Search

The establishment of a three-dimensional velocity field is an essential step in seismic exploration, playing a crucial role in understanding complex underground geological structures. Accurate 3D velocity fields are significant for seismic imaging, observation system design, precise positioning of underground geological targets, structural interpretation, and reservoir prediction. Therefore, obtaining an accurate 3D velocity field is a focus and challenge in this field of study. To achieve intelligent interpolation of the 3D velocity field more accurately, we have built a network model based on the attention mechanism, JointA 3DUnet. Based on the traditional U-Net, we have added triple attention blocks and channel attention blocks to enhance dimension information interaction, while adapting to the different changes of geoscience data in horizontal and vertical directions. Moreover, the network also incorporates dilated convolution to enlarge the receptive field. During the training process, we introduced transfer learning to further enhance the network's performance for interpolation tasks. At the same time, our method is a deep learning interpolation algorithm based on an unsupervised model. It does not require a training set and learns information solely from the input data, automatically interpolating the missing velocity data at the missing positions. We tested our method on both synthetic and real data. The results show that, compared with traditional intelligent interpolation methods, our approach can effectively interpolate the three-dimensional velocity field. The SNR increased to 36.22 dB, and the pointwise relative error decreased to 0.89%. [ABSTRACT FROM AUTHOR]

Well logging data contain abundant information on stratigraphic sedimentology. Artificial identification is usually strongly subjective and time-consuming. Pattern recognition algorithms like SVM may not adequately capture the depth-related variations in logging curve shape. This paper defines logging sedimentary microfacies as unidirectional 2D image segmentation and builds an improved U-net model to meet the requirements of logging sedimentary microfacies acquaintance. The proposed model contains three characteristics: (1) It removes pooling layers to avoid the loss of spatial features; (2) it utilizes multi-scale convolution blocks for mining multi-scale spatial features in logging data; (3) a one dimensional convolution layer is added to achieve deep single-direction segmentation. In this model, a 2D image composed of several standardized logging curves is used as the network's input. In addition, we propose an effective data enhancement method and calculate the geometric feature attributes of well logging curves to reduce the complexity of the data characteristics. We tested the model on manually annotated validation datasets. Our method automatically measures fine sedimentary microfacies characteristics, improving the accuracy of sedimentary microfacies identification and achieving the desired result. Additionally, the model was tested on unlabeled actual logging data, which shows the generalizability of this deep learning method on different datasets. [ABSTRACT FROM AUTHOR]

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach of deep-learning-based seismic horizon auto-picking that introduces a modified vector quantized variational autoencoder (VQ-VAE) framework to improve the accuracy of seismic horizon interpretation and, for the first time, quantitatively evaluate the uncertainty of the auto-picked horizon by exploiting the concept of entropy. Compared with the conventional VQ-VAE approach, the proposed method not only modifies the VQ-VAE model with more deep-learning channels at each layer of the network to enhance the performance of horizon auto-picking within the VQ-VAE framework, but also extends the 1D seismic labels with more continuous samplings within a single trace to boost the stability of auto-picked horizon in geologically complex settings and also significantly suppress the resulting uncertainty. To further improve the resulting accuracy in geologically complex settings, we introduce the directional structure tensor to extract a more reliable initial horizon and, moreover, a dilated horizon searching strategy to extend the capacity of the proposed method in dealing with the large fault displacement and reducing the computational cost simultaneously. Additionally, the resulting uncertainty quantitatively measured by entropy can also serve as an effective indicator to enable a further refinement of the auto-picked result accordingly. Both 2D example and 3D field applications are carried out to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Underground flow paths (UFP) often play an important role in the illustration of geological data by geologists, especially in illustrating geological data and revealing stratigraphic structures, which can help domain experts in their exploration of petroleum information. In this paper, we present a new immersive visualization tool to help domain experts better illustrate stratigraphic data. We use a visualization method based on bit-array-based 3-D texture to represent stratigraphic data. Our visualization tool has three major advantages: it allows for flexible interaction at the immersive device, it enables domain experts to obtain their desired UFP structure through the execution of quadratic surface queries, and supports different stratigraphic display modes, as well as switching and integration geological information flexibly. Feedback from domain experts has shown that our tool can contribute more for domain experts in the scientific exploration of stratigraphic data, compared to the existing UFP visualization tools in the field. Thus, experts in geology can have a more comprehensive understanding and more effective illustration of the structure and distribution of UFPs. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhoea virus (PEDV) is an emerging and re‐emerging epizootic virus of swine that causes substantial economic losses to the pig industry in China and other countries. The variations in the virus, and its co‐infections with other enteric viruses, have contributed to the poor control of PEDV infection. In the current study, a broad epidemiological investigation of PEDV was carried out in 22 provinces or municipalities of China during 2015–2018. The enteric viruses causing co‐infection with PEDV and the genetic diversity of the PEDV S1 gene were also analysed. The results indicated that, of the 543 diarrhoea samples, 66.85% (363/543) were positive for PEDV, and co‐infection rates of PEDV with 13 enteric viruses ranged from 3.58% (13/363) to 81.55% (296/363). Among these enteric viruses, the signs of diarrhoea induced by PEDV were potentially associated with co‐infections with porcine enterovirus 9/10 (PEV) and torque teno sus virus 2 (TTSuV‐2) (p <.05). The 147 PEDV strains identified in our study belong to Chinese pandemic strains and exhibited genetic diversity. The virulence‐determining S1 proteins of PEDV pandemic strains were undergoing amino acid mutations, in which S58_S58insQGVN–N135dup–D158_I159del‐like mutations were common patterns (97.28%, 143/147). When compared with 2011–2014 PEDV strains, the amino acid mutations of PEDV pandemic strains were mainly located in the N‐terminal domain of S1 (S1‐NTD), and 21 novel mutations occurred in 2017 and 2018. Furthermore, protein homology modelling showed that the mutations in pattern of insertion and deletion mutations of the S1 protein of PEDV pandemic strains may have caused structural changes on the surface of the S1 protein. These data provide a better understanding of the co‐infection and genetic evolution of PEDV in China. [ABSTRACT FROM AUTHOR]

Summary: Underground flow path (UFP) is one of the most significant stratigraphic structures in revealing the distribution of oil or gas from seismic data. We design a domain‐specific visualization system to extract the stratigraphic structures by seed point tracing and explore the seismic data by graph interactions. The seeds are automatically generated by kernel function–based density gradients computation. Users are allowed to adjust the recommended seeds by fine‐tuning them with visual interactions. The seeds are further merged by a weighted quick‐union algorithm to get the link information to construct a graph. Different types of nodes in the graph are designed to enable users to explore the extracted UFP structures intuitively. Finally, we evaluated the proposed approach by performance tests, sensitivity tests, and ground truth tests. The feedback from the domain experts demonstrates that the proposed visualization tool improved the capability of revealing the distribution and geostructures of UFPs compared with the existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To investigate the potential of SBL-EM in a seismic spectral sparse reflectivity inversion, we evaluate the inversion results by comparing them with those of a SBL-SA-based approach in multiple aspects, including the sensitivity to different frequency bands, the robustness to data noise, the lateral continuity of the final profiles and so on. Furthermore, we apply the mean square error (MSE), residual variance (RV) of seismograms and residual energy (RE) between the frequency spectra of the true and inverted reflectivity model to highlight the advantages of the proposed method over the SBL-SA-based approach in terms of spectral sparse reflectivity inversion within a sparse Bayesian learning framework. Multiple examples, including both numerical and field experiments, are carried out to validate the proposed method. [ABSTRACT FROM AUTHOR]

Summary: The emerging Porcine circovirus type 3 (PCV3) is associated with porcine dermatitis and nephropathy syndrome, reproductive failure and cardiac and multisystemic inflammation. To trace the prevalence and evolution of PCV3 in pigs with respiratory diseases or digestive diseases in China, 616 samples were collected from 21 provinces or municipalities of China from 2015 to 2017. All samples were analysed with PCR and a cap‐gene‐based phylogeny. The results indicated that the positive rate of PCV3 was 12.2% (75/616) at the sample level; 24.1% (42/174) at the farm level; 10.4% (50/480) in the digestive‐disease‐affected samples; 26.6% (25/94) in the respiratory‐disease‐affected samples; all 42 healthy samples were negative for PCV3. A statistical analysis showed that PCV3 infection was closely associated with both digestive diseases (p < 0.05) and respiratory diseases (p < 0.01). A sequence analysis revealed that the cap genes of the 51 PCV3 strains identified in our study shared nucleotide homologies of 97.2%–100% and amino acid homologies of 96.3%–100%. A total of 17 amino acid mutations were observed among the Cap proteins of the 51 PCV3 strains, of which R10/K, A24/V, R27/K, T77/S, F104/Y, I150/L are mutations among worldwide strains. A phylogenetic analysis demonstrated that the 51 PCV3 strains formed three clades, including PCV3a (15/51, 29.4%), PCV3b (21/51, 41.2%) and PCV3c (15/51, 29.4%). These data provide evidence that PCV3 exhibits high prevalence and genetic diversity and is associated with digestive diseases and respiratory diseases in pig. [ABSTRACT FROM AUTHOR]

Feline infectious peritonitis (FIP) is a fatal infectious disease of wild and domestic cats, and the occurrence of FIP is frequently reported in China. To trace the evolution of type I and II feline coronavirus in China, 115 samples of ascetic fluid from FIP‐suspected cats and 54 fecal samples from clinically healthy cats were collected from veterinary hospitals in China. The presence of FCoV in the samples was detected by RT‐PCR targeting the 6b gene. The results revealed that a total of 126 (74.6%, 126/169) samples were positive for FCoV: 75.7% (87/115) of the FIP‐suspected samples were positive for FCoV, and 72.2% (39/54) of the clinically healthy samples were positive for FCoV. Of the 126 FCoV‐positive samples, 95 partial S genes were successfully sequenced. The partial S gene‐based genotyping indicated that type I FCoV and type II FCoV accounted for 95.8% (91/95) and 4.2% (4/95), respectively. The partial S gene‐based phylogenetic analyses showed that the 91 type I FCoV strains exhibited genetic diversity; the four type II FCoV strains exhibited a close relationship with type II FCoV strains from Taiwan. Three type I FCoV strains, HLJ/HRB/2016/10, HLJ/HRB/2016/11 and HLJ/HRB/2016/13, formed one potential new clade in the nearly complete genome‐based phylogenetic trees. Further analysis revealed that FCoV infection appeared to be significantly correlated with a multi‐cat environment (p < 0.01) and with age (p < 0.01). The S gene of the three type I FCoV strains identified in China, BJ/2017/27, BJ/2018/22 and XM/2018/04, exhibited a six nucleotide deletion (C4035AGCTC4040). Our data provide evidence that type I and type II FCoV strains co‐circulate in the FIP‐affected cats in China. Type I FCoV strains exhibited high prevalence and genetic diversity in both FIP‐affected cats and clinically healthy cats, and a multi‐cat environment and age (<6 months) were significantly associated with FCoV infection. [ABSTRACT FROM AUTHOR]

Seismic data visualization and analysis can help the domain experts, e.g., geologists and oil or gas exploration experts, to explore the distribution of petroleum or gas. It assists them to get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP) and the contexts of UFPs (river delta, floodplain, slump fan, oil well, etc.). UFPs are one of the significant stratigraphic structures according to the domain experts, because they are closely related to the distribution and the migration of oil or gas. We design a quadratic-surface distance query scheme to explore UFPs and their contexts within a local region. First, it just needs to share parameters of quadratic surfaces to the rendering modules instead of all volume data or all subvolume data to conduct distance queries, and it is flexible to perform multiple complex logic operations through the quadratic surface-based queries. Second, it enables one to perform domain-specific interactions after distance queries such as the flexible switching of multiple display modes. Third, it enables one to perform local transfer function on different subvolumes different query results or their arbitrary combinations. We have evaluated the approach by comparing them with existing methods by performance evaluation and result evaluation. Results show that the proposed approach is capable of performing complex distance queries and fulfilling the domain-specific interactions getting better results and timing performance.© 2019 SPIE and IS&T [DOI:10.1117/1.JEI.28.2.021009] [ABSTRACT FROM AUTHOR]

To trace the evolution of canine coronavirus (CCoV), 201 stool samples from diarrheic dogs in northeast China were subjected to reverse transcription-polymerase chain reactions (RT-PCRs) targeting the partial M and S genes of CCoV, followed by an epidemiological analysis. M gene RT-PCRs showed that 28.36% (57/201) of the samples were positive for CCoV; of the 57 positive samples, CCoV-I and CCoV-II accounted for 15.79% (9/57) and 84.21% (48/57), respectively. A sequence comparison of the partial M gene revealed nucleotide homologies of 88.4%–100% among the 57 CCoV strains, and 88.7%–96.2% identity between the 57 CCoV strains and the Chinese reference strain HF3. The CCoV-I and CCoV-II strains exhibited genetic diversity when compared with reference strains from China and other countries. The 57 CCoV strains exhibited high co-infection rates with canine kobuvirus (CaKV) (33.33%) and canine parvovirus-2 (CPV-2) (31.58%). The CCoV prevalence in diarrheic dogs differed significantly with immunization status, regions, seasons, and ages. Moreover, 28 S genes were amplified from the 57 CCoV-positive samples, including 26 CCoV-IIa strains, one CCoV-IIb strain, and one CCoV-I strain. A sequence comparison of the partial S gene revealed 86.3%–100% nucleotide identity among the 26 CCoV-IIa strains, and 89.6%–92.2% identity between the 26 CCoV-IIa strains and the Chinese reference strain V1. The 26 CCoV-IIa strains showed genetic diversity when compared with reference strains from China and other countries. Our data provide evidence that CCoV-I, CCoV-IIa, and CCoV-IIb strains co-circulate in the diarrhoetic dogs in northeast China, high co-infection rates with CaKV and CPV-2 were observed, and the CCoV-II strains exhibited high prevalence and genetic diversity. [ABSTRACT FROM AUTHOR]

To trace evolution of canine parvovirus-2 (CPV-2), a total of 201 stool samples were collected from dogs with diarrhea in Heilongjiang province of northeast China from May 2014 to April 2015. The presence of CPV-2 in the samples was determined by PCR amplification of the VP2 gene (568 bp) of CPV-2. The results revealed that 95 samples (47.26%) were positive for CPV-2, and they showed 98.8%–100% nucleotide identity and 97.6%–100% amino acid identity. Of 95 CPV-2-positive samples, types new2a (Ser297Ala), new2b (Ser297Ala), and 2c accounted for 64.21%, 21.05%, and 14.74%, respectively. The positive rate of CPV-2 and the distribution of the new2a, new2b and 2c types exhibited differences among regions, seasons, and ages. Immunized dogs accounted for 48.42% of 95 CPV-2-positive samples. Coinfections with canine coronavirus, canine kobuvirus, and canine bocavirus were identified. Phylogenetic analysis revealed that the identified new2a, new2b, and CPV-2c strains in our study exhibited a close relationship with most of the CPV-2 strains from China; type new2a strains exhibited high variability, forming three subgroups; type new2b and CPV-2c strains formed one group with reference strains from China. Of 95 CPV-2 strains, Tyr324Ile and Thr440Ala substitutions accounted for 100% and 64.21%, respectively; all type new2b strains exhibited the Thr440Ala substitution, while the unique Gln370Arg substitution was found in all type 2c strains. Recombination analysis using entire VP2 gene indicated possible recombination events between the identified CPV-2 strains and reference strains from China. Our data revealed the co-circulation of new CPV-2a, new CPV-2b, and rare CPV-2c, as well as potential recombination events among Chinese CPV-2 strains. [ABSTRACT FROM AUTHOR]