly analyze the variations of petrophysical parameters of loess medium such as collapsibility, moisture content, porosity, saturation, density and compressional coefficient in Huan County. Then, we study the features of seismic wave propagation in loess region using finite-element method. The numerical results in two typical models show that the most energy of propagation waves are trapped in dry and moist loess layers with low velocities while only a small amount of energy passes through the high-velocity zone such as sedimentary formation.

We propose a blind nonlinear acoustic impedance inversion method. The seismic wavelet is first extracted through the Euclid deconvolution method from multichannel seismic data. Then, the acoustic impedance is inverted based on the exact nonlinear forward operator. The conventional Euclid deconvolution can theoretically estimate the reflectivity without special prior assumptions, but the method is extremely inefficient and unstable in the case of a large amount of data. We optimize the method and propose a frequency-domain algorithm to improve its efficiency. Conventional impedance inversions are almost implemented based on the linearized approximate formula, but the inversion errors will increase sharply when there is a strong reflection interface. We build the inversion objective function by the accurate nonlinear formula to improve accuracy. The total variation (TV) regularization and low-frequency components of well-logging curves are added to the objective function to make the inversion result have a block structure and converge to the absolute impedance. The nonlinear optimization problem is finally solved by the Levenberg–Marquardt (LM) algorithm. The results of synthetic data and field data verify that our method has high accuracy and good practicability.

Distributed acoustic sensing (DAS) is one of the most popular sensors for seismic acquisition. Compared with traditional seismic acquisition technology, DAS has the advantages of full-well coverage, high density, high efficiency, high sensitivity, low cost, strong resistance to high temperature and high pressure, and anti-electromagnetic field interference. However, the DAS vertical seismic profile (VSP) data are contaminated by strong noise interference, which brings challenges for practical applications and difficulties to seismic inversion and interpretation. A deep learning method named U-net with Global Context Block and Attention Block (GC-AB-Unet) is proposed to suppress the background noise and increase the data quality for DAS-VSP records without knowing any prior information. In GC-AB-Unet, several dropout layers are added to the U-net to avoid overfitting. Meanwhile, to speed up the network training, the residual units are set as the output of the network. Furthermore, GC-Block is introduced for better capturing shallow and deep features by extracting global context information. In addition, Attention Block is used to emphasize seismic event features and restrain irrelevant details in seismic data. We also construct a training dataset by utilizing the synthetic data and real noise of DAS-VSP data. The denoising results for both synthetic data and field DAS-VSP data show that compared to original U-net and Damped Rank Reduction (DRR) method, the GC-AB-Unet network is able to preserve the effective signals with almost no energy leakage while suppressing a large amount of background noise.

Time–frequency (T–F) analysis has powerful applications in various fields such as geoscience and engineering. Seismograms are distorted by dispersion and attenuation in such a way that they alter their amplitude and phase spectra. Velocity dispersion is usually neglected in the conventional seismic data processing. However, it has a severe effect on seismic data processing if dispersion is intense in high-attenuation media. Most of the studies on dispersion are theoretical models and laboratory measurements, whereas velocity dispersion analysis directly from field data is rare. In this letter, a method is proposed to estimate the dispersion of vertical seismic profile (VSP) data based on continuous wavelet transform (CWT) in time–frequency domain. The Morlet wavelet, three-parameter wavelet (TPW), and Cauchy wavelet are chosen and compared as mother wavelets in CWT to determine the variations of velocity with frequency. Then, the detailed process of extracting velocity dispersion of propagating waves in a dissipative medium is proposed by using their T–F spectra. Finally, the synthetic and field VSP data are used to demonstrate the validity of the proposed method. The results indicate that the velocity dispersion in synthetic data estimated by TPW and Cauchy wavelet generally match well with the theoretical values while it is not the case for the Morlet wavelet. The velocity dispersion of field data is weak and the estimated velocity basically matches well with the logging data.

For oil and gas exploration, seismic wave propagation in coupled acoustic, elastic, poroelastic, and even anisotropic media is a valuable aspect. However, it is a challenging task to deal with the interfaces of the coupled model. In order to tackle the specific issue, a unified numerical scheme is developed, which is based on the discontinuous Galerkin method. The acoustic, elastic, poroelastic, and anisotropic elastic wave equations are unified into a first-order velocity–stress system. The numerical simulation at the interfaces in the coupled model is conveniently handled by the Godunov flux without any extra operations. Numerical results from the coupled acoustic–elastic and acoustic–poroelastic model are compared with the analytic solutions. In addition, the rates of convergence from different orders are analyzed, which demonstrates the accuracy of the proposed numerical scheme. The surface waves at the fluid–solid interface are studied. Moreover, the proposed scheme is applied to a more complex coupled model, including the coupled acoustic–elastic–poroelastic model and the coupled acoustic–anisotropic elastic model. The corresponding results demonstrate that the proposed numerical scheme is capable of dealing with the coupled model.

Seismic reflections at an interface are often regarded as the variation of the acoustic impedance (product of seismic velocity and density) in a media. In fact, they can also be generated due to the difference in absorption of the seismic energy. In this work, we investigate the impacts of attenuation on seismic reflections based on the diffusive-viscous wave equation, which is used to investigate seismic attenuation and frequency-dependent seismic anomalies related to hydrocarbon reservoirs. The results show that the reflections are significantly affected by the diffusive attenuation but they are insensitive to the viscous attenuation in an acoustic dispersive medium. In an elastic dispersive medium, the attenuation parameter in P wave equation has a big impact on both PP and PS reflections, however, the attenuation parameter in S wave equation has little effect on the PP reflection but it strongly affects the PS reflections. Furthermore, the PP and PS reflections in the dispersive medium are dependent on the frequency, and the effect of attenuation on PP and PS reflections at lower frequencies is bigger than those at higher frequencies.

Seismic numerical modeling is a valuable tool for seismic interpretation and an essential part of seismic inversion algorithms. The aim is to predict the seismogram, given an assumed structure of the subsurface. Real subsurface structure is often multi-phase media because of fluid saturation, so the commonly used models such as acoustic media, elastic media can't characterize the information of real subsurface structure. The diffusive-viscous model can be used to describe seismic wave propagation in fluid-saturated rocks, and it is also used to investigate the relationship between the frequency dependence of reflections and the fluid saturation in a porous rock. In this paper we simulate the propagation of diffusive-viscous waves in fluid-saturated media using the Flux Corrected Transport-Finite Difference Method (FCT-FDM). The numerical results show that the propagating waves in fluid-saturated media greatly attenuate by comparing with those of acoustic case.

SOAP is the basis of Web Services application, and, SOAP messages are glue of heterogeneous Web Services. Secure transit of SOAP messages plays a critical role for the applicability of Web Services. The main challenges to the secure transit of SOAP messages includes: confidentiality, authentication, integrity, both- party nonrepudiation, and single sign-on. Analyze and take advantage of the existing technologies and solutions related to SOAP and Web Services, and a model of secure transmission of SOAP messages is developed, adopting technologies like XML Signature, XML Encryption, and X.509 Certificate. Analysis to the transmission model represents that basic requirements towards secure transmission of SOAP messages are fulfilled and high-level security and efficiency are acquired.

We describe the development and evaluation of a tool, iSonic, to assist users with visual impairment in exploring georeferenced data using coordinated maps and tables, augmented with nontextual sounds and speech output. Our in-depth case studies with 7 blind users during 42 hours of data collection, showed that iSonic enabled them to find facts and discover trends in georeferenced data, even in unfamiliar geographical contexts, without special devices. Our design was guided by an Action-by-Design-Component (ADC) framework, which was also applied to scatterplots to demonstrate its generalizability. Video and download is available at www.cs.umd.edu/hcil/iSonic/.

iSonic is an interactive sonification tool for vision impaired users to explore geo-referenced statistical data, such as population or crime rates by geographical regions. Users use a keyboard or a smooth surface touchpad to interact with coordinated map and table views of the data. The integrated use of musical sounds and speech allows users to grasp the overall data trends and to explore the data to get more details. Scenarios of use are described.

System administrators work with many different tools to manage and fix complex hardware and software infrastructure in a rapidly paced work environment. Through extensive field studies, we observed that they often build and share custom tools for specific tasks that are not supported by vendor tools. Recent trends toward web-based management consoles offer many advantages but put an extra burden on system administrators, as customization requires web programming, which is beyond the skills of many system administrators. To meet their needs, we developed A1, a spreadsheet-based environment with a task-specific system-administration language for quickly creating small tools or migrating existing scripts to run as web portlets. Using A1, system administrators can build spreadsheets to access remote and heterogeneous systems, gather and integrate status data, and orchestrate control of disparate systems in a uniform way. A preliminary user study showed that in just a few hours, system administrators can learn to use A1 to build relatively complex tools from scratch.

This paper describes a pixel‐based technique that enables highly interactive Web choropleth maps for georeferenced data publishing and visual exploration. Instead of delivering geographic knowledge to the client in polygon‐based vector formats, we encode geographic object IDs and shape information into highly compact pixel images (decoding maps). This allows the combination of raster and vector characteristics while avoiding the problems in the currently existing pixel‐based (raster‐image‐based) or vector‐based techniques. Differing from traditional pixel‐based techniques that are static and allow very little user interaction, our pixel‐based technique allows varieties of sub‐second (less than 1 s) interface controls such as dynamic query, dynamic classification, geographic object data identification, user‐setting adjusting, as well as turning on/off layers, panning and zooming, with no or minimum server support. Compared with Web GIS techniques that use vector geographic data, our technique avoids transferring over the network large vector geographic data. It also avoids the non‐trivial client‐side computation to interpret the vector data and render the maps. Our technique features a short initial download time, near‐constant performance scalability for larger numbers of geographic objects, and download‐map‐segment‐only‐when‐necessary which potentially reduces the overall data transfer over the network. As a result, it accommodates general public users with slow modem network connections and low‐end machines, as well as users with fast T‐1 connections and fast machines. The client‐side (browser) is implemented as lightweight Java applets. YMap, an easy‐to‐use, user‐task‐oriented highly interactive mapping tool for visual georeferenced data exploration is implemented using this technique. The performance comparison of YMap to some other vector‐based Web GIS demonstrates the feasibility and benefits of this technique.