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Automatic first‐break picking is a basic step in seismic data processing, so much so that the quality of the picking largely determines the effect of subsequent processing. To a certain extent, artificial intelligence technology has solved the shortcomings of traditional first‐break picking algorithms, such as poor applicability and low efficiency. However, some problems still remain for seismic data, with a low signal‐to‐noise ratio and large first‐break change leading to inaccurate picking and poor generalization of the network. In order to improve the accuracy of the automatic first‐break picking results of the above seismic data, we propose a multi‐view automatic first‐break picking method driven by multi‐network. First, we analysed the single‐trace boundary characteristics and the two‐dimensional boundary characteristics of the first break. Based on these two characteristics of the first break, we used the Long Short‐Term Memory and the ResNet attention gate UNet (resudual attention gate UNet) networks to extract the characteristics of the first arrival and its location from the seismic data, respectively. Then, we introduced the idea of multi‐network learning in the first‐break picking work and designed a feature fusion network. Finally, the multi‐view first‐break features extracted by the Long Short‐Term Memory and resudual attention gate UNet networks are fused, which effectively improves the picking accuracy. The results obtained after applying the method to field seismic data show that the accuracy of the first break detected by a feature fusion network is higher than that given by the above two networks alone and has good applicability and resistance to noise. [ABSTRACT FROM AUTHOR]

Climate change is always an immense challenge for conservation biologists in the development and implementation of effective conservation strategies. The expected loss of biodiversity due to climate change weakens the delivery of ecosystem functioning and services, which can lead to a global environmental crisis. This study aims to conduct a comprehensive review of literature on recent progress in climate change and biodiversity conservation research and identifies the leading countries, institutions and researchers in this research area. We also aim to explore the current research trends, and existing scientific knowledge as well as to provide perspectives on the topic. A scientometric analysis approach included co-occurrence analysis of countries, institutions, and authors for research productivity and academic development; co-occurrence of keywords for identification of research hotspots; co-citation analysis for prominent articles and journals; and keyword burst for detection of global research trends, was applied by using CiteSpace based on 1965 articles retrieved from the Web of Science Core Collection for the period 2000–2021. We found an increasing research production over time on climate change and biodiversity conservation research. Although climate change and biodiversity conservation research have been studied across the globe (particularly in developed countries such as the USA) and institutions (such as the University of Queensland, Australia), several more countries, regions and institutions are underrepresented across the globe. The current research hot topics we found are mainly “management”, “impact”, “diversity” and “land use”, whereas the research trends are “richness”, “scenario”, “perception” and “REDD”. Thus, further research is needed to cover the huge knowledge gap related to humans, climate change, biodiversity, and ecosystem functioning across the globe (particularly in unexplored countries and regions) through several interdisciplinary approaches and collaborations.

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 letter, 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. [ABSTRACT FROM AUTHOR]

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 letter, 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 multistage noise supervised framework to prevent noise from deteriorating the performance of deep model. Noise-supervised deep learning framework (NSDI) consists of clean training, weight initialization, and hybrid training stages. In the clean training stage, we train a segmentation model using a small amount of clean labels and remote sensing images to compute the confusion probability matrix. In the weight initialization stage, we use the confusion probability matrix and the prediction probability to calculate the label reliability of the electronic map. In the hybrid training stage, an adaptive weighted loss function based on cross-entropy is used to dynamically update the label reliability. Then we train the model further using electronic maps with the support of the adaptive weighted loss function and label reliability. Experiments were undertaken on 2656 images of 512 × 512 pixels. Ablation studies show that NSDI improves the model robustness and the segmentation quality. [ABSTRACT FROM AUTHOR]

The Sichuan Basin is a significant region for exploration and development of shale gas in China, and it is essential to clarify the impact of deep shale gas reservoir parameters on cost-effective development at scale to ensure national energy security. Rock physics modeling is a significant means of communicating the physical and elastic parameters of rocks. A rock physics modeling method applicable to fractured shale gas reservoirs is proposed for the current situation of complex fluid relationships in shale gas reservoirs and unclear characteristics of gas identification seismic response. In this paper, based on the Self Consistent Approximation (SCA) model and the differential effective medium (DEM) model, the anisotropic source of shale is used as a starting point to add bound water, kerogen, clay, and brittle minerals, the Schoenberg linear slip theory is used to add fracture disturbance effects, and then the Brown–Korringa model is used to perform fluid replacement under anisotropic conditions. Finally a rock physics model applicable to fractured shale gas reservoirs is obtained, and the established rock physics model is used for analysis of elastic parameters, Thomsen parameters, and fracture weakness parameters. Rock physics tests were performed on shale in southern Sichuan as an example. The experimental results show that the model established by the process can accurately invert the longitudinal and transverse wave velocities of the shale, which can provide a conceptual basis for the study of fractured shale gas reservoirs. [ABSTRACT FROM AUTHOR]

Brittleness is an important factor that indicates shale properties, as well as fracturability, and it can be well-represented using the elasticity parameter Eρ. Seismic inversion allows direct access to the Eρ ofbrittleness parameters. Seismic inversion is a typical ill-posed problem that has an enormous multiplicity of solutions. In order to invert reservoir brittleness parameters more stably and reliably, a direct inversion method for determining brittleness parameters based on a reweighted Lp-norm is proposed, and the reweighted Lp method is introduced to brittleness parameter inversion for the first time. The alternating direction method of multipliers (ADMM) is used to establish the inversion structure and to optimize the objective function in blocks, which effectively improves the convergence speed. We first introduce a reweighted Lp method and establish a pre-stack inversion objective function based on the reweighted Lp method. Then, theoretical simulation data are applied to compare the inversion outcomes of the new method with those of the traditional method, and the effect of the method in this manuscript is verified. Finally, the feasibility of this method is further verified using actual data for experimental analysis. Through an analysis of the experimental results, we find that this method can be well-applied to seismic pre-stack inversion calculation and provides a new direct inversion method for the determination of brittleness parameters for exploration geophysics. [ABSTRACT FROM AUTHOR]

This paper presents a novel trans-critical CO2 heat pump cycle with expander (TCCE) for increasing COP in the warm period and the suction preheating in the cold period. This cycle has two main operation modes. One is TCCEA for warm period and the other is TCCEB for cold period. Theoretical simulations are conducted to study energy efficiency and exergy efficiency of this novel cycle as well as the basic cycle. The results show that in the warm period TCCEA has an excellent COP and exergy efficiency and at low discharge pressure, the COP of TCCEA is improved by up to 31.49% compared with the basic cycle. Additionally, in the cold period, using TCCEB is helpful to avoid liquid suction. Compared with the basic cycle, the heating capacity of TCCEB can be improved by up to 12%. Taking the heating capacity into account, TCCEB is the best system under the warm period. According to the simulation results, this novel cycle is better suited for warm or cold weather. [ABSTRACT FROM AUTHOR]