ARTICLE | doi:10.20944/preprints202206.0184.v1
Subject: Environmental And Earth Sciences, Geochemistry And Petrology Keywords: Over mature shale gas; Magnitude of isotope reversal; CH4 polymerization; CH4 cracking; Mud gas
Online: 13 June 2022 (10:41:14 CEST)
Exploration practices have proven that over mature shale gas exhibits a feature of carbon isotope reversal. The geochemical statistics indicate that the wetness (C2-C5/C1-C5) of shale gas with carbon isotope reversal is less than 1.8%. In addition, the magnitude of carbon isotope reversal (δ13C1- δ13C2) for the over mature shale gas presents a parabolic variation with decreasing wetness. δ13C1-δ13C2 increases with decreasing wetness within a wetness range of 0.9% ~1.8% and then decreases with decreasing wetness at wetness < 0.9%. The CH4 cracking experiment demonstrates that CH4 polymerization occurring in the early stage of CH4 cracking is an important factor involved in isotope reversal of over mature shale gas. Moreover, δ13C1- δ13C2 decreases with an increase in experimental temperature prior to CH4 substantial cracking. The values of δ13C1 and δ13C2 tend to equalize during CH4 substantial cracking. The δ13C1-δ13C2 of mud gas present at different depths during shale gas drilling in Sichuan Basin increases initially, then decreases with further increase in the depth and finally tends to zero, with only a trace hydrocarbon gas being detectable. Statistical data suggests that the shale gas production in Sichuan Basin decreases with the decreasing δ13C1-δ13C2 value and wetness. Thus, δ13C1-δ13C2 and wetness could potentially serve as useful criteria to screen CH4 cracking degree and to determine the largest depth of natural gas exploration. Great care should be taken during shale gas exploration in deeper layers, with wetness and δ13C1-δ13C2 less than 0.2% and 1%, respectively, since very low wetness (<0.2%) and δ13C1-δ13C2 (<1%) might be indicative of CH4 substantial cracking in a geological setting.
ARTICLE | doi:10.20944/preprints202304.0086.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Image fusion; generative adversarial network (GAN); local binary patterns (LBP); multi-modal images
Online: 6 April 2023 (10:03:31 CEST)
Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.