Interpretation Of Maps Research Articles

Oil occurrence state and quantity in alkaline lacustrine shale using a high-frequency NMR technique

Complex fluid occurrence states and structures of shale reservoirs greatly affect shale oil productivity. For lacustrine shales with strong heterogeneity, it is important to identify oil movability, occurrence states and pore structures in different lithofacies in order to efficiently develop shale oil. This study combines conventional core analysis, X-ray diffraction (XRD), argon ion milled-scanning electron microscopy (AIM-SEM), multiple isothermal stages (MIS) pyrolysis and high-frequency nuclear magnetic resonance (HF-NMR) to create maps for samples in native, saturated, centrifuged, and dried states. A new T1-T2 map interpretation scheme is proposed, and fluid occurrence models for different shale lithofacies are established. Four oil species can be identified on the T1-T2 map. Free oil and adsorbed oil in macropores are located in the centers and edges of intergranular/intercrystalline pores, respectively. Oil in mesopores is located in intragranular/intracrystalline dissolution pores. Adsorbed oil exists in the pores of dispersed organic matter and in the marginal fractures of organic matter laminae. NMR and pyrolysis correlate well in the oil contents that they define. The occurrence state of shale oil in the Fengcheng Fm is closely related to minerals and organic matter. Siliceous minerals are beneficial to the occurrence of free oil that is usually a highly movable. The fluid-holding capacity of calcareous shale is limited, especially for adsorbed oil and restricted oil. Clay minerals (>10%) are not conducive to shale oil enrichment and flow, and mixed shale almost exclusively contains bound water. Organic matter is conducive to increasing oil content and to the movable oil saturation of siliceous shale and mixed shale.

Distribution of soil morphology and physicochemical properties to assess the evaluation of soil fertility status using soil fertility capability classification in North Galela, Indonesia

One of the indicators of land productivity assessment is soil fertility. The causes of infertile soil have been widely reported. This study aimed to evaluate soil fertility using the Soil Fertility Capability Classification (SFCC) approach and to determine the relationship between soil morphological and physiochemical properties and soil fertility. The research was conducted in North Galela Sub-district, North Halmahera Regency. The research framework began with the interpretation of thematic maps, surveys, field observations, and the making of soil fertility evaluation maps. Soil sampling was carried out based on the stratified random sampling method and supported by laboratory measurements according to USDA international standards. The research result showed that the research area had 14 soil fertility statuses. The fertility classes having the most significant limiting factor were loamy texture (Ø<0.002 mm), rock surface, gravel subsoil, moderate salinity (0.2-0.4 mS cm<sup>-1</sup>), low exchangeable K (<20 cmol<sub>(+)</sub> kg<sup>-1</sup>), and sloping slope (8%) with Lithic Haprendolls type and loamy texture (Ø<0.002 mm), subsoil >15% rock outcrop, moderate salinity (0.2-0,4 mS cm<sup>-1</sup>), Na saturation >15%, and a rather steep slope (14%) with Lithic Eutrudepts type. Both of these classes have a top layer texture, and the bottom layer contains clay <35%, with limiting factors of hard layer inhibiting root to a depth of 50 cm, extremely low nutrient reserve, low electrical conductivity (EC) and high Na. The limiting factors found in North Galela Sub-district were hard root-restricting layer, exchangeable K, electrical conductivity, Na saturation, slope, and gravel.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms

Background and objectiveBreast cancer is the world’s most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. MethodsDeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists’ needs. ResultsThe proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. ConclusionTo conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

An AR Map Virtual–Real Fusion Method Based on Element Recognition

The application of AR to explore augmented map representation has become a research hotspot due to the growing application of AR in maps and geographic information in addition to the rising demand for automated map interpretation. Taking the AR map as the research object, this paper focuses on AR map tracking and registration and the virtual–real fusion method based on element recognition. It strives to establish a new geographic information visualization interface and application model. AR technology is applied to the augmented representation of 2D planar maps. A step-by-step identification and extraction method of unmarked map elements are designed and proposed based on the analysis of the characteristics of planar map elements. This method combines the spatial and attribute characteristics of point-like elements and line-like elements, extracts the color, geometric features, and spatial distribution of map elements through computer vision methods, and completes the identification and automatic extraction of map elements. The multi-target image recognition and extraction method based on template and contour matching, and the line element recognition and extraction method based on color space and area growth are introduced in detail. Then, 3D tracking and registration is used to realize the unmarked tracking and registration of planar map element images, and the AR map virtual–real fusion algorithm is proposed. The experimental results and results of an analysis of stepwise identification and extraction of unmarked map elements and map virtual–real fusion reveal that the stepwise identification of unmarked map elements and map model virtual–real fusion studied in this paper is effective. Through the analysis of map element step-by-step recognition efficiency and recognition rate, it is proved that the element step-by-step method in this paper is fast, its recognition efficiency meets the AR real-time requirements, and its recognition accuracy is high.

Improving digital soil mapping in Bogor, Indonesia using parent material information

Digital soil mapping depends on the accurate representation of soil forming factors in the form of spatial layers called scorpan. Within the scorpan factors, digital elevation model (DEM) and its derived data are the most commonly used covariates, while parent materials and soil age received the least attention. This may be due to the coarse resolution or complex interpretation of parent material maps. This study examines the role of parent material derived from a semi-detailed soil map as a covariate for improving the accuracy of digital soil maps. The study was conducted in Bogor, West Java (2663.81 km2). Observations of 56 soil map units consisting of 19 soil subgroups of the USDA Soil Taxonomy were made on an existing 1:50,000 soil map. The study evaluated the contribution of covariates representing soil, organisms, relief and parent material in predicting soil classes using random forest models. The results show that the inclusion of parent material covariate can increase the total accuracy of predicting soil classes from 44.60 to 66.19% to 59.91–73.89%. The combination of 13 covariates representing topography, soil, organisms and parent materials achieved the highest accuracy above 70%. On the other hand, the most parsimonious set of covariates that can achieve 66.02% accuracy includes DEM, NDVI, NDWI, and parent material. The use of parent material covariate derived from detailed soil maps can improve the accuracy of digital soil mapping of soil classes in regions derived from complex volcanic materials.

Human-induced reactivation of a dormant earthflow? A geomorphological interpretation of historical mining maps of the Ischl salt mine (Austria)

This case study from the Northern Calcareous Alps demonstrates the utilization of historical mining documents for geomorphological purposes, especially for landslide event documentation. A combination of geomorphological mapping and interpretation of up to 200 year old mining maps for the Ischl salt mine (Austria) revealed the existence of a 500 m-long earthflow in an area where the engineering geology has been well investigated. The CAD-based creation of a polythematic map (surface and subsurface features) led to the conclusion that a prehistoric earthflow has been partly reactivated between 1850 and 1934 due to mining subsidence. The depletion zone of the historical earthflow is situated directly above the major collapse zone of the mine. Reactivation was most likely to have been caused by the lowering of the slope base due to the slow development of a subsidence trough within the ductile Haselgebirge Formation. Particularly for engineering geomorphological tasks in post-mining-areas, where subsurface facilities are no longer accessible, a combined approach of historical mining map analysis and geomorphological mapping can be regarded as a promising and cost-effective working concept. Supplementary material : Historical ground plans and cross-sections listed in Table 1 are available at https://doi.org/10.6084/m9.figshare.21740459 Thematic collection: This article is part of the Engineering Geology and Hydrogeology of the Anthropocene collection available at: https://www.lyellcollection.org/topic/collections/engineering-geology-and-hydrogeology-of-the-anthropocene

Collaborative mapping on sustainable development goals in Latin America UNESCO Global Geopark: A methodological discussion

UNESCO Global Geoparks (UGGps) are territories that promote sustainable development through a territorial management plan focusing on education, conservation, and local development. In their guidelines, they establish, among others, the relevance of the territorial bottom-up approach. This implies the active involvement and participation of the local communities on the projects and strategies focusing on sustainable territorial development and management. In this sense, the participatory processes should be one of the main strategies for the progress, analysis and diagnosis on the development and management of the UGGps. On this scope, the use of geographic analysis tools, like interpretative maps of the territory and the application of Geographic Information Systems (GIS) through a collaborative approach, could be an asset for the spatial planning required in the UGGps, reinforced by the local knowledge. One of the main applications that can be implemented in UGGps for the improvement, adaptation, and actualization of the spatial planning and management of the territories is the application of collaborative mapping strategies. This work is an example of a methodological proposal for the spatial diagnosis of the sustainable development in four UGGps of the Latin America and Caribbean Geoparks Network (GeoLAC) through a collaborative mapping process. This should be understood as a map's creation practice by local communities' perception. In addition, it also allows the understanding of the dynamics, evolution, and transformation of the UGGps through community knowledge and citizen science, supporting the improvement of the management plans of each territory. The objective of this methodology is to identify and discuss the spatial and territorial distribution of the “geopark” concept and how different activities related to geoconservation, geoeducation and geotourism, as main axes of the principles of the UGGp, are acting for and contributing to the sustainable development in four UGGps of the GeoLAC. Results allow to define new strategies to involve, in a more homogeneous spatial distribution, the impacts and benefits of the UGGps. The final maps become important tools not only for researchers or managers but also for local communities. This methodology has the potential to become a tool for its application in any UGGp willing to analyze the spatial distribution of the territorial strategies, the activities impact, and the benefits for the sustainable development of its territory. Furthermore, it can be an efficient evaluation tool when applied periodically in the territory, allowing to compare the development and evolution of the spatial impacts and territorial dynamics of the strategies defined by the management structure, calibrated by the vision and perception of the local communities.

Geophysical Survey and Changes in the Use of the Cultural Landscape

The results of detailed and large-scale geophysical measurements in archaeology have been steadily increasing for years. The growth in measured data has also increased the need for processing and interpretation; in archaeology, this primarily means the archaeological interpretation of the measured data. However, the information contained in geophysical data includes a substantial volume or area of data of varying size or thickness of some different natural or modern anthropogenic origin (beyond archaeological interest). Like the archaeological situations themselves, these must also be identified and demarcated. The presented article consists of a wide range of case studies in which the result of a specific applied geophysical method includes both the desired interpretations of archaeological features and the differentiation and warning of other anomalies, the origin of which may or may not be unambiguous or related to the post-deposition processes of archaeological features. The purpose of selecting several different examples of results in our paper is to point out that there are many more consequences of anthropogenic activity hidden beneath the surface of the terrain of the contemporary cultural landscape than just those that archaeologists have in their viewfinder. Other anomalies in specific environments may be of natural origin or related to various geological, pedological or hydrological changes in a site’s natural environment. This should be dealt with by the alternative differentiation of anomalies of various probable origins; the interpretive descriptions, diagrams or maps should not just focus strictly on the anticipated subsurface relics of the archaeological features and situations, as these are not there alone.

Visualization and analysis of Pareto-optimal fronts using interpretable self-organizing map (iSOM)

Visualizing and analyzing multiple Pareto-optimal solutions obtained using an evolutionary multi- or many-objective optimization algorithm is as important a task as the task of finding them. Besides helping to choose a single preferred solution, they provide a better understanding of the trade-off among the objectives and also reveal key insights about interactions among variables and objectives for the Pareto-optimal solutions. Existing visualization methods do not provide a comprehensive account of both visualization and analysis of Pareto-optimal solutions. In this paper, we present an interpretable self-organizing map (iSOM) method that produces a more simplistic mapping of higher-dimensional variable spaces into two dimensions. Multiple iSOM plots, one for each objective, allows an easier visual understanding of trade-off among objectives. By identifying high trade-off Pareto-optimal solutions and marking them on the iSOM plots, we also provide decision-makers a comprehensive method to locate critical and likely preferred solutions on the Pareto-optimal front. The visualization and analysis of Pareto-optimal solutions using iSOMs are demonstrated on 11 problems involving three to five objectives. As discussed, the approach is generically applicable to higher-dimensional and constrained problems.

Coupled Kähler-Einstein and Hermitian-Yang-Mills equations

We introduce a new system of equations coupling Kähler-Einstein and Hermitian-Yang-Mills equations. We provide a moment map interpretation of these equations. We identify a Futaki type invariant as an obstruction to the existence of solutions to these equations. We also prove a Matsushima-Lichnerowicz type theorem. We prove a deformation result that produces nontrivial solutions of these equations under some conditions. We produce examples on some projective bundles using Calabi ansatz.

PREVALENCE OF KERATOCONUS IN KASHMIR IN NORTH INDIA: A VIDEOKERATOGRAPHIC STUDY

Purpose: To detect the incidence of keratoconus by videokeratography in patients in Kashmir in North India and to describe the clinical characteristics associated with it. Design: Cross sectional study Methodology: This study was carried out on eyes of 212 patients between 5 to 20 years of age presenting to tertiary care hospital of Kashmir in north India over a period of two years. Best Corrected Visual Acuity (BCVA), slit lamp examination, pachymetry, refraction, fundus examination, and corneal topography was done in all the patients and clinical ndings recorded. To detect keratoconus, corneal topography maps were analysed using Rabinowitz-McDonnel criteria. Maps with central corneal power(Kmax) greater than 47.2 diopters and the inferosuperior asymmetry value greater than 1.4 were considered to have a keratoconus pattern. Results: Out of 212 patients, 145 were males and 67 were females. Majority were in the age group of 5- 10 years. Among the study eyes, Kmax> 47.2 D was seen in 10.1 %, Sim K astigmatism > 1.5D in 29%, I-S difference > 1.4 D in 11.79 % , SRAX > 21° in 20% and K max>47.2 D + I-S Difference >1.4 i.e keratoconus pattern was seen in 93 eyes i.e 21.9% Conclusion: Incidence of keratoconus in allergic patients is more when compared to general population of the same age group from previous studies. The videokeratographic examination of patients leads to early detection of mild keratoconus by interpretation of color-coded maps.

Liver segmentation using Turbolift learning for CT and cone-beam C-arm perfusion imaging

Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST – making the previous trainings act as pre-training stages for the subsequent ones – addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874±0.031 and 0.905±0.007 in 6-fold and 4-fold cross-validation experiments, respectively — securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

The purpose of this research is to delineate the structures of the Lower Goru Formation, investigate fluid properties, and clarify the hydrocarbon-prone areas through seismic attributes analysis. First, the acquired data was matched by the interpretation datum. Structural analysis was done by performing horizon interpretation, fault interpretation, and contour mapping on the C-Interval of the Lower Goru Formation. Hydrocarbon zones were marked with the help of attribute analysis on seismic sections and were justified by petrophysical analysis. An integrated approach such as seismic structural interpretation, seismic attribute, spectral decomposition, and petrophysical analyses was used in current research to better understand geological structure and features. This research showed that normal faults are present in the area showing negative flower structure, horst and graben, and faults oriented north-west to south-east. The contour map shows structural inclination and faults bound closure near well locations. Variance attribute and spectral decomposition attribute were used to verify horizon lineation and fault behavior. Instantaneous amplitude and instantaneous phase attributes justify hydrocarbon bearing zones, and bright spots are present on seismic sections at C–Interval of Lower Goru Formation. Petrophysical analysis of the available wells showed a number of significant hydrocarbon zones having more than 55% of hydrocarbon saturation at the C-Interval of the Lower Goru Formation. The four possible reservoir zones in Sawan-02 well, two zones in Sawan-07 well, and three zones in Sawan-09 well are identified based on well data interpretation. Based on these analyses, the area of interest has a very good reservoir potential, structural closure, and visible bright spots. The current finding of this research will be helpful for future exploration and development of the Sawan area.

Mapping Interpretation by the International Criminal Court

Mapping Interpretation by the International Criminal Court

RFI Localization Using Jointly Non-Convex Low-Rank Approximation and Expanded Virtual Array in Microwave Interferometric Radiometry

The scientific goal of the Soil Moisture and Ocean Salinity (SMOS) mission is to retrieve the geophysical parameter from brightness temperature (TB) maps. However, radio frequency interference (RFI) significantly influences the interpretation of TB maps, leading to a deteriorated retrieval performance. RFI localization is essential for switching off these illegal emitters and mitigating their impacts on TB maps. This article proposes a novel high-resolution RFI localization method via jointly non-convex low-rank approximation and expanded virtual array (EVA). Concretely, the RFI localization problem is first formulated from the perspective of non-convex low-rank recovery, which better approximates the rank of the covariance matrix collecting visibility samples. Then, we propose the EVA concept by relaxing the size constraint on the physical antenna array. Moreover, we employ a new algorithm based on the joint Schatten-p and Lp (JSL) norms to solve the above non-convex low-rank recovery problem. This JSL algorithm can improve the spatial resolution for RFI localization. Combining the JSL algorithm and the EVA can further improve detection performance and enhance the spatial resolution for RFI localization. The experimental results using synthetic data and real SMOS data prove that the proposed method shows enhanced spatial resolution, better detection performance, and competitive or better localization accuracy compared with the currently existing methods.

Early Pliocene (Zanclean) stratigraphic framework for PRISM5/PlioMIP3 time slices

Global reconstructions of Pliocene climate provide important insights into how the climate system operates under elevated temperatures and atmospheric CO2 levels. These reconstructions have been used extensively in paleoclimate modeling experiments for comparison to simulated conditions, and as boundary conditions. Most previous work focused on the Late Pliocene interval known as the mid Piacenzian Warm Period (mPWP), the interval originally identified by the U.S. Geological Survey Pliocene Research, Interpretation and Synoptic Mapping Project (PRISM) as the PRISM interval or Mid Pliocene Warm Period. The term Mid Pliocene Warm Period is a misnomer due to changes to the geological time scale, and its use should be discontinued. The Pliocene Model Intercomparison Project (PlioMIP), now in its third phase, is expanding to include a focus on the Early Pliocene (Zanclean). PlioMIP3 experiments will allow comparison of environmental and climatic conditions before and after closure of the Central American Seaway (CAS). PlioMIP3 used the annual insolation pattern at the top of the atmosphere to determine time slices in the Zanclean that have orbital configurations that are most similar to modern. Two have been selected by PlioMIP and adopted by PRISM for inclusion in future studies: PRISM5.1 (4.474 Ma) and PRISM5.2 (4.870 Ma). Here we establish the stratigraphic framework for these Early Pliocene time slices and furnish information to help locate these intervals in proxy records of paleoenvironmental data using oxygen isotope stratigraphy, paleomagnetic stratigraphy, biostratigraphy, and biochronology (calibrated planktic foraminifer and calcareous nannofossil events).

A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images.

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding's inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at https://github.com/Baron-Huang/ViT-AMCNet.

Rural Capitalization and Agrarian Transformation in the Ciwidey Highlands, West Java, Indonesia

The objective of this study was to examine the relationship between capitalization and rural transformation. By conducting a study in the Ciwidey Highlands of West Java, this study focused on the development of tourism as a path for capital flow that is directly connected with the existence of agricultural land and the livelihoods of the population as farmers. These two aspects are key elements in reviewing rural transformation. The data for this study were obtained through field observation at fourteen locations of agricultural land and a questionnaire survey distributed among ninety tourism workers. Through map interpretation and descriptive analysis, the results of this study show the impact of capitalization through tourism development in the increasing economic value of land, which results in the desire of farmers to sell their agricultural land. This impact was also seen in the shift in livelihoods. Rural capitalization through tourism investment has a significant impact on livelihoods, land tenure, and land use.

Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention

Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the clinical diagnosis, mainly because of the commonly used low-magnification pathological images lack fine cellular structure information and accurate localization, the diagnosis results of pathologists are different from those of attentional convolutional network -based methods, and the gradient-weighted class activation mapping method cannot be optimized to create the best visualization map. To address this problem, we propose an end-to-end depth domain adaptive network (DDANet) with integration gradient CAM and priori experience-guided attention to improve the tumor grading performance and interpretability by introducing the pathologist's a priori experience in high-magnification into the depth model. Specifically, a novel priori experience-guided attention (PE-GA) method is developed to solve the traditional unsupervised attention optimization problem. Besides, a novel integration gradient CAM is proposed to mitigate overfitting, information redundancies and low sparsity of the Grad-CAM graphs generated by the PE-GA method. Furthermore, we establish a set of quantitative evaluation metric systems for model visual interpretation. Extensive experimental results show that compared with the state-of-the-art methods, the average grading accuracy is increased to 88.43% (↑4.04%), the effective interpretable rate is increased to 52.73% (↑11.45%). Additionally, it effectively reduces the difference between CV-based method and pathology in diagnosis results. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and our model outperforms pathologists with different levels of experience.

Structure Analysis of Sa’adi Reservoir in X Supergiant Oilfield, Southern Iraq

The study area is located in southern Iraq in Mesopotamia hydrocarbon province. It’s part of supergiant liner anticline that extends from south to north, South Rumaila, North Rumaila, West Qurna-1, and West Qurna-2. The major fold oriented north-northwest and extends approximately 120Km. All available data was integrated and used in this study to support a better image of the subsurface of the Sa'adi structure. The Sa'adi reservoir in the study area is considered as a secondary reservoir. Therefore, a three-dimensional seismic survey, and seven VSPs, in addition, to well tops that drive from a set of well logs are used in this study. The three-dimensional seismic interpretation and subsurface structure maps of both Tops of Sa'adi and Tanuma formations were performed by Halliburton software (OpenWorks, DecisionSpace Geoscience, and Seismic Analysis). The structure maps of Lower Sa'adi_I and Lower Sa'adi_II have been created based on well tops. A set of well logs (Quad combo) were used to identify the well tops in more than four hundred wells that penetrated the Sa'adi reservoir and distributed over the study area using Geolog software. The structure maps of Lower Sa'adi_I and Lower Sa'adi_II in addition to the cross-sections were set up by Petrel 2018 (Schlumberger software). The study gives highly accurate structure maps of top of Upper Sa'adi, top of Lower Sa'adi_I, top of Lower Sa'adi_II, and top of Tanuma formations. The Sa'adi structure in study area is classified as asymmetrical, non-cylindrical, horizontal, antiform, brachy, gentle anticline.