Affected by near-surface absorption, seismic wave energy attenuation and phase distortion greatly reduce the resolution and signal-to-noise ratio (SNR) of seismic data, causing changes in seismic attributes much greater than other factors. Inverse Q filtering is a common method to compensate for these undesirable effects. To overcome the drawbacks of the traditional inverse Q filtering, such as the difficulty of parameter selection and the instability of wave amplitude compensation, we propose a new unsupervised inverse Q filtering method in a deep learning (DL) framework, using a forward attenuation operator based on the seismic wave attenuation theory to drive the network. The filtering strategy does not require actual training labels and avoids the numerical instability of the amplitude compensation. First, we design a hybrid convolutional neural network bidirectional LSTM (CNN-BiLSTM)-attention model for multivariate time series prediction and then take the data to be compensated as input for the DL network and the compensated data as output. The output is then attenuated using a forward attenuation operator constructed from the near-surface Q model. After that, the error between the attenuated data and the original input data is transmitted back to the DL network to modify the network output, and the error is minimized by optimizing the network parameters to generate the final compensation result. In the entire prediction process, there is no need to produce unattenuated data labels, which achieves the effect of unsupervised learning. The results with synthetic and field data demonstrate that the unsupervised method can effectively and stably compensate for seismic signals. Compared to the classical inverse Q filtering, the proposed method improves the resolution and SNR of seismic records.

Deep learning full waveform inversion (DL-FWI) is an end-to-end and time-efficient high-resolution imaging technique for subsurface media. Popular methods are often plagued by location drift and significant velocity misfits at the stratigraphic boundaries. In this study, we propose an augmented boundary attention algorithm (ABA-FWI) to focus on the key boundary information. Regarding network composition, the wavelet convolution (WTconv) layer and the spatial attention module (SAM) are incorporated into the encoder and the decoder, respectively. The WTconv layer captures low frequencies by obtaining a large receptive field without suffering from overparameterization. SAM extracts distinctive information by utilizing the interspatial relationship of features for resolution enhancement. For loss function design, our reflection coefficient tuned boundary (RCTB) loss introduces the reflection coefficient to adjust the gradient map weight of low-contrast areas. It focuses on boundary regions characterized by speed transitions to minimize errors. Results on OpenFWI, the SEG simulation, and the Marmousi II slice datasets show that our method is superior to the state-of-the-art data-driven methods, especially on boundary details. The source code is available athttps://github.com/FanSmale/ABA-FWI.

Deep learning full waveform inversion (DL-FWI) is gaining much research interest due to its high prediction efficiency, effective exploitation of spatial correlation, and lack of the need for an initial estimate. As a data-driven approach, it has several key issues. For example, effective deep networks need to be designed, the training process needs to be controlled, and the generalization ability needs to be enhanced. In this paper, we propose a dual decoder network with curriculum learning (DD-Net) to handle these issues. First, we design a U-Net with two decoders to grasp the velocity value and stratigraphic boundary information of the velocity model. These decoders’ feedback will be combined at the encoder to enhance the encoding of edge spatial information. Second, we introduce curriculum learning to network training by organizing data in three difficulty levels. The easy-to-hard training process enhances the data fitting of the network. Third, we apply the network to low resolution seismic observations via a pre-network dimension reducer. This can serve as a general design idea without destroying the original network characteristics. Experiments are undertaken on SEG salt datasets and four synthetic datasets from OpenFWI. The results show that our network is superior to other state-of-the-art data-driven networks. The source code is available at github.com/fansmale/ddnet.

Seismic analysis and interpretation are sensitive to data resolution. High-density acquisition, deconvolution, and inverse Q filtering are traditional methods for resolution improvement. These methods have drawbacks such as strict assumptions and complex parameter solving processes. In this paper, we propose a channel attention U-Net and physical convolution combination (CAUC) algorithm to overcome these limitations. First, we design a channel attention U-Net (CAU) to establish a sophisticated nonlinear relationship between low-frequency data and coarse reflection coefficients. Specifically, we use a channel attention block after each downsampling convolution block to extract important local features of seismic data. Second, we convolve the coarse reflection coefficients with the wavelets to obtain high-resolution data. This physical convolution operation has a solid theoretical foundation. Therefore, our algorithm benefits not only from the learning ability of the neural network but also from the explainability of the physical operation. Both synthetic and field data are employed to verify the validity of the new algorithm. The traditional fast iterative shrinkage-thresholding algorithm (FISTA) and deep convolutional neural network (CNN) are used for comparison. The results show the superiority of the proposed method, especially in enhancing details such as thin layers and continuity.

Deep learning has made substantial progress in remote sensing image segmentation tasks. It usually requires a large number of high-quality annotation maps (i.e., clean labels), which are labor-intensive. In this paper, we propose to use abundant electronic maps (i.e., noisy labels) to supplement a small number of clean labels to solve the problem of massive label production. In addition, we propose a multi-stage noise supervised framework (NSDI) to prevent noise from deteriorating the performance of deep model. NSDI consists ofclean training,weight initialization, andhybrid trainingstages. In theclean trainingstage, we train a segmentation model using a small amount of clean labels and remote sensing images to compute the confusion probability matrix. In theweight initializationstage, we use the confusion probability matrix as well as the prediction probability to calculate the label reliability of the electronic map. In thehybrid trainingstage, an adaptive weighted loss function based on cross-entropy is used to dynamically update the label reliability. Then we train model further using electronic maps with the support of the adaptive weighted loss function and label reliability. Experiments were undertaken on 2,656 images of 512 × 512 pixels. Ablation studies show that NSDI improves the model robustness as well as the segmentation quality.

Super-resolution reconstruction is an essential task of seismic inversion due to the low resolution and strong noise of field data. Popular deep networks derived from U-Net lack the ability to recover detailed edge features and weak signals. In this paper, we propose a dual decoder U-Net (D2UNet) to explore both detail and edge information of the data. The encoder inputs the low resolution image and the edge image obtained through the Canny algorithm. Edge image can provide rich shape and boundary information, which is helpful to generate more accurate and high-quality data. The dual decoder consists of a main decoder for high-resolution recovery and an edge decoder for edge contour detection. These two decoders interact with a texture warping module (TWM) with deformable convolution. TWM aims to distort realistic edge details to match the fidelity of low resolution inputs, especially the location of edges and weak signals. The loss function is a combination ofL1loss and multi-scale structural similarity loss (MS-SSIM) to ensure perception quality. Results on synthetic and field seismic images show that D2UNet not only improves the resolution of noisy seismic images, but also maintains the image fidelity.

Brittleness is an important parameter characterizing the fracturing properties of shale reservoir, which can be predicted by the pre-stack seismic inversion. In order to overcome the low efficiency and ill-posed problems of the traditional pre-stack brittleness inversion, we propose a new unsupervised deep learning (DL) inversion method for seismic brittleness parameters based on the physical equation. This method integrates DL framework and the physical equation, and provides a DL inversion strategy without actual labels. We first input the original seismic data into the Fastformer network, and use the low-frequency model as the physical constraint to predict the brittle parameters. Then, the prediction results of brittleness parameters are sent to the forward modeling module (a linear approximation equation) to calculate the synthetic seismic data. Next, the error between the calculated seismic data and original seismic data is used to update the network prediction results. The network parameters are iteratively optimized to minimize the error, and the brittle prediction parameters are finally output. In the whole training process, it is not necessary to use the real brittle parameters as the labels. Through this method, the effect of approximate unsupervised learning is obtained. Finally, we apply the proposed method to the synthetic data and field data, and compared with the results inverted by the traditional L1 method. The experimental results show that the proposed method has higher inversion accuracy and efficiency than the traditional L1 method, which has a great potential in the practical application.

Due to low cost and nondestructive characteristics, the passive surface-wave imaging has shown great potentials in urban near-surface exploration. However, the imaging methods are facing with many challenges in practical applications, such as uneven noise source distribution and complex site environment, which will seriously affect the dispersion imaging quality of surface waves, and result in the failure of retrieving accurate dispersion curve and inaccurate inversion. Therefore, data screening is required to improve the accuracy of passive surface-wave dispersion imaging. This process is usually completed manually, which is time-consuming for processing the large data sets. To solve this problem, we propose an automatic data screening method for the near-surface passive surface-wave imaging. Based on the distribution characteristics of the surface-wave energy of noise data in the F-K domain, this proposed method uses the least-squares technique to fit the quadratic distribution of energy, and sorts the noise time segments according to the defined correlation coefficients and bandwidth coefficients. Thus, we can automatically detect the noise time segments with high signal-to-noise ratio (SNR) without manual intervention. In order to verify the effectiveness of this proposed method, both synthetic data and field data are used in this study. The results show that this automatic data screening method significantly improves the accuracy of the passive surface-wave dispersion imaging, effectively expands the surface-wave energy band, and realizes rapid and automatic data screening.

Rust is an industrial systems programming language unique in achieving memory safety without the need for a garbage collector. Instead, Rust relies on a unique and sometimes subtle resource ownership and borrowing system. This system can make learning Rust a challenge, even for experienced programmers. Motivated by these challenges, we introduce RustViz, a tool that allows an instructor to generate custom interactive timelines depicting ownership and borrowing events alongside Rust code examples embedded within learning material. These visualizations makes visible the static events, and subsequent state changes, that a Rust programmer must otherwise track entirely mentally. We have used RustViz to build a week-long Rust unit in a large undergraduate programming languages course. We demonstrate that this learning material, and the RustViz visualizations in particular, were valuable to students and led to the development of an accurate mental model of the essentials of ownership and borrowing in Rust.

Surface-wave analysis methods have been widely applied to construct near-surface shear-wave velocity structures. Whether it is an active source or passive source, the near-surface shear-wave velocity structure is obtained by inverting the surface-wave dispersion curve. In order to solve the problems of low inversion efficiency and poor inversion results in traditional surface-wave exploration, we have studied the surface-wave inversion methods based on deep learning technology. In this study, we propose a long short-term memory (LSTM) surface-wave inversion method based on the first height last velocity (FHLV) loss function. The core of our proposed method is the FHLV loss function consisting of two parts: a speed loss and a thickness loss, which improves the overall prediction accuracy through optimizing the learning process of the thickness parameter by the network. To verify the accuracy of the proposed LSTM surface-wave inversion method based on the FHLV loss function, experiments are conducted on both synthetic and real datasets. The results show that our proposed method can efficiently and accurately invert the near-surface shear-wave velocity structure.

In the absence of fracture, the strata with horizontal interbedding structure can be approximately equivalent to transversely isotropic media with vertical axis of symmetry (VTI) in the sedimentary basin. Accurate estimation of Young’s modulus, Poisson’s ratio, and weak anisotropy (WA) parameters can provide basic information for further prediction of shale reservoir rock brittleness andin situstress. Based on the scattering theory and Born approximation, we derive the P-wave reflection coefficients involving Young’s modulus and Poisson’s ratio and WA parameters for an interface separating two elastic VTI media. Assuming in the case of shale, we modify this reflection coefficient to involve only three model parameters through a series of integration and simplification for stabler inversion. A Bayesian amplitude versus offset (AVO) inversion method is implemented to estimate the three attributes, which are then converted to calculate the brittleness-related and WA parameters. The numerical simulation results show that for AVO of III in the case of gas-bearing shale, the derived approximation has more accuracy than the Young’s modulus, Poisson’s ratio and density (YPD) equation, especially at large angle. Tests on synthetic and real seismic data verify that the established inversion strategy for VTI shale reservoirs is stable and accurate in the estimation of brittleness-related and WA parameters.

Abnormal first-arrival times in the automatic first-arrival picking significantly affect the structural inversion and static correction. After implementing an automatic picking, the manual intervention is still required to correct or eliminate false first-arrival times, which seriously affects the efficiency of the first-arrival picking. Therefore, it is necessary to develop automatic removal methods for abnormal first-arrival times. Here, we propose a distance-based outlier detection algorithm to automatically and effectively eliminate the abnormal first-arrival times. In order to identify the abnormal first-arrival times, each first-arrival time is decomposed into a receiver delay time and a shot delay time. Then, according to offset and azimuth information, we eliminate the influences of offset and shot delay times on the first-arrival times in a single shot to get the receiver delay times. We further rearrange the delay times in polar coordinates. Finally, we apply a distance-based outlier detection algorithm to the delay times on different azimuths and delete the first-arrival times that correspond to the detected outliers. Our study indicates that the distance-based outlier detection algorithm can effectively eliminate the abnormal first-arrival times. The results from real data processing demonstrate that the proposed automatic removal approach of the abnormal first-arrival times can effectively improve the quality of the first-arrival times. In addition, we find that our proposed algorithm has a better performance than a least-squares regression method for low signal-to-noise ratio (SNR) seismic data.

Unconventional reservoirs usually have strong anisotropy. Generally, they can be regarded as transversely isotropic media with a vertical symmetry axis [(VTI) media] in the absence of fractures. Therefore, it is of great significance to develop high-accuracy inversion method for VTI media. The three elastic parameters and two Thomsen anisotropy parameters of VTI media are usually obtained by indirect calculation or inversion methods based on the approximate formulas. However, the cumulative errors caused by indirect calculation and low calculation accuracy of the approximate formulas limit the estimation accuracy of these parameters. In this article, we propose a new method based on the exact reflection coefficients equations (ERCEs) of VTI media to improve the inversion accuracy of these elastic and anisotropy parameters. The new method adopts the Bayesian deterministic inversion (BDI) strategy to solve the inversion problem, which becomes highly nonlinear when using the ERCEs. We analyze the feasibility of the new method using the residual function maps (RFMs) and eigenvalue analysis of Hessian matrix. The analysis results show that the BDI strategy can well solve a series of problems brought by the ERCEs for nonlinear inversion, such as strong nonlinearity and more target parameters need to invert. Both synthetic and field data examples show that the proposed method can accurately estimate the elastic and anisotropy parameters, which verifies the feasibility and effectiveness of the method.