Self-organizing Map Clustering Research Articles

Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.

Joint effects of air pollution and neighborhood socioeconomic status on cognitive decline - Mediation by depression, high cholesterol levels, and high blood pressure

Air pollution and neighborhood socioeconomic status (N-SES) are associated with adverse cardiovascular health and neuropsychiatric functioning in older adults. This study examines the degree to which the joint effects of air pollution and N-SES on the cognitive decline are mediated by high cholesterol levels, high blood pressure (HBP), and depression. In the Emory Healthy Aging Study, 14,390 participants aged 50+ years from Metro Atlanta, GA, were assessed for subjective cognitive decline using the cognitive function instrument (CFI). Information on the prior diagnosis of high cholesterol, HBP, and depression was collected through the Health History Questionnaire. Participants' census tracts were assigned 3-year average concentrations of 12 air pollutants and 16 N-SES characteristics. We used the unsupervised clustering algorithm Self-Organizing Maps (SOM) to create 6 exposure clusters based on the joint distribution of air pollution and N-SES in each census tract. Linear regression analysis was used to estimate the effects of the SOM cluster indicator on CFI, adjusting for age, race/ethnicity, education, and neighborhood residential stability. The proportion of the association mediated by high cholesterol levels, HBP, and depression was calculated by comparing the total and direct effects of SOM clusters on CFI. Depression mediated up to 87 % of the association between SOM clusters and CFI. For example, participants living in the high N-SES and high air pollution cluster had CFI scores 0.05 (95 %-CI:0.01,0.09) points higher on average compared to those from the high N-SES and low air pollution cluster; after adjusting for depression, this association was attenuated to 0.01 (95 %-CI:-0.04,0.05). HBP mediated up to 8 % of the association between SOM clusters and CFI and high cholesterol up to 5 %. Air pollution and N-SES associated cognitive decline was partially mediated by depression. Only a small portion (<10 %) of the association was mediated by HBP and high cholesterol.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl−-Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3− poses a higher PN-CHR risk to human health than F− and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3− emissions in the Indo-Bangla Sundarbans region.

OECD Ülkelerinin Sürdürülebilir Kalkınma Değişkenlerine Göre Kendi Kendine Öğrenen Haritalar Yaklaşımı ile Kümelenmesi

Sustainable Development concept (SD) aims to better life for future generations. However, the COVID-19 pandemic has caused tremendous effects on people’s life in several areas. Therefore, the study aimed to investigate the impact of COVID-19 on the selected part of SD indicators in the OECD countries using Self-Organizing Map (SOM). SOM is a kind of artificial neural network (ANN) method, which is an effective clustering method to find hinder non-linear relationships between indicators. The data contained 38 OECD member countries for 11 variables for each country, covering three years (2019-2021). Firstly, descriptive statistics and Spearman rank correlation analysis were used for bivariate analysis. The coefficient of variation was also used to measure the convergence of indicators. Then, it was a two-stage clustering method using SOM and hierarchical clustering methods—the optimal cluster found according to the Silhouette Index and Davies–Bouldin Index, and as three. The convergence of gross domestic product increased gradually to 40.33% in 2019, 42.01% in 2020, and 43.69% in 2021, meaning increasing relative variability of OECD countries. While the mean of the life span was decreased, the share of health expenditure, health expenditure per capita, out-of-pocket health expenditure, and government health expenditure were increased in the study period. According to clustering analysis, the countries had similar characteristics within three years, except Colombia. Also, the USA distinguished very different characteristics from other OECD countries. Although the mean of study indicators varies due to the effect of the pandemic, the change within each OECD country showed mostly similar characteristics within three years.

A rapid and non-destructive identification for paper cup evidence based on shifted-excitation Raman difference spectroscopy and SOM clustering.

In order to achieve rapid, non-destructive, efficient, and accurate classification of paper cup samples, we propose a classification model that integrates shifted-excitation Raman difference spectroscopy (SERDS) with self-organizing map (SOM) and Bayesian optimization-support vector machine (BO-SVM). We collected differential Raman data from 52 paper cup samples using SERDS, with an excitation wavelength range of 784-785 nm, a laser power of 440 mW, an integration time of 10 s, and a spectral range spanning from 280 to 2700 cm-1 . Subsequently, principal component analysis (PCA) was applied to reduce the dimensionality of the data. The SOM clustering outcomes were utilized as the foundation for constructing the discriminant analysis (FDA) and BO-SVM classification models. The primary constituent of the paper cup samples was identified as cellulose, while additional fillers such as talc, calcium carbonate, and kaolin were also present. The SOM clustering categorized the samples into seven distinct groups. The FDA model achieved a classification accuracy of 92.3%, and the BO-SVM model reached a classification accuracy of 96.2%. The SOM clustering effectively discerned samples with different fillers, as evidenced by distinct peak numbers and shapes in the differential Raman spectra, thereby underscoring the practical significance of SOM clustering. In comparison with FDA, BO-SVM exhibited enhanced classification accuracy and exceptional performance in handling outliers and linearly inseparable data, indicating its superior generalization capabilities.

Relationship between Summer Synoptic Circulation Patterns and Extreme Precipitation in Northern China

Synoptic circulation patterns over the midlatitudes play a pivotal role in regional precipitation changes; however, the synoptic circulation patterns over eastern Asia (35°–60° N, 105°–145° E) and their effects on extreme precipitation events in the North China Plain (NCP) and northeastern China (NEC) remain unclear. The summer daily 500 hPa geopotential height anomaly fields for 1979–2021 are classified into six synoptic circulation patterns using self-organizing map (SOM) cluster analysis. The SOM1 pattern, characterized by a high-pressure ridge over the north of eastern Asia and a trough near the Korean Peninsula, yields decreased precipitation in NEC. The SOM2 pattern reveals a robust high ridge over eastern Asia, resulting in a higher incidence of regional extreme precipitation events (REPEs) of approximately 24% in the NCP. Under the SOM3 pattern, the anomalous cyclonic circulation over eastern Asia leads to above-average precipitation in the NCP. The SOM4 pattern yields the highest incidence of REPEs in NEC, with the lowest incidence of REPEs in the NCP, as the anomalous cyclonic circulation over eastern Asia moves southeastward compared to the SOM3 pattern. The SOM5 pattern presenting an anticyclone–cyclone dipole reduces precipitation in the NCP and NEC, and the anticyclonic circulation near eastern China associated with the SOM6 pattern causes above-average precipitation in the NCP. On interannual time scales, the SOM2 pattern occurrence with an increasing trend tends to induce an increasing summer precipitation trend in the NCP. The SOM3 pattern occurrence is negatively correlated with the summer precipitation in NEC. Overall, classifying the synoptic circulation patterns helps to improve precipitation forecasting and provides insights into the synoptic circulation patterns dominating the occurrences of REPEs.

Revealing the Environmental Characteristics of Towns in the Middle Himalayas Using a Geographic Information System and Self-Organizing Map

Through long-term interactions with the natural environment, the ethnic groups in the mid-Himalayas have formed unique urban environmental characteristics. Effectively identifying urban environmental characteristics is a prerequisite for implementing sustainable urban management strategies. This study took 194 towns in the mid-Himalayan as the research objects. GIS was used to statistically analyze the terrain, climate, soil, and other environmental characteristics of the towns. The SOM (Self-organizing map) method was used to classify the comprehensive environmental characteristics of the towns. The results show that the main urban environmental characteristics in this area are low-altitude towns account for a large proportion, gentle-slope towns account for a small proportion, rainfall is mainly 125–265 mm, vegetation cover is dominated by high-coverage towns, mainly distributed in central and southern parts, the soil is dominated by embryonic soil and alluvial soil. The SOM method overcomes the subjectivity and low degree of automation in traditional research on urban environmental characteristics using threshold indicator methods or feature interpretation methods. Based on environmental characteristics, the towns were divided into six categories, and the classification results showed a distinct north–south zonal distribution pattern. There were significant differences in the environmental characteristics of towns in different clusters, such as the towns in cluster 5 had high altitudes, low rainfall, and low vegetation coverage, while the towns in cluster 2 had low altitudes, high rainfall, and high vegetation coverage. Finally, based on the SOM clustering results, governance strategies were proposed for towns in different clusters to cope with climate and environmental changes and promote sustainable development in the mid-Himalayan.

A novel evaluation method of renewable energy development based on improved rough set theory and grey cloud model

Renewable energy is an effective solution to alleviate the energy crisis and achieve sustainable development, and is gradually growing into an important energy source to support economic and social development. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. Given this background, this paper proposes a novel evaluation method for renewable energy technical plans based on improved rough set theory and grey cloud model. Firstly, based on the normalized evaluation data, self-organizing map (SOM) clustering algorithm and system clustering analysis are adopted to discretize the evaluation attributes indexes and decision attributes indexes, respectively. Next, the improved rough set conditional information entropy method is used to calculate the index weights. Finally, the grey cloud model is employed to determine the grade classification of each evaluated plan. At the same time, each grade is quantified by assigning a corresponding score, and the comprehensive performances of renewable energy plans can be calculated, and then the ranking of different plans is achieved. Based on the actual renewable energy development data from a province in China, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than some evaluation methods for renewable energy technical plans.

Lower tropospheric states revealed in high vertical-resolution radiosonde data in Korea and synoptic patterns for instability based on a self-organizing map

Seasonal and spatial characterizations of wind and stability in the lower troposphere below 1500 m are investigated using 1 s high-resolution operational radiosonde data observed at five stations (Baengnyeongdo, Heuksando, National Typhoon Center (NTC), Bukgangneung, and Pohang) in South Korea for 4 years (July 2016–June 2020), concentrating on four variables: horizontal wind speed (HWS), vertical wind shear (VWS), squared Brunt–Väisälä frequency (N2), and Richardson number (Ri). Significant seasonal variations are identified in these variables due to the heterogeneity of the geographical locations as well as seasonal variations in the synoptic patterns. VWS is high in spring and summer at two island stations on the west of the Korean Peninsula, Baengnyeongdo, and Heuksando, whereas it is high in winter at NTC, Bukgangneung, and Pohang, leading to frequent Kelvin–Helmholtz (KH) instability (KHI; 0 ≤ Ri < 1/4). Below z = 1000 m, static stability is lower in winter than in summer at all stations, resulting in a higher convective instability (Ri < 0) in winter, which is attributed to the inflow of the marine boundary layer. The frequent convective instability observed in spring at Bukgangneung suggests a potential relationship with mountain wave breaking and downslope windstorms. The microstructures of the lower tropospheric instabilities revealed in the current radiosonde observations are not properly represented in the high-resolution local numerical weather prediction and analysis system, namely local Data Assimilation and Prediction System (LDAPS) of Korea Meteorological Administration. In particular, the observed convective and KH instabilities, which are approximately 45% for the whole period, are rarely (approximately 15%) represented in LDAPS. The synoptic patterns favorable for the convective and KH instabilities cases are identified based on a self-organizing map clustering algorithm: (1) west-high and east-low patterns with extended Siberian high in winter, (2) strong low-pressure systems susceptible to KHI from spring to fall, (3) weak high-pressure systems with potential convective instability, accompanying northerly or north-westerly flows with cold advection at 925 hPa.

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015–2022 ozone increase in Guangzhou

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015–2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015–2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015–2022 ozone increase in Guangzhou.

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. After inspecting the current work on clustering Arabic text, it is observed that most researchers focus on applying K-Means clustering while hindering other clustering techniques. Our evaluation shows that K-Means has a weakness of inconsistent clustering results and weak clustering performance when the data dimensionality increases. Unlike K-Means clustering, Artificial Neural Networks (ANN) models such as Self-Organizing Maps (SOM) demonstrated higher accuracy and efficiency in clustering even with high dimensional datasets. In this paper, we introduce a new clustering model based on an optimization technique called Grey Wolf Optimization (GWO) used conjointly with SOM clustering to enhance its clustering performance and accuracy. The evaluation results of our proposed technique show an improvement in the effectiveness and efficiency in comparison with state-of-the-art approaches.

Unsupervised Clustering of Geostationary Satellite Cloud Properties for Estimating Precipitation Probabilities of Tropical Convective Clouds

Abstract Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (&gt;70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT &lt; 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT &lt; 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties. Significance Statement We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability &gt; 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.

Artificial neural network approaches to identify maternal and infant risk and asset factors using Peridata.Net: a WI-MIOS study.

To analyze PeriData.Net, a clinical registry with linked maternal-infant hospital data of Milwaukee County residents, to demonstrate a predictive analytic approach to perinatal infant risk assessment. Using unsupervised learning, we identified infant birth clusters with similar multivariate health indicator patterns, measured using perinatal variables from 2008 to 2019 from n = 43 969 clinical registry records in Milwaukee County, WI, followed by supervised learning risk-propagation modeling to identify key maternal factors. To understand the relationship between socioeconomic status (SES) and birth outcome cluster assignment, we recoded zip codes in Peridata.Net according to SES level. Three self-organizing map clusters describe infant birth outcome patterns that are similar in the multivariate space. Birth outcome clusters showed higher hazard birth outcome patterns in cluster 3 than clusters 1 and 2. Cluster 3 was associated with lower Apgar scores at 1 and 5 min after birth, shorter infant length, and premature birth. Prediction profiles of birth clusters indicate the most sensitivity to pregnancy weight loss and prenatal visits. Majority of infants assigned to cluster 3 were in the 2 lowest SES levels. Using an extensive perinatal clinical registry, we found that the strongest predictive performance, when considering cluster membership using supervised learning, was achieved by incorporating social and behavioral risk factors. There were inequalities in infant birth outcomes based on SES. Identifying infant risk hazard profiles can contribute to knowledge discovery and guide future research directions. Additionally, presenting the results to community members can build consensus for community-identified health and risk indicator prioritization for intervention development.

Clustering energy support beliefs to reveal unique sub-populations using self-organizing maps

Americans’ support for energy sources is quite complex and dependent on a range of socio-demographic characteristics. In a novel approach to investigate energy opinions on renewables and fossil fuels, self-organizing maps are employed to cluster individuals solely on their energy beliefs using social survey data collected from Pew Research in the Spring of 2021. These energy preference clusters are then used in regression models to examine attitudes regarding energy policy in the United States. Results from the self-organizing map (SOM) analysis reveal four distinct clusters: energy traditionalists who oppose renewable sources due to partisan ideologies; energy renewers who strongly prefer investment in only renewable energy sources; energy universalists who universally support all forms of energy; and the aberrant cluster, individuals who prefer solar power greatly over wind energy but demonstrate no other energy preference patterns. Results from regression analyses reveal that SOM clusters are highly predictive of attitudes regarding energy policy. Taken together, these results reveal the unique capability of machine learning to categorize human attitudes – which should be of particular interest to energy policymakers when considering the opinions of the electorate.

Investigations of synoptic wind profile patterns in complex urban areas based on LiDAR measurements

Accurately assessing the variations in wind profiles is very significant for wind engineering and atmospheric research. The complexity of topography poses challenges to accurately predicting wind profile patterns. To accurately evaluate the impact of the heterogeneity of the underlying urban surface and mountainous terrain on the synoptic wind profiles, the vertical profiles of synoptic winds below 1032 m in Shenzhen were investigated for six months based on a high-spatiotemporal-resolution light detection and ranging (LiDAR) system. Specifically, the wind speed and direction profile patterns were summarized and analyzed using a self-organizing map (SOM) clustering technique, considering all possible atmospheric stability conditions. The results indicate that SOMs are effective tools for extracting synoptic wind speed and direction profile patterns based on the sum of squared errors (SSE) and variance ratio criterion (VRC) values. Additionally, the heterogeneity of the underlying urban surface and mountainous terrain has considerable effects on the wind speed and direction profile patterns. The wind speed and wind direction profiles can be divided into three distinct categories. Due to the presence of mountainous terrain, gradient winds can rise to altitudes of approximately 800 m, causing the mean wind twist angles to exceed 45° at heights below 1 km. The maximum wind twist angles can reach 100°. Moreover, this study offers improved models for describing diverse wind speed and direction profile patterns. These models may be beneficial in future research and engineering applications.

ESPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells

BackgroundBioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.ResultsThis study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations.ConclusionseSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations.

Petrophysical interpretation and reservoir characterisation on Proterozoic shales in National Drilling Initiative Carrara 1, Northern Territory

The Proterozoic succession in the National Drilling Initiative Carrara 1 drill hole, Northern Territory, is dominated by tight shales, siltstones and calcareous clastic rocks. As part of Geoscience Australia’s Exploring for the Future program, this study aimed to improve the Proterozoic shale gas reservoir characterisation by derivation of porosity, permeability and gas content from laboratory testing and machine learning approaches to wireline log interpretation. The Proterozoic Lawn Hill Formation is divided into four chemostratigraphic packages. The middle two packages are further divided into seven internal units according to principal component analysis and self-organising map clustering on well logs and inorganic geochemical properties. Artificial neural networks were then applied to interpret the mineral compositions, porosity and permeability from well logs, density and neutron-density crossplot interpretations. Gas content was estimated from the interpreted porosity, gas saturation, total organic carbon and clay contents. Petrophysical interpretation results are summarised for all chemostratigraphic packages and units. P2 (1126.3–1430.1 m) has the highest potential among the four chemostratigraphic packages. P2U1 (1126.3–1271 m) and P2U3 (1335.5–1430.1 m) units have the most favourable petrophysical properties for organic-rich shales, with average total gas contents of 1.213 and 1.315 cm3/g, geometric mean permeability of 6.6 and 25.31 µD and net shale thickness of 53.5 and 83.3 m, respectively. P3U4 (687.9–697.9 m) has high gas content and permeability, with a net shale thickness of 35.9 m. The tight non-organic-rich siltstone and shale reservoirs in package P1 (below 1430.1 m) have an average gas saturation of 17.4% and a geometric mean permeability of 0.48 µD.

Multi-scale geophysical characterization of microporous carbonate reservoirs utilizing machine learning techniques: An analog case study from an upper Jubaila formation outcrop, Saudi Arabia

Microporosity hosts a significant portion of the total hydrocarbon volume in the Middle Eastern carbonate reservoirs. An improved understanding of microcrystals' morphology that hosts micropores, their impact on reservoir properties, and their spatial distributions will contribute to a more accurate reservoir quality prediction. This study proposes a methodology that utilizes machine learning approaches for microporosity characterization and integrates a multi-scale dataset ranging from micrometer-scale SEM images to meter-scale seismic attributes to build reservoir porosity models. We tested the methodology on an outcrop in Riyadh, Saudi Arabia, which exposes the upper part of the Jubaila Formation equivalent to the lower part of the Arab-D reservoir. We acquired a 35 m-long core and a 600 m-long 2D seismic line behind the outcrop. Laboratory-scale petrophysical measurements, including porosity, permeability, acoustic velocity, bulk and grain density, and x-ray diffraction, were performed over 106 horizontal core plugs drilled from the core. We quantitatively characterized the morphology and microtextures of micrite crystals using SEM images by performing Random Forest classifications trained on SEM image features. We performed unsupervised classification using Self-Organizing Map (SOM) to all lab-measured properties for data clustering. We investigated potential correlations between SOM clusters with micrite morphology, which resulted in a predictable relationship following the granularity and sphericity of microcrystals with porosity, permeability, and acoustic velocity. It was possible to represent multiple lithofacies with a single log-linear porosity-permeability relationship and a single value of equivalent differential effective medium (DEM) aspect ratio in velocity-porosity space. The inter-relationship between micrite morphology and microporosity in the well was then propagated to reservoir-grid scale using inverse differential effective medium (DEM) of acoustic impedance from inverted seismic data. The methodology developed in this study thus provides a practical way to integrate key sub-grid scale micro-and macro-heterogeneities into reservoir scale property models.

Multi-USV cooperative path planning by window update based self-organizing map and spectral clustering

Unmanned surface vehicles (USVs) have shown tremendous application prospects recently. In this paper, a hierarchical two-layer framework is proposed for the cooperative path planning of multi-USV system in complex marine environments. The whole framework incorporates the low-level layer planning the optimal path for each USV, and the high-level layer assigning targets for the multi-USV system. First, to avoid collisions and access multiple targets in the optimal sequence, an improved self-organizing map (SOM) based on window update is proposed. The energy cost is calculated to evaluate the path planning results and construct a similarity graph. Then, the spectral clustering method is utilized based on the customized graph to determine the allocated targets which each USV should access in the low-level layer. Finally, a double smoothing strategy based on B-spline is proposed, and the line-of-sight (LOS) guidance method and active disturbance rejection control (ADRC) controller are used to achieve accurate tracking of paths by USV. The simulation results in various scenarios validate the feasibility and effectiveness of our method for multi-USV cooperation, as well as path traceability.