Determining the high-temperature history of rocks and evaluating their associated deterioration levels are essential for the stable and efficient functioning of geological engineering projects.This research introduces...Determining the high-temperature history of rocks and evaluating their associated deterioration levels are essential for the stable and efficient functioning of geological engineering projects.This research introduces a precise,time-efficient,and cost-effective approach that integrates metal intrusion technology,backscattered electron(BSE)imaging,and the ResNet50 deep-learning algorithm to differentiate high-temperature histories.The damage characteristics in the microstructure of rocks subjected to different temperature treatments are successfully extracted.The results show that,compared to previously reported convolutional neural networks(CNN)training and classification methods,the proposed ResNet50 algorithm improves identification accuracy by over 10%,achieving up to 98%accuracy in classifying degraded sandstone treated at temperatures ranging from 25℃ to 1000℃.More importantly,through feature extraction of sandstone specimens after high-temperature deterioration,the ResNet50 algorithm demonstrates a superior ability to locate microscopic damage characteristics associated with different temperatures-an achievement rarely reported in previous research.For sandstone specimens exposed to 200℃-600℃,the extracted features primarily highlight the opening of primary pores and changes in rock particle morphology.In contrast,as the treated temperature exceeds 600℃,the extracted features predominantly reflect thermal damage fracture,whose area first diffuses and then concentrates,aligning closely with the thermal damage theory of rock.The findings of this study not only advance a deep learning-based approach for identifying rock deterioration after high-temperature exposure but also deepen the understanding of the relation between rock microstructural characteristics and high-temperature deterioration.展开更多
The rich accumulation of methane(CH_(4))in tectonic coal layers poses a significant obstacle to the safe and efficient extraction of coal seams and coalbed methane.Tectonic coal samples from three geologically complex...The rich accumulation of methane(CH_(4))in tectonic coal layers poses a significant obstacle to the safe and efficient extraction of coal seams and coalbed methane.Tectonic coal samples from three geologically complex regions were selected,and the main results obtained by using a variety of research tools,such as physical tests,theoretical analyses,and numerical simulations,are as follows:22.4–62.5 nm is the joint segment of pore volume,and 26.7–100.7 nm is the joint segment of pore specific surface area.In the dynamic gas production process of tectonic coal pore structure,the adsorption method of methane molecules is“solid–liquid adsorption is the mainstay,and solid–gas adsorption coexists”.Methane stored in micropores with a pore size smaller than the jointed range is defined as solid-state pores.Pores within the jointed range,which transition from micropore filling to surface adsorption,are defined as gaseous pores.Pores outside the jointed range,where solid–liquid adsorption occurs,are defined as liquid pores.The evolution of pore structure affects the methane adsorption mode,which provides basic theoretical guidance for the development of coal seam resources.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52408271 and 52174092)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20230615).
文摘Determining the high-temperature history of rocks and evaluating their associated deterioration levels are essential for the stable and efficient functioning of geological engineering projects.This research introduces a precise,time-efficient,and cost-effective approach that integrates metal intrusion technology,backscattered electron(BSE)imaging,and the ResNet50 deep-learning algorithm to differentiate high-temperature histories.The damage characteristics in the microstructure of rocks subjected to different temperature treatments are successfully extracted.The results show that,compared to previously reported convolutional neural networks(CNN)training and classification methods,the proposed ResNet50 algorithm improves identification accuracy by over 10%,achieving up to 98%accuracy in classifying degraded sandstone treated at temperatures ranging from 25℃ to 1000℃.More importantly,through feature extraction of sandstone specimens after high-temperature deterioration,the ResNet50 algorithm demonstrates a superior ability to locate microscopic damage characteristics associated with different temperatures-an achievement rarely reported in previous research.For sandstone specimens exposed to 200℃-600℃,the extracted features primarily highlight the opening of primary pores and changes in rock particle morphology.In contrast,as the treated temperature exceeds 600℃,the extracted features predominantly reflect thermal damage fracture,whose area first diffuses and then concentrates,aligning closely with the thermal damage theory of rock.The findings of this study not only advance a deep learning-based approach for identifying rock deterioration after high-temperature exposure but also deepen the understanding of the relation between rock microstructural characteristics and high-temperature deterioration.
基金supported by the National Natural Science Foundation of China(52164015)the Technology Funding Projects of Guizhou Province([2022]231).
文摘The rich accumulation of methane(CH_(4))in tectonic coal layers poses a significant obstacle to the safe and efficient extraction of coal seams and coalbed methane.Tectonic coal samples from three geologically complex regions were selected,and the main results obtained by using a variety of research tools,such as physical tests,theoretical analyses,and numerical simulations,are as follows:22.4–62.5 nm is the joint segment of pore volume,and 26.7–100.7 nm is the joint segment of pore specific surface area.In the dynamic gas production process of tectonic coal pore structure,the adsorption method of methane molecules is“solid–liquid adsorption is the mainstay,and solid–gas adsorption coexists”.Methane stored in micropores with a pore size smaller than the jointed range is defined as solid-state pores.Pores within the jointed range,which transition from micropore filling to surface adsorption,are defined as gaseous pores.Pores outside the jointed range,where solid–liquid adsorption occurs,are defined as liquid pores.The evolution of pore structure affects the methane adsorption mode,which provides basic theoretical guidance for the development of coal seam resources.
