Research on the differential tectonic-thermal evolution of Longmaxi shale in the southern Sichuan Basin

The southern Sichuan Basin in China holds abundant shale gas resources; however, the shale gas bearing property shows great differences due to the multiple stages of tectonic transformation. The key to revealing the shale gas differential enrichment mechanism is to explore the thermal evolution characteristics during tectonic evolution. Therefore, taking the Luzhou and Changning blocks as an example, which have obvious differences in tectonic evolution, the organic geochemical conditions of Longmaxi shale were firstly compared with the test data. Then, the thermal evolution characteristics under the background differential tectonic uplift-erosion were recovered using basin modeling techniques. The results showed that the two blocks contain similar organic geochemical conditions of the Longmaxi shale. Moreover, the hydrocarbon generation condition in Luzhou Block is greater than that in the Changning Block. Influenced by the differential tectonic evolution, the study area experienced a complex burial history and the formation of multiple unconformities. As a result, the present burial depth of Longmaxi Formation in the Luzhou Block is significantly greater than that in the Changning Block. The thermal evolution history of Longmaxi shale in the study area could be divided into three stages, including a low-temperature stage from Caledonian to Hercynian, a middle-temperature stage from Hercynian to Indosinian, and a high-temperature stage from Yanshanian to Himalayan. In addition, it was found that the Himalayan period is the main stage resulting in the differential gas bearing property of Longmaxi shale in the southern Sichuan area. Under the differential structural modification, the peak time of hydrocarbon generation in the Luzhou Block occurred earlier and the conversion rate was slightly higher than that in the Changning Block.Cited as: Zhao, L., Mao, W., Liu, Z., Cheng, S. Research on the differential tectonic-thermal evolution of Longmaxi shale in the southern Sichuan Basin. Advances in Geo-Energy Research, 2023, 7(3): 152-163. https://doi.org/10.46690/ager.2023.03.0

A SURVEY OF THE C-14 CONTENT OF DISSOLVED INORGANIC CARBON IN CHINESE LAKES

We present radiocarbon (C-14) measurements of dissolved inorganic carbon (DIC) from surface waters of 11 lakes, widely distributed in China. Surface lake water DIC (FC)-C-14 values show distinct differences, and we relate these to the physical exchange character ("open" or "closed") of each lake. Open lakes studied here generally have lower DIC (FC)-C-14 values than closed lakes. We present a simple model of a lake water cycle to calculate an average residence time for each lake. Comparisons between lake DIC (FC)-C-14 and average residence time shows that the DIC (FC)-C-14 increases with the average residence time and reflects a steady-state

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and capability of utilizing very limited or no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a novel multi-modal medical foundation model that explores masked contrastive learning to achieve granular alignment and zero-shot learning for a variety of medical imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust the correlation between masked image patches and their corresponding reports, thereby enhancing the representation learning capabilities. We evaluate MaCo on six well-known open-source X-ray datasets, and the experimental results show it outperforms seven state-of-the-art approaches for classification, segmentation, and zero-shot phase grounding, demonstrating its great potential to promote a wide range of medical image analysis tasks

Simulations of summertime fossil fuel CO2 in the Guanzhong basin, China

Recent studies on fossil fuel CO2 simulation associated with Delta(CO2)-C-14 measurements is quite limited, particularly in China. In this study, the fossil fuel CO2 recently added to the atmosphere (delta CO(2)ff) over the Guanzhong basin, central China, during summer 2012 is simulated using a modified WRF-CHEM model constrained by measured CO2 mixing ratio and Delta(CO2)-C-14. The model well captures the temporal variation of observed CO2 mixing ratio and Delta(CO2)-C-14, and reasonably reproduces the distribution of observed Delta(CO2)-C-14. The simulation shows a significant variation of delta CO(2)ff during summertime, ranging from <5 ppmv to similar to 100 ppmv and no remarkable trend of delta CO(2)ff is found for June, July, and August. The delta CO(2)ff level is closely associated with atmospheric diffusion conditions. The diurnal cycle of delta CO(2)ff presents a double-peak pattern, a nocturnal one and a rush-hour one, related to the development of planetary boundary layer and CO2 emission from vehicles. The spatial distributions of summertime delta CO(2)ff within the basin is clearly higher than the outside, reaching up to 40 ppmv in urban Xi'an and 15 ppmv in its surrounding areas, indicative of large local fossil fuel emissions. Furthermore, we find that neglecting the influence of summer heterotrophic respiration in terrestrial biosphere would slightly underestimate the calculated delta CO(2)ff by about 0.38 ppmv in the basin. (C) 2017 Elsevier B.V. All rights reserved

CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke

Segmenting stroke lesions from T1-weighted MR images is of great value for large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there are great challenges with this task, such as large range of stroke lesion scales and the tissue intensity similarity. The famous encoder-decoder convolutional neural network, which although has made great achievements in medical image segmentation areas, may fail to address these challenges due to the insufficient uses of multi-scale features and context information. To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images. Specifically, a Cross-Level feature Fusion (CLF) strategy was developed to make full use of different scale features across different levels; Extending Atrous Spatial Pyramid Pooling (ASPP) with CLF, we have enriched multi-scale features to handle the different lesion sizes; In addition, convolutional long short-term memory (ConvLSTM) is employed to infer context information and thus capture fine structures to address the intensity similarity issue. The proposed approach was evaluated on an open-source dataset, the Anatomical Tracings of Lesions After Stroke (ATLAS) with the results showing that our network outperforms five state-of-the-art methods. We make our code and models available at https://github.com/YH0517/CLCI_Net

A 550,000-year record of East Asian monsoon rainfall from Be-10 in loess

Cosmogenic Be-10 flux from the atmosphere is a proxy for rainfall. Using this proxy, we derived a 550,000-year-long record of East Asian summer monsoon (EASM) rainfall from Chinese loess. This record is forced at orbital precession frequencies, with higher rainfall observed during Northern Hemisphere summer insolation maxima, although this response is damped during cold interstadials. The Be-10 monsoon rainfall proxy is also highly correlated with global ice-volume variations, which differs from Chinese cave delta O-18, which is only weakly correlated. We argue that both EASM intensity and Chinese cave delta O-18 are not governed by high-northern-latitude insolation, as suggested by others, but rather by low-latitude interhemispheric insolation gradients, which may also strongly influence global ice volume via monsoon dynamics

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on feature learning at a single learning scale, investigation on integrating multi-scale information is lacking, which may hinder the potential for mutual reinforcement among these features. This paper aims to bridge this gap by proposing a method that effectively exploits multi-scale information to enhance the performance of medical foundation models. The proposed method simultaneously exploits features at the local, instance, modality and global aspects, facilitating comprehensive representation learning within the models. We evaluate the effectiveness of the proposed method on six open-source datasets across different clinical tasks, demonstrating its ability to enhance the performance of medical foundation models