Geothermometry and geobarometry of overpressured lower Paleozoic gas shales in the Jiaoshiba field, Central China: insight from fluid inclusions in fracture cements

The Wufeng-Longmaxi organic-rich shales host the largest shale gas fields of China. This study examines sealed fractures within core samples of the Wufeng-Longmaxi shales in the Jiaoshiba shale gas field in order to understand the development of overpressures (in terms of magnitude, timing and burial) in Wufeng-Longmaxi shales and thus the causes of present-day overpressure in these Paleozoic shale formations as well as in all gas shales. Quartz and calcite fracture cements from the Wufeng-Longmaxi shale intervals in four wells at depth intervals between 2253.89 m and 3046.60 m were investigated, and the fluid composition, temperature, and pressure during natural fracture cementation determined using an integrated approach consisting of petrography, Raman spectroscopy and microthermometry. Many crystals in fracture cements were found to contain methane inclusions only, and aqueous two-phase inclusions were consistently observed alongside methane inclusions in all cement samples, indicating that fluid inclusions trapped during fracture cementation are saturated with a methane hydrocarbon fluid. Homogenization temperatures of methane-saturated aqueous inclusions provide trends in trapping temperatures that Th values concentrate in the range of 198.5 °C–229.9 °C, 196.2 °C-221.7 °C for quartz and calcite, respectively. Pore-fluid pressures of 91.8–139.4 MPa for methane inclusions, calculated using the Raman shift of C-H symmetric stretching (v1) band of methane and equations of state for supercritical methane, indicate fluid inclusions trapped at near-lithostatic pressures. High trapping temperature and overpressure conditions in fluid inclusions represent a state of temperature and overpressure of Wufeng-Longmaxi shales at maximum burial and the early stage of the Yanshanian uplift, which can provide a key evidence for understanding the formation and evolution of overpressure. Our results demonstrate that the main cause of present-day overpressure in shale gas deposits is actually the preservation of moderate-high overpressure developed as a result of gas generation at maximum burial depths

FFSDF: An improved fast face shadow detection framework based on channel spatial attention enhancement

Recent shadow detectors excel on simple datasets but encounter difficulties with facial shadow images under complex lighting due to the lack of annotated shadow masks, varying shadow sizes, and imbalances between the target and background. This leads to training difficulties, reduced accuracy, and slower processing, posing a significant challenge for precise and fast detection framework development. We collected images and created Extended Yale Shadow Detection Dataset (EYSDD). In comparison to other datasets, this dataset includes additional manually annotated shadow masks, making it suitable for training convolutional neural networks. To address this problem, we propose incorporating Channel Spatial Direction-aware Spatial Context (CSDSC) module into Fast Shadow Detection Network (FSDNet). Additionally, we introduce Selective Attention Inverted Residual Bottleneck (SAIRB) with Selective Attention Mechanism (SAM). Furthermore, we integrate Detail Enhancement Module (DEM), which refines low-level features, into Fast Face Shadow Detection Framework (FFSDF). Finally, compared to other methods, our model surpasses the baseline method FSDNet and the advanced method EVP by 3.5% and 1.9% in terms of IoU, and 1.8% and 4.3% in terms of Dice score, respectively. Our model has only 4.31 M parameters and achieves a computing speed of 0.022 sec/image, demonstrating superior efficiency compared to other methods

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf (including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-of-the-art methods

Facial expression recognition considering individual differences in facial structure and texture

Facial expression recognition (FER) plays an important role in human–computer interaction. The recent years have witnessed an increasing trend of various approaches for the FER, but these approaches usually do not consider the effect of individual differences to the recognition result. When the face images change from neutral to a certain expression, the changing information constituted of the structural characteristics and the texture information can provide rich important clues not seen in either face image. Therefore it is believed to be of great importance for machine vision. This study proposes a novel FER algorithm by exploiting the structural characteristics and the texture information hiding in the image space. Firstly, the feature points are marked by an active appearance model. Secondly, three facial features, which are feature point distance ratio coefficient, connection angle ratio coefficient and skin deformation energy parameter, are proposed to eliminate the differences among the individuals. Finally, a radial basis function neural network is utilised as the classifier for the FER. Extensive experimental results on the Cohn–Kanade database and the Beihang University (BHU) facial expression database show the significant advantages of the proposed method over the existing ones

Facial expression recognition considering individual differences in facial structure and texture

Facial expression recognition (FER) plays an important role in human-computer interaction. The recent years have witnessed an increasing trend of various approaches for the FER, but these approaches usually do not consider the effect of individual differences to the recognition result. When the face images change from neutral to a certain expression, the changing information constituted of the structural characteristics and the texture information can provide rich important clues not seen in either face image. Therefore it is believed to be of great importance for machine vision. This study proposes a novel FER algorithm by exploiting the structural characteristics and the texture information hiding in the image space. Firstly, the feature points are marked by an active appearance model. Secondly, three facial features, which are feature point distance ratio coefficient, connection angle ratio coefficient and skin deformation energy parameter, are proposed to eliminate the differences among the individuals. Finally, a radial basis function neural network is utilised as the classifier for the FER. Extensive experimental results on the Cohn-Kanade database and the Beihang University (BHU) facial expression database show the significant advantages of the proposed method over the existing ones

A hierarchical birdsong feature extraction architecture combining static and dynamic modeling

To conserve bird biodiversity and monitor the distribution of species in the region, it is of tremendous necessity to identify birds by their songs and explore the rich ecological information birdsong contains. The audios recorded in the monitoring area generally have complex background noise, the characteristics of the song are not prominent and the biological spectrum information is not comprehensive, which brings some challenges to the identification of birds. This study proposes a hierarchical birdsong feature extraction architecture combining dynamic and static modeling to cope with complex environments as a modeling context. Firstly, six common speech features were extracted for the characteristics of birdsong. The Pearson correlation coefficient is then used to analyze the correlations between birdsong and human speech, examining the correlations between each feature in the presence and absence of environmental noise interference. Combined with the scatter plot matrix analysis, we conclude that Mel Frequency Cepstral Coefficient (MFCC) is more suitable comparing with other features when dealing with birdsong and can superiorly cope with a complex background noise. Secondly, a feature extraction architecture is built, which integrates static and dynamic modeling to fully explore the contextual relationship, to solve the problem of ignoring the internal structure information of the patch and losing some spatial information in the Transformer-type model. Finally, a hierarchical refinement module is designed to help extract more detailed features, as well as to optimize the computational cost of the Transformer-type model that requires many training data and has high complexity. The performance of the model can be detected with 93.67 % accuracy on the self-built birdsong dataset, 95.19 % accuracy on the public birdsong dataset Birdsdata and 97.02 % accuracy on the public environmental dataset UrbanSound8k

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques

The location accuracies for different methods.

<p>(a) Gray image. (b) Color image.</p