Logging-while-drilling (LWD) azimuthal imaging is a widely used well-logging technique in modern geological resource exploration. However, due to the measurement principles and data transmission capacity, the circumferential resolution of current techniques is very limited. In this article, we propose a deep convolutional network-based algorithm called azimuthal image super-resolution (AzSR), which is capable of reconstructing high-resolution borehole images with 128 fans from noisy azimuthal responses of 4/8/16 fans. To make the proposed algorithm more suitable for AzSR, techniques such as sample synthesis, circular padding, and special loss terms are introduced. The advantages and effectiveness of the proposed AzSR algorithm are demonstrated through systematic experiments and real-world applications. The results show that the proposed AzSR has significant advantages over existing algorithms in terms of noise robustness, detail reconstruction, and resolution improvement. With the super-resolution results of AzSR, detailed information about lithological interfaces, local structure, and thin layers can be clearly revealed. This will be of great value for decision-making during geosteering drilling and for fine-scale geological interpretation after drilling.

Seismic multiple is one of the most common noises in marine seismic data, which heavily affects subsequent processing and interpretation. To eliminate the influence of seismic multiples, many methods have been developed, while surface-related multiple elimination (SRME) is one of the most widely deployed methods. However, results of SRME always contain a few strong residual diffracted multiples (RDMs) in practice because of the unprecise prediction of diffracted multiples compared to reflection multiples. If we try to apply further multiple suppression methods to SRME results, it not only tends to damage the signals, but also spends lots of unnecessary computations where there is no RDM. In this article, we propose an improved RDM suppression method based on object detection and image segmentation. First, we employ an object detection network to locate bounding boxes containing RDMs in the SRME results. Then a threshold-based image segmentation method is utilized to identify regions of strong RDMs in the detected boxes. According to the segmentation results, parameters for weak multiples and strong multiples are provided for the adaptive multiple subtraction (AMS) in different regions to generate different results. At last, we combine the suppression results of strong RDMs and weak RDMs as the final results. Application on field data demonstrates that our method is able to suppress RDMs with little loss of signal.

Known for its great potential for determining subsurface properties quantitatively, full-waveform inversion (FWI) is a hot topic in the field of exploration seismology. The success of FWI depends significantly on the accuracy of the starting model. Given that both the migration and velocity profiles originate from the same geological structure, the two should be morphologically consistent. Starting from the velocity-reflector depth tradeoff, we propose a deep learning approach with a new training paradigm for building a good starting model. A velocity model and the corresponding migration image are used to form two-channel inputs, and the generative adversarial network (GAN) is trained to minimize the difference between the output and the true velocity model. After the training, the velocity generator network becomes a plug-in component to enhance the FWI performance. The network can be well generalized to unseen data by training with only the synthetic data. We perform extensive experiments on our test dataset, the Marmousi model, the salt velocity model, and field data to demonstrate the effectiveness of our method. Besides, we briefly give an explanation of why our model produces such outputs in this article, making the proposed method more controllable and credible.

Accurate estimation of volumetric seismic dip is of great significance for subsequent seismic processing and interpretation works. Recently, with the development of deep learning techniques, convolutional networks are also applied for seismic dip estimation. Compared with traditional approaches, estimating dips with convolutional networks is not only more efficient but also shows great promise in accuracy and robustness. However, if we take dips estimated by traditional approaches as labels and train networks on the field seismic data directly, the accuracy and robustness of learned networks are influenced due to the error in dip labels. An alternative solution is synthesizing realistic seismic samples with accurate dip labels. However, we find that due to the differences in seismic responses and structural patterns between the synthetic and field seismic data, networks directly learned from synthetic samples cannot guarantee their generalization on the field seismic data. To overcome these drawbacks, we develop a transfer learning approach for improvement. The proposed approach pretrains the dip estimation network on synthetic seismic samples at first and then transfers it to the targeted field seismic data with a domain knowledge-inspired fine-tuning process. Moreover, the proposed approach also highlights the combination of deep learning techniques and domain knowledge in seismic processing—several subtle realizations, such as knowledge-driven sample augmentation, knowledge constrained loss function, and knowledge motivated transfer learning strategy, are introduced, which greatly enhance the learning of the seismic dip estimation network. The advantages of the proposed approach in accuracy, robustness, and resolution are validated by applying the estimated dips for structural filtering and curvature extraction on the Netherlands F3 and Kerry3D seismic data, which further confirms its practicality in the real-world application. We believe that the proposed approach has provided an effective improved way for further seismic dip estimation practices, and the present domain knowledge constrained deep learning case will also inspire researchers in the same discipline.

Transverse relaxation T2 spectrum obtained by nuclear magnetic resonance (NMR) logging tools is an intuitive reflection of the pore size distribution for subsurface formation, which is valuable for petroleum reservoir characterization. However, the deployment of NMR logging tools is constrained by financial and operational factors, while NMR data are only available in very limited wells. This seriously limits its application in practices. Therefore, researchers try to synthesize NMR T2 spectra from more widely measured conventional logging data with the help of machine learning technologies. In the article, we propose the spectrum regression forest (SRF) algorithm for the prediction of NMR T2 spectra from conventional logging responses. Based on the experiment on the real-world well data of carbonate reservoir, the proposed algorithm is proved to provide effective NMR T2 spectrum predictions with accuracy amplitudes and consist morphology, which is believed to enhance the understanding of formation pore structures for future reservoir characterization practices.

Structural curvatures are widely used seismic attributes that help interpreters to understand both structural and stratigraphic features. Traditional structural curvature extractions are mainly calculated from dip estimations through lateral scanning of seismic events, which is not only a very time-costing approach but also influenced by parameter settings, seismic frequency, and data quality. In this article, we propose a deep learning-based volumetric curvature extraction approach that directly derives structural curvature volumes from the seismic response. To realize the above approach, we develop a suite of sample generation and augmentation methods to synthesize seismic samples with accurate curvature labels. Then, a multitask end-to-end convolutional neural network architecture and a geometric loss function are proposed to establish the volume mapping model from complex seismic responses to the most positive and negative curvature volumes. The performance of the proposed curvature extraction approach is evaluated on both the synthetic data and the Netherlands F3 field seismic data. Extensive experiments demonstrate that curvature volumes extracted with the proposed approach are not only more accurate and less influenced by the noises of poststack seismic data but also more friendly for structure interpretation. Therefore, we believe that our proposed deep learning curvature extraction approach can be a useful tool for further seismic structure interpretation practices.

Seismic image, especially for the prestack image, performs a blurred version of the reflectivity image due to spatial aliasing, poor acquisition aperture, and nonuniform illumination. The blurring effects can be quantified by the point spread function (PSF). We herein adopt an explicit space-variant PSF formula, which can be defined as a sequential application of the modeling and migration operators with the asymptotic Green's function. The deblurred images are restored using the nonstationary deconvolution with total variation regularization in which the blurred images are described by the convolution between the space-variant PSF and the reflectivity image. However, nonstationary deconvolution is computationally challenging. We introduce an extending primal-dual hybrid gradient (E-PDHG) method to decompose the complex problem into a sequence of simple subproblems that have closed-form solutions. Numerical results on synthetic data and field data demonstrate that the proposed E-PDHG method outperforms the basic PDHG method in the prestack seismic image deblurring.

In the seismic exploration, recorded data contain primaries and multiples, where primaries, as signals of interest, can be used to image the subsurface geology. Surface-related multiple elimination (SRME), one important class of multiple attenuation algorithms, operates in two stages, multiple prediction and subtraction. Due to the phase and amplitude errors in the predicted multiples, adaptive multiple subtraction (AMS) is the key step of SRME. The main challenge of this technique resides in removing multiples without distorting primaries. The curvelet-based AMS methods, which exploit the sparsity of primary and multiple in curvelet domain and the misfit between the original and estimated signals in data domain, have shown outstanding performances in real seismic data processing. These methods are realized by using the iterative curvelet thresholding (ICT), which has heavy computation burden since it includes two forward/inverse curvelet transform (CuT) pairs in each iteration. To ameliorate the computational cost, we propose an accelerating ICT method by exploiting the misfit between the original and estimated signals in curvelet domain directly. Since the proposed method only needs do one forward/inverse CuT pair, it is faster than the traditional ICT method. Considering that the error of the predicted multiple is frequency-dependent, we furthermore introduce the joint constraints within different frequency bands to stabilize and improve the multiple attenuation. Synthetic and field examples demonstrate that the proposed method outperforms the traditional ICT method. In addition, the proposed method has shown to be suitable for refining other AMS methods' results, yielding a SNR improvement of 0.5-2.8 dB.