High-resolution Image Data Research Articles

A study of high-precision stereo positioning with multi-scene and multi-mode GF-3 satellite images

ABSTRACT Due to factors that include systematic sensor delay, it is difficult for synthetic aperture radar (SAR) satellite stereo positioning to meet the requirements of large-scale mapping. In traditional stereo positioning methods, ground control points (GCP) are required to eliminate errors. In this study, a method is proposed to improve the stereo positioning accuracy without GCP. First, the factors influencing the Gaofen-3 (GF-3) satellite stereo positioning accuracy were analysed. The errors were divided into systematic fixed errors, spatial and temporal variation errors, and random errors. Subsequently, a systematic geometric error compensation model and a stereo adjustment model were constructed, and a method to improve the multiscene and multimode stereo positioning accuracy without GCP was proposed. Finally, the effects of different intersection angles on the accuracy of stereo positioning were compared and analysed, and the optimal intersection angle of the SAR satellite was quantitatively analysed. The effectiveness and accuracy of this method were verified using GF-3 high-resolution image data. The experimental results indicate that the proposed method can improve the SAR satellite stereo positioning accuracy. The plane positioning accuracy improved by 95.1%, and the elevation positioning accuracy improved by 97.9%. In general, the plane and elevation positioning accuracies of the GF-3 satellite were better than 3 m and 2 m, respectively. The stereo-positioning accuracy of the SAR satellite was the best and most stable when the intersection angle was between 40° and 50°.

An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images

Object detection in aerial images poses significant challenges due to the high dimensions of the images, requiring efficient handling and resizing to fit object detection models. The image-slicing approach for object detection in aerial images can increase detection accuracy by eliminating pixel loss in high-resolution image data. However, determining the proper dimensions to slice is essential for the integrity of the objects and their learning by the model. This study presents an evaluation of the image-slicing approach for alternative sizes of images to optimize efficiency. For this purpose, a dataset of high-resolution images collected with Unmanned Aerial Vehicles (UAV) has been used. The experiments evaluated using alternative YOLO architectures like YOLOv7, YOLOv8, and YOLOv9 show that the image dimensions significantly change the performance results. According to the experiments, the best mAP@05 accuracy was obtained by slicing 1280×1280 for YOLOv7 producing 88.2. Results show that edge-related objects are better preserved as the overlap and slicing sizes increase, resulting in improved model performance.

A spatial human thymus cell atlas mapped to a continuous tissue axis

T cells develop from circulating precursor cells, which enter the thymus and migrate through specialized subcompartments that support their maturation and selection1. In humans, this process starts in early fetal development and is highly active until thymic involution in adolescence. To map the microanatomical underpinnings of this process in pre- and early postnatal stages, we established a quantitative morphological framework for the thymus—the Cortico-Medullary Axis—and used it to perform a spatially resolved analysis. Here, by applying this framework to a curated multimodal single-cell atlas, spatial transcriptomics and high-resolution multiplex imaging data, we demonstrate establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions by the beginning of the the second trimester of fetal development. We pinpoint tissue niches of thymic epithelial cell progenitors and distinct subtypes associated with Hassall’s corpuscles and identify divergence in the timing of medullary entry between CD4 and CD8 T cell lineages. These findings provide a basis for a detailed understanding of T lymphocyte development and are complemented with a holistic toolkit for cross-platform imaging data analysis, annotation and OrganAxis construction (TissueTag), which can be applied to any tissue.

Accurately Segmenting/Mapping Tobacco Seedlings Using UAV RGB Images Collected from Different Geomorphic Zones and Different Semantic Segmentation Models.

The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas.

Research on ecological and climate impacts of offshore wind farms based on remote sensing images

Abstract. Offshore wind farm (OWF)’s impact on marine ecological environment has attracted widespread attention since its rapid development. The paper uses high-resolution satellite image data to extract the distribution of offshore wind farms (OWFs) in China based on deep learning method. Chlorophyll-a (CHL-a) concentration and sea surface temperature (SST) were used as indicators to analyze the impact of OWFs’ development. We can then draw the following conclusions: 1) From 2016 to 2022, the number of OWFs in China continues to increase, the growth rate reachs 28% in 2022. 2) The construction of OWFs presents a development trend from near sea to far sea and from shallow sea to deep sea. From 2016 to 2022, the new OWFs are mainly concentrated in sea areas with 5-50 meters water depth, accounting for 84.7 % of the total increase in OWFs. 3) The distribution density of OWFs is negatively correlated with CHL-a concentration and SST, the more OWFs, the greater the impact on CHL-a and SST. In one word, our work analyzed the development trend of OWFs in China and evaluated the impact of OWFs’ expansion on marine ecological environment and climate.

A novel adjunctive diagnostic method for bone cancer: Osteosarcoma cell segmentation based on Twin Swin Transformer with multi-scale feature fusion

BackgroundOsteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma. MethodsIn this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion. ResultsThe experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings. ConclusionThe development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

Improved stomatognathic model for highly realistic finite element analysis of temporomandibular joint biomechanics

BackgroundMechanical response analysis of the temporomandibular joint (TMJ) is crucial for understanding the occurrence and development of diseases. However, the realistic modeling of the TMJ remains challenging because of its complex composition and multivariate associations. ObjectiveThis study aims to develop a highly realistic stomatognathic model that accurately represents the geometric accuracy, structural integrity, and material properties. And further optimizes the interference and establishes the application range of the simplifications and the assumptions. MethodsGeometric reconstruction of the bone was based on high-resolution image data, with the accuracy of the occlusal surface ensured by plaster cast model registration. Soft tissues such as cartilage, the disc, the periodontal ligament (PDL), and disc attachments often need to be approximated or assumed. Therefore, the finite element methods (FEM) was used to optimize these assumptions, including 1) the biomechanical effects of the thickness and modulus of the PDL, 2) the approximation of the geometry and material behavior of the disc, and 3) the simplification of the loading and boundary conditions. Results1) The deformation of the PDL causes tooth movement, which spreads to the distal condyle and further effects the TMJ load situation, 2) Disc reconstructed by MRI and hyperelastic material behavior are necessary for accurate TMJ loading analyses, 3) The loss of relative sliding movement between teeth interferes with realistic TMJ loading. ConclusionThe improved stomatognathic model delivers highly realistic and validated simulation, offering theoretical guidance for virtual treatments and TMJ multivariate overload studies.

Time series cube data construction and land use attribute change study based on high-resolution imagery

Abstract In order to better understand the changes in land use attributes, comprehend the drivers of land use changes, assess their environmental and economic impacts, and provide a scientific basis and data support for land use planning, environmental protection, and agricultural production, this study is based on Qiu County’s high-resolution image data from 2020-2022 and makes use of the latest research results of hyperspectral remote sensing technology to analyze its changes in land use attributes by establishing the time of the high-resolution image sequence cube data and solving the characteristic variables through band operations to quickly and accurately calculate a variety of remote sensing indicators and analyze the changes in their land use attributes. The results of the study show that vegetation in Qiu County will change significantly between 2020 and 2022, mainly due to seasonal and land-use changes; Water bodies will change less but more consistently; And buildings will change less, mainly focusing on villages and industrial land.

Computational modeling of predicting cerebrovascular injury in traumatic brain injury patients

Despite significant progress in understanding traumatic brain injury (TBI) and subsequent outcomes, predicting and preventing cerebrovascular injury (CVI) remains challenging. We introduces a novel computational modeling approach using advanced fluid-structure interaction (FSI) models, which incorporate high-resolution magnetic resonance imaging (MRI) data to analyze changes in intracranial pressure and blood flow in cerebral arteries post-TBI. We analyzed the mechanical fields between the PCOMA, ICA, PCA, and ACA areas by conducting a region of interest analysis in this research. The approach uniquely integrates detailed patient-specific brain vasculature geometries and dynamic boundary conditions to enhance predictive accuracy. The model indicates that peak pressure in the anterior cerebral artery (ACA) reaches 130 kPa within the first hour post-injury, with a 20% increase observed at 2 milliseconds. Wall shear stress (WSS) in the posterior communicating artery (PCOMA) reaches 140 kPa, which exceeds the typical physiological range of 10–20 kPa and may lead to endothelial damage. Regions such as the PCOMA and ACA are identified as particularly vulnerable to high mechanical stress and strain, which suggests the need for timely medical intervention to reduce the risk of CVI. Further validation with clinical data is required to improve the predictive accuracy of the model.

Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations.

Advancements and Challenges in Algebraic Techniques for Medical Imaging: A Comprehensive Review of MRI and CT Scan Applications in Nigeria

Medical imaging plays a crucial role in modern healthcare by providing essential diagnostic and treatment information. This review examines the advancements and challenges of algebraic techniques in enhancing Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans in Nigeria. Algebraic methods, including Algebraic Reconstruction Techniques (ART), Compressed Sensing (CS), and Total Variation (TV) regularization, are integral in refining image reconstruction processes, reducing noise, and improving diagnostic accuracy. These techniques facilitate high-resolution imaging, artifact correction, and effective data fusion across different modalities. Despite the promising advancements, Nigeria faces several challenges, including high implementation costs, limited access to advanced imaging equipment, and a shortage of trained professionals. The economic barriers and disparities in technology access underscore the need for strategic investments, policy support, and collaborative initiatives to broaden the reach of these techniques. Successful case studies from institutions such as Lagos University Teaching Hospital and University College Hospital, Ibadan, highlight the tangible benefits of algebraic imaging techniques, including enhanced diagnostic clarity and reduced scan times. Training and education are essential for leveraging these advanced techniques, necessitating interdisciplinary programs and international collaborations. The integration of algebraic methods holds transformative potential for Nigeria's healthcare system, improving patient outcomes through early disease detection, precise treatment planning, and expanded access to advanced imaging services. Addressing the associated challenges through targeted efforts and continued innovation will further enhance diagnostic practices and healthcare delivery in Nigeria. Keywords: Algebraic Techniques, Medical Imaging, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Nigeria.

Sound reception and hearing capabilities in the Little Penguin (Eudyptula minor): first predicted in-air and underwater audiograms.

Despite increasing concern about the effects of anthropogenic noise on marine fauna, relevant research is limited, particularly in those inaccessible species, such as the Little Penguin (Eudyptula minor). In this study, we collected freshly deceased Little Penguins for dissection and micro-computed tomography (microCT) scans. The head structures, including the ear apparatus, were reconstructed based on high-resolution imaging data for the species. Moreover, three-dimensional finite-element models were built based on microCT data to simulate the sound reception processes and ear responses to the incident planar waves at the selected frequencies. The received sound pressure fields and motion (i.e. displacement and velocity) of the internal ear-related structures were modelled. The synergistic response of ear components to incident aerial and underwater sounds was computed to predict the hearing capabilities of the Little Penguins across a broad frequency range (100 Hz-10 kHz), both in air and under water. Our predicted data showed good agreement with other diving birds in both the form and range of auditory sensitivity. This study demonstrates a promising method to study hearing in other inaccessible animals. The outputs from this study can inform noise impact mitigation and conservation management.

Neuroprotective Effects of Inhaled Xenon Gas on Brain Structural Gray Matter Changes After Out-of-Hospital Cardiac Arrest Evaluated by Morphometric Analysis: A Substudy of the Randomized Xe-Hypotheca Trial.

We have earlier reported that inhaled xenon combined with hypothermia attenuates brain white matter injury in comatose survivors of out-of-hospital cardiac arrest (OHCA). A predefined secondary objective was to assess the effect of inhaled xenon on the structural changes in gray matter in comatose survivors after OHCA. Patients were randomly assigned to receive either inhaled xenon combined with target temperature management (33°C) for 24h (n = 55, xenon group) or target temperature management alone (n = 55, control group). A change of brain gray matter volume was assessed with a voxel-based morphometry evaluation of high-resolution structural brain magnetic resonance imaging (MRI) data with Statistical Parametric Mapping. Patients were scheduled to undergo the first MRI between 36 and 52h and a second MRI 10days after OHCA. Of the 110 randomly assigned patients in the Xe-Hypotheca trial, 66 patients completed both MRI scans. After all imaging-based exclusions, 21 patients in the control group and 24 patients in the xenon group had both scan 1 and scan 2 available for analyses with scans that fulfilled the quality criteria. Compared with the xenon group, the control group had a significant decrease in brain gray matter volume in several clusters in the second scan compared with the first. In a between-group analysis, significant reductions were found in the right amygdala/entorhinal cortex (p = 0.025), left amygdala (p = 0.043), left middle temporal gyrus (p = 0.042), left inferior temporal gyrus (p = 0.008), left parahippocampal gyrus (p = 0.042), left temporal pole (p = 0.042), and left cerebellar cortex (p = 0.005). In the remaining gray matter areas, there were no significant changes between the groups. In comatose survivors of OHCA, inhaled xenon combined with targeted temperature management preserved gray matter better than hypothermia alone. ClinicalTrials.gov: NCT00879892.

Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence.

Coronary artery calcification (CAC) is a marker of atherosclerosis and is thought to be associated with worse clinical outcomes. However, evidence from large-scale high-resolution imaging data is lacking. We proposed a novel deep learning method that can automatically identify and quantify CAC in massive intravascular OCT data trained using efficiently generated sparse labels. 1,106,291 OCT images from 1,048 patients were collected and utilized to train and evaluate the method. The Dice similarity coefficient for CAC segmentation and the accuracy for CAC classification are 0.693 and 0.932, respectively, close to human-level performance. Applying the method to 1259 ST-segment elevated myocardial infarction patients imaged with OCT, we found that patients with a greater extent and more severe calcification in the culprit vessels were significantly more likely to have major adverse cardiovascular and cerebrovascular events (MACCE) (p < 0.05), while the CAC in non-culprit vessels did not differ significantly between MACCE and non-MACCE groups.

An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

Microscope-Based Augmented Reality: A New Approach in Intraoperative 3D Visualization in Microvascular Decompression?

Neurovascular compression (NVC) syndromes such as trigeminal neuralgia (TN) are causally treated with microvascular decompression (MVD). Semiautomatic segmentation of high-resolution magnetic resonance imaging (MRI) data and constructive interference in steady state (CISS)/time-of-flight (TOF) sequences are utilized for the three-dimensional (3D) visualization of underlying causative vessels at the root entry zones of the relevant cranial nerves. Augmented reality (AR) of neurovascular structures was introduced especially in the resection of brain tumors or aneurysmatic operations. In this report, the potential feasibility of the implementation of microscope-based AR into the intraoperative microsurgical set-up of MVD was investigated. This article recommends the preoperative evaluation of 3D visualization besides the microscopical view of the surgeon. The implementation of multiple imaging data by AR into the operating microscope may afflict the experienced surgeon's view, which should be examined prospectively.

RegFSC-Net: Medical Image Registration via Fourier Transform With Spatial Reorganization and Channel Refinement Network.

Medical image registration is crucial in medical image analysis applications. Recently, U-Net-style networks have been commonly used for unsupervised image registration, predicting dense displacement fields in full-resolution space. However, this process is resource-intensive and time-consuming for high-resolution volumetric image data. To address this challenge, this paper proposes a novel model named RegFSC-Net, which utilizes Fourier transform with spatial reorganization (SR) and channel refinement (CR) network for registration. We embed efficient feature extraction modules SR and CR modules into the encoder, and adopt a parameter-free model to drive the decoder to improve the U-shaped network. Precisely, RegFSC-Net does not directly predict the full-resolution displacement field in space but learns the low-dimensional representation of the displacement field in the bandlimited Fourier domain, which is beneficial in reducing network parameters, memory usage, and computational costs. Experimental results show that RegFSC-Net outperforms various state-of-the-art methods. Specifically, in comparison to the widely recognized Transformer-based method TransMorph, RegFSC-Net utilizes only around 8.2% of its parameters, resulting in a 1.95% higher Dice score and significantly faster inference speeds of 126.67% and 419.99% on GPU and CPU, respectively. Furthermore, we also designed three variants of RegFSC-Net and demonstrated their potential applications in computer-aided diagnosis.

Propagating image-based rock classes from cored to non-cored depth intervals using supervised machine learning

High-resolution image data is instrumental in quantifying the variation of rock fabric in formation evaluation that conventional well logs fail to capture. However, the acquisition of image data for all wells in a reservoir is restricted due to technology limitations and the high cost involved. The main objective of this paper is to propose a workflow to extrapolate rock fabric information from imaged depth intervals/wells to nearby un-imaged depth intervals/wells for enhanced formation evaluation in un-imaged wells. To propagate rock fabric information, we trained a supervised learning algorithm in a well with core photos, CT-scan images, and conventional well logs. Subsequently, the trained model is used to identify fabric-influenced well-log-based rock classes using only conventional well logs in un-imaged depth intervals/well (referred to as fabric-based rock classes). We applied the proposed workflow to two wells in a siliciclastic formation with spatial variation in rock fabric. Comparison of the detected fabric-based rock classes in the nearby depth intervals/well using the trained model with image-based rock classes resulted in an average accuracy of 94%. The outcomes of this paper contribute to accelerated identification of rock types honouring rock fabric while minimizing extensive imaging and coring efforts.Thematic collection:This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at:https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a +newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100–200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.