Research progress and application of deep in-situ condition preserved coring and testing

With the depletion of shallow resources,the exploration of deep earth resources has become a global strategy.The study of the different patterns in the physical mechanical properties of rocks at different occurrence depths is the basis for exploring deep into the earth,with the core and premise being the acquisition and testing of deep in-situ core specimens.Based on the original idea of deep in-situ condition preserved coring(ICP-Coring)and testing,combined with theoretical modeling,numerical analysis,test platform development,indoor testing and engineering application,the principles and technologies of deep ICP-Coring are developed.This principle and technology consists of five parts:in-situ pressurepreserved coring(IPP-Coring),in-situ substance-preserved coring(ISP-Coring),in-situ temperaturepreserved coring(ITP-Coring),in-situ light-preserved coring(ILP-Coring),and in-situ moisturepreserved coring(IMP-Coring).The theory and technology of temperature and pressure reconstruction at different occurrence depths and in different environments are proposed,and prototype trial production was completed by following the principle of displacement and tests based on the in-situ reconstructed environment.The notable advances are as follows:(1)Deep in-situ coring system:A pressure-preserved controller with an ultimate bearing capacity greater than 140 MPa,highperformance(temperature-resistant,pressure-resistant,and low thermally conductive)temperaturepreserved materials,an active temperature control system,and high-barrier quality-preserved membrane materials were developed;a deep ICP-Coring capacity calibration platform was independently developed,a deep in-situ coring technology system was developed,and the acquisition of deep in-situ cores was realized.(2)In-situ storage displacement system:Following the dual-circuit hydraulic design idea,a single-drive source push-pull composite grabbing mechanism was designed;the design of the overall structure for the deep in-situ displacement storage system and ultrahigh pressure cabin structure was completed,which could realize docking the coring device and core displacement in the in-situ reconstructed environment.(3)Test analysis system:A noncontact acoustic-electric-magnetic test system was developed under the in-situ reconstructed environment,and the errors between the test results and traditional contact test results were mostly less than 10%;a detachable deep in-situ core true triaxial test system was developed,which could perform loading tests for deep in-situ cores.The relevant technological achievements were successfully applied to the exploration and development of deep resources,such as deep mines,deep-sea natural gas hydrates,and deep oil and gas.The research results provide technical and equipment support for the construction of a theoretical system for deep in-situ rock mechanics,the development of deep earth resources and energy,and the scientific exploration of different layers and occurrence depths(deep and ultradeep)of the Earth.

Hollow glass microspheres/silicone rubber composite materials toward materials for high performance deep in-situ temperaturepreserved coring

Deep petroleum resources are stored under high temperature and pressure conditions,with the temperature having a significant influence on the properties of rocks.Deep in-situ temperature-preserved coring(ITP-coring)devices were developed to assess deep petroleum reserves accurately.Herein,hollow glass microspheres(HGMs)/silicone rubber(SR)composites that exhibit excellent thermal insulation properties were prepared as thermal insulation materials for deep ITP-coring devices.The mechanism and process of heat transfer in the composites were explored,as well as their other properties.The results show that the HGMs exhibit good compatibility with the SR matrix.When the volume fraction of the HGMs is increased to 50%,the density of the HGMs/SR composites is reduced from 0.97 to 0.56 g/cm^(3).The HGMs filler introduces large voids into the composites,reducing their thermal conductivity to 0.11 W/m·K.The addition of HGMs into the composites further enhances the thermal stability of the SR,wherein the higher the HGMs filler content,the better the thermal stability of the composites.HGMs significantly enhance the mechanical strength of the SR.HGMs increase the compressive strength of the composites by 828%and the tensile strength by 164%.Overall,HGMs improve the thermal insulation,pressure resistance,and thermal stability of HGMs/SR composites.

Contact performance analysis of pressure controller's sealing interface in deep in-situ pressure-preserved coring system

The sealing performance of contact interfaces plays the most important role in the design and operation of the in-situ pressure-preserved coring system.To meet the demand of ultra-high pressure-retained coring for oil and gas exploration in deep reservoirs,a quantitative analysis of the contact mechanical behavior of the pressure controller was performed.Based on the micro-contact theory of rough surfaces,a three-dimensional numerical model of the rough contact interface between the valve cover and the valve seat was constructed,and the micro-contact behavior of the metal contact surfaces was comprehensively studied.The results show that the actual contact area of the valve interface increases with the increase of surface roughness before the critical contact point,but decreases after that.Compared with the real contact model with double rough surfaces,although the simplified hard-contact model with a single rough surface can reflect the micro-contact behavior of the rough surface to a certain extent,it cannot truly reveal the microchannel morphology between the sealing interfaces under pressure.Therefore,the realistic double-rough-surface model should be recommended to evaluate the sealing performance of coring tools,particularly for high pressure conditions.The material properties of valves have a significant effect on the contact characteristics of rough surfaces,which suggested that the actual contact area decreases with the increase of the elastic modulus of the contact material under the same loading conditions.The knowledge of this work could help to enhance the seal design of pressure controllers for in-situ pressure-preserved coring.

基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建

为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、Light GBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型,准确率达89.00%。本研究将人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。

In-situ observations of damage-fracture evolution in surrounding rock upon unloading in 2400-m-deep tunnels

The damage-fracture evolution of deep rock mass has obvious particularity,which is revealed in 2400-mdeep tunnels by field tests.The evolution of the excavation damaged zone depth is consistent with that of the fractured zone depth.The ratio of the excavation damaged zone depth to the excavation fractured zone depth is greater than 2.0 in a rock mass with both high strength and good integrity,but less than1.5 in a rock mass with lower strength or poor integrity.Zonal disintegration in a rock mass with high strength and fair integrity is more likely to occur when it contains more than two groups of primary fractures in damaged zones.Fractures develop outward in zonal disintegration but are totally different from the single-zone fracture,in which the fractures develop inward,and it is the starting position of the fractured zone when the excavation surface of the middle pilot is 7–9 m close to the pre-set borehole and it stops after the excavation surface of the baseplate is 11–14 m away.The most intense evolution occurs around 2–4 m from the pre-set borehole in the sidewall expansion stage.The research results provide a reference for the monitoring scheme and support design of CJPL-Ⅲin its future construction.

Design and development of the deep-rock in-situ condition-preserved coring calibration platform

To systematically validate and calibrate the theory and technology of the deep in-situ conditionpreserved coring, the in-situ conditions at different depths should be simulated, and the full-size coring tests should be carried out in this simulated environment. Therefore, a deep-rock in-situ conditionpreserved coring calibration platform was designed and developed. The self-tightening sealing structure and the quick-disassembly structure were designed on the basis of an innovative segmented nonuniformdiameter structure, which was a breakthrough from the traditional high-pressure vessel frame and was verified by finite element simulation and actual testing under extreme working conditions, respectively.To simulate the actual deep in-situ environment with a temperature of 150℃ and pressure of 140 MPa for a large Φ450 mm×H1400 mm core, temperature and pressure control systems were designed by coupling, and a pre-embedded high-pressure-resistant temperature sensor was designed. Finally, highprecision assembly automation, complex movement coordination of the coring device with the platform,and rotary dynamic sealing were achieved by utilizing the combination of adaptive cabin body servo control and an adaptive mechanical structure in a limited space, laying a solid foundation for the calibration of in-situ condition-preserved coring.

基于DeepLabv3+的船体结构腐蚀检测方法

利用图像识别方法对无人机、机器人所采集的实时图像开展船体结构腐蚀检测,可有效提高检验检测效率和数字化、智能化水平,具有极大的应用价值和潜力,将改变传统的船体结构检验检测方式。提出一种基于DeepLabv3+的船体结构腐蚀检测模型,通过收集图像样本并进行三种腐蚀类别的分割标注,基于DeepLabv3+语义分割模型进行网络的训练,预测图片中腐蚀的像素点类别和区域,模型在测试集的精准率达到52.92%,证明了使用DeepLabv3+检测船体腐蚀缺陷的可行性。

基于M-DeepLab网络的速度建模技术研究

本文提出了一种适用于速度建模方法的M-DeepLab网络框架,该网络将地震炮集记录作为输入,网络主体使用轻量级MobileNet,以此提升网络训练速度;并在编码环节ASPP模块后添加了Attention模块,且在解码环节将不同网络深度的速度特征进行了融合,既获得了更多的速度特征,又保留了网络浅部的速度信息,防止出现网络退化和过拟合问题。模型测试证明,M-DeepLab网络能够实现智能、精确的速度建模,简单模型、复杂模型以及含有噪声数据复杂模型的智能速度建模,均取得了良好的效果。相较DeepLabV3+网络,本文方法对于速度模型界面处的预测,特别是速度突变区域的预测,具有更高的预测精度,从而验证了该方法精确性、高效性、实用性和抗噪性。

In-situ pressure-preserved coring for deep exploration:Insight into the rotation behavior of the valve cover of a pressure controller

In-situ pressure-preserved coring(IPP-Coring)is considered to be the most reliable and efficient method for the identification of the scale of oil and gas resources.During IPP-Coring,because the rotation behavior of the pressure controller valve cover in different medium environments is unclear,interference between the valve cover and inner pipe may occur and negatively affect the IPP-Coring success rate.To address this issue,we conducted a series of indoor experiments employing a high-speed camera to gain greater insights into the valve cover rotation behavior in different medium environments,e.g.,air,water,and simulated drilling fluids.The results indicated that the variation in the valve cover rotation angle in the air and fluid environments can be described by a one-phase exponential decay function with a constant time parameter and by biphasic dose response function,respectively.The rotation behavior in the fluid environments exhibited distinct elastic and gravitational acceleration zones.In the fluid environments,the density clearly impacted the valve cover closing time and rotation behavior,whereas the effect of viscosity was very slight.This can be attributed to the negligible influence of the fluid viscosity on the drag coefficient found in this study;meanwhile,the density can increase the buoyancy and the time period during which the valve cover experienced a high drag coefficient.Considering these results,control schemes for the valve cover rotation behavior during IPP-Coring were proposed for different layers and geological conditions in which the different drilling fluids should be used,e.g.,the use of a high-density valve cover in high-pore pressure layers.

Influence of complicated faults on the differentiation and accumulation of in-situ stress in deep rock mass

High geostress will become a normality in the deep because in-situ stress rises linearly with depth.The geological structure grows immensely intricate as depth increases.Faults,small fractures,and joint fissures are widely developed.The objective of this paper is to identify geostress anomalies at a variety of locations near faults and to demonstrate their accumulation mechanism.Hydrofracturing tests were conducted in seven deep boreholes.We conducted a test at a drilling depth of over one thousand meters to reveal and quantify the influence of faults on in-situ stresses at the hanging wall,footwall,between faults,end of faults,junction of faults,and far-field of faults.The effect of fault sites and characteristics on the direction and magnitude of stresses has been investigated and compared to test boreholes.The accumulation heterogeneity of stresses near faults was illustrated by a three-dimensional numerical simulation,which is utilized to explain the effect of faults on the accumulation and differentiation of in-situ stress.Due to regional tectonics and faulting,the magnitude,direction,and stress regime are all extremely different.The concentration degree of geostress and direction change will vary with the location of faults near faults,but the magnitude and direction of in-situ stress conform to regional tectonic stress at a distance from the faults.The focal mechanism solution has been verified using historical seismic ground motion vectors.The results demonstrate that the degree of stress differentiation varies according to the fault attribute and its position.Changes in stress differentiation and its ratio from strong to weak occur between faults,intersection,footwall,end of faults,and hanging wall;along with the sequence of orientation is the footwall,between faults,the end of faults,intersection,and hanging wall.This work sheds new light on the fault-induced stress accumulation and orientation shift mechanisms across the entire cycle.

Dendritic Deep Learning for Medical Segmentation

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

基于改进DeeplabV3+的水面多类型漂浮物分割方法研究

【目的】为解决传统图像处理方法鲁棒性差、常用深度学习检测方法无法准确识别大片漂浮物的边界等问题,【方法】提出一种基于改进DeeplabV3+的水面多类型漂浮物识别的语义分割方法,提高水面漂浮的识别能力。对所收集实际水面漂浮物进行分类,采用自制数据集进行对比试验。算法选择xception网络作为主干网络以获得初步漂浮物特征,在加强特征提取网络部分引入注意力机制以强调有效特征信息,在后处理阶段加入全连接条件随机场模型,将单个像素点的局部信息与全局语义信息融合。【结果】对比图像分割性能指标,改进后的算法mPA(Mean Pixel Accuracy)提升了5.73%,mIOU(Mean Intersection Over Union)提升了4.37%。【结论】相比于其他算法模型,改进后的DeeplabV3+算法对漂浮物特征的获取能力更强,同时能获得丰富的细节信息以更精准地识别多类型水面漂浮物的边界与较难分类的漂浮物,在对多个水库场景测试后满足实际水域环境中漂浮物检测的需求。

基于场因子分解的xDeepFM推荐模型

极深因子分解机(eXtreme deep factorization machine,xDeepFM)是一种基于上下文感知的推荐模型,它提出了一种压缩交叉网络对特征进行阶数可控的特征交叉,并将该网络与深度神经网络进行结合以优化推荐效果。为了进一步提升xDeepFM在推荐场景下的表现,提出一种基于场因子分解的xDeepFM改进模型。该模型通过场信息增强了特征的表达能力,并建立了多个交叉压缩网络以学习高阶组合特征。最后分析了用户场、项目场设定的合理性,并在3个不同规模的MovieLens系列数据集上通过受试者工作特征曲线下面积、对数似然损失指标进行性能评估,验证了该改进模型的有效性。

Engineering behaviour of in situ cored deep cement mixed marine deposits subjected to undrained and drained shearing

The deep cement mixing(DCM)is used to improve the capacity and reduce the settlement of the soft ground by forming cemented clay columns.The investigation on the mechanical behaviour of the DCM samples is limited to either laboratory-prepared samples or in-situ samples under unconfined compression.In this study,a series of drained and undrained triaxial shearing tests was performed on the in-situ cored DCM samples with high cement content to assess their mechanical behaviours.It is found that the drainage condition affects significantly the stiffness,peak and residual strengths of the DCM samples,which is mainly due to the state of excess pore water pressure at different strain levels,i.e.being positive before the peak deviatoric stress and negative after the peak deviatoric stress,in the undrained tests.The slope of the failure envelope changes obviously with the confining pressures,being steeper at lower stress levels and flatter at higher stress levels.The strength parameters,effective cohesion and friction angle obtained from lower stress levels(c′0 andφ′0)are 400 kPa and 58°,respectively,which are deemed to be true for design in most DCM applications where the in-situ stress levels are normally at lower values of 50-200 kPa.Additionally,the computed tomography(CT)scanning system was adopted to visualize the internal structures of DCM samples.It is found that the clay pockets existing inside the DCM samples due to uneven mixing affect markedly their stress-strain behaviour,which is one of the main reasons for the high variability of the DCM samples.

Pressure control method and device innovative design for deep oil in-situ exploration and coring

Deep oil exploration coring technology cannot accurately maintain the in-situ pressure and temperature of samples, which leads to a distortion of deep oil and gas resource reserve evaluations based on conventional cores and cannot guide the development of deep oil and gas resources on Earth. The fundamental reason is the lack of temperature and pressure control in in-situ coring environments. In this paper, a pressure control method of a coring device is studied. The theory and method of deep intelligent temperature-pressure coupling control are innovatively proposed, and a multifield coupling dynamic sealing model is established. The optimal cardinality three term PID (Proportional-Integral-Differential) intelligent control algorithm of pressure system is developed. The temperature-pressure characteristic of the gas-liquid two-phase cavity is analyzed, and the pressure intelligent control is carried out based on three term PID control algorithms. An in-situ condition-preserved coring (ICP-Coring) device is developed, and an intelligent control system for the temperature and pressure of the coring device is designed and verified by experiments. The results show that the temperature-pressure coupling control system can effectively realize stable sealing under temperature-pressure fields of 140 MPa and 150 °C. The temperature-pressure coupling control method can accurately realize a constant pressure inside the coring device. The maximum working pressure is 140 MPa, and the effective pressure compensation range is 20 MPa. The numerical simulation experiment of pressure system control algorithm is carried out, and the optimal cardinality and three term coefficients are obtained. The pressure steady-state error is less than 0.01%. The method of temperature-pressure coupling control has guiding significance for coring device research, and is also the basis for temperature-pressure decoupling control in ICP-Coring.

Assessing the conservation impact of Chinese indigenous chicken populations between ex-situ and in-situ using genome-wide SNPs

Conservation programs require rigorous evaluation to ensure the preservation of genetic diversity and viability of conservation populations. In this study, we conducted a comparative analysis of two indigenous Chinese chicken breeds, Gushi and Xichuan black-bone, using whole-genome SNPs to understand their genetic diversity, track changes over time and population structure. The breeds were divided into five conservation populations(GS1, 2010, ex-situ;GS2, 2019, ex-situ;GS3, 2019, in-situ;XB1, 2010, in-situ;and XB2, 2019, in-situ) based on conservation methods and generations. The genetic diversity indices of three conservation populations of Gushi chicken showed consistent trends, with the GS3 population under in-situ strategy having the highest diversity and GS2 under ex-situ strategy having the lowest. The degree of inbreeding of GS2 was higher than that of GS1 and GS3. Conserved populations of Xichuan black-bone chicken showed no obvious changes in genetic diversity between XB1 and XB2. In terms of population structure, the GS3 population were stratified relative to GS1 and GS2. According to the conservation priority, GS3 had the highest contribution to the total gene and allelic diversity in GS breed, whereas the contribution of XB1 and XB2 were similar. We also observed that the genetic diversity of GS2 was lower than GS3, which were from the same generation but under different conservation programs(in-situ and ex-situ). While XB1 and XB2 had similar levels of genetic diversity. Overall, our findings suggested that the conservation programs performed in ex-situ could slow down the occurrence of inbreeding events, but could not entirely prevent the loss of genetic diversity when the conserved population size was small, while in-situ conservation populations with large population size could maintain a relative high level of genetic diversity.

In-situ AFM and quasi-in-situ studies for localized corrosion in Mg-9Al-1Fe-(Gd) alloys under 3.5 wt.% NaCl environment

Revealing the localized corrosion process of Mg alloy is considered as one of the most significant ways for improving its corrosion resistance.The reliable monitor should be high distinguishability and real-time in liquid environment.Herein,Mg-9Al-1Fe and Mg-9Al-1Fe-1Gd alloys were designed to highlight the impact of intermetallic on the corrosion behaviour.In-situ AFM with a special electrolyte circulation system and quasi-in-situ SEM observation were used to monitor the corrosion process of the designed alloys.SEM-EDS and TEM-SAED were applied to identify the intermetallic in the designed alloys,and their volta potentials were measured by SKPFM.According to the real-time and real-space in-situ AFM monitor,the corrosion process consisted of dissolution of anodicα-Mg phase,accumulation of corrosion products around cathodic phase and shedding of some fine cathodic phase.Then,the localized corrosion process of Mg alloy was revealed combined with the results of the monitor of corrosion process and Volta potential difference.

An in-situ study of static recrystallization in Mg using high temperature EBSD

It has been a common method to improve the mechanical properties of metals by manipulating their microstructures via static recrystallization,i.e.,through heat treatment.Therefore,the knowledge of recrystallization and grain growth is critical to the success of the technique.In the present work,by using in-situ high temperature EBSD,the mechanisms that control recrystallization and grain growth of an extruded pure Mg were studied.The experimental results revealed that the grains of priority for dynamic recrystallization exhibit fading competitiveness under static recrystallization.It is also found that grain boundary movement or grain growth is likely to show an inverse energy gradient effect,i.e.,low energy grains tend to swallow or grow into high energy grains,and grain boundaries of close to 30°exhibit superior growth advantage to others.Another finding is that{10-12}tensile twin boundaries are sites of hardly observed for recrystallization,and are finally swallowed by adjacent recrystallized grains.The above findings may give comprehensive insights of static recrystallization and grain growth of Mg,and may guide the design of advanced materials processing in microstructural engineering.

Hyperspectral image super resolution using deep internal and self-supervised learning

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By consolidating the DIL and DSL into a unified deep framework,the authors construct a more robust HSI SR method without any prior training and have great potential of flexible adaptation to different settings per obser-vation.To verify the effectiveness of the proposed approach,extensive experiments have been conducted on two benchmark HS datasets,including the CAVE and Harvard datasets,and demonstrate the great performance gain of the proposed method over the state-of-the-art methods.