Probability Of Category Research Articles

Incidence and Predictors of Acute Kidney Injury Among Newly Diagnosed Type 2 Diabetes Patients at Chronic Follow-Up Clinic of University of Gondar Comprehensive Specialized Hospital: A Retrospective Follow-Up Study.

BackgroundAcute kidney injury is a common disorder worldwide, occurring in more than 13 million per year, 85% of whom live in developing countries. The high incidence of acute kidney injury among type 2 diabetic patients is a major cause of morbidity and mortality. There is limited data that address the incidence and predictors of acute kidney injury to apply evidence-based interventions in developing countries including Ethiopia specifically in the study area.MethodsInstitution-based retrospective follow-up study was conducted among 420 adults with newly diagnosed type 2 diabetes patients from January 1, 2014, to December 31, 2019. Log rank test and Kaplan–Meier curve were used to compare different categories of survival probability. In a multivariable analysis, variable having a p-value <0.05 in the Cox, proportional hazard model was considered as independent predictors.ResultsOverall, 19.76% (95% CI; 16.2–23.8) of the study population developed acute kidney injury, with a median follow-up period of 30.75 months. Congestive heart failure [adjusted hazard ratio (AHR): 2.89 (95% CI; 1.62, 5.13)], chronic kidney disease [AHR: 2.92 (95% CI; 1.56, 5.48)], hypertension [AHR: 2.87 (95% CI; 1.20, 6.90)], and diabetic nephropathy [AHR: 2.04 (95% CI; 1.13, 3.68)] were found to be predictors of acute kidney injury.ConclusionThe incidence of acute kidney injury among type 2 diabetes patients was high in the study area. In patients with hypertension congestive heart failure, chronic kidney disease, and diabetic nephropathy efforts should be made to diagnose AKI early and treat it – in addition to better control accordingly among type 2 diabetes mellitus patients.

Rasch analysis for development and reduction of Symptom Questionnaire for Visual Dysfunctions (SQVD)

To develop the Symptom Questionnaire for Visual Dysfunctions (SQVD) and to perform a psychometric analysis using Rasch method to obtain an instrument which allows to detect the presence and frequency of visual symptoms related to any visual dysfunction. A pilot version of 33 items was carried out on a sample of 125 patients from an optometric clinic. Rasch model (using Andrich Rating Scale Model) was applied to investigate the category probability curves and Andrich thresholds, infit and outfit mean square, local dependency using Yen’s Q3 statistic, Differential item functioning (DIF) for gender and presbyopia, person and item reliability, unidimensionality, targeting and ordinal to interval conversion table. Category probability curves suggested to collapse a response category. Rasch analysis reduced the questionnaire from 33 to 14 items. The final SQVD showed that 14 items fit to the model without local dependency and no significant DIF for gender and presbyopia. Person reliability was satisfactory (0.81). The first contrast of the residual was 1.908 eigenvalue, showing unidimensionality and targeting was − 1.59 logits. In general, the SQVD is a well-structured tool which shows that data adequately fit the Rasch model, with adequate psychometric properties, making it a reliable and valid instrument to measure visual symptoms.

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study.

Background:In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.Methods:We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using1.raw laboratory versus normalized data and2.training vs testing datasets (n = 361 and n = 106/361≅30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]).An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.Results:We observed that1.the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data,2.the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and3.a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study.Conclusions:Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

Robust link functions

In binary and ordinal response models outlying covariates as well as incoherent responses may affect the reliability of the maximum likelihood estimators and that of the derived inferential procedures. However the various link functions, which provide the relationship between the linear predictor and the probabilities of the response categories, differ in terms of sensitivity to anomalous data. The current paper derives conditions useful to evaluate the properties of the link functions with respect to robustness, either when the covariates are outlier free or when extreme design points may occur. The main results show that – by an appropriate choice of the link function – robust estimators, with a bounded influence function, can be easily derived from the usual likelihood function, while preserving the predictive ability of the fitted model.

METAPHOR OF MODALITY IN CLASSROOM INTERACTION

METAPHOR OF MODALITY IN CLASSROOM INTERACTION *Raissa Sati ** Dr. Siti Aisah Ginting, M.Pd **Indra Hartoyo, S.Pd, M. Hum ABSTRACT This study aims at analyzing the types and realization of metaphor of modality in classroom interaction of grade eleven Global Prima National Plus Medan. Qualitative research design was used by the researcher to conduct this study. The subjects of this study were the teacher and the students in grade eleven Global Prima National Plus Medan in academic 2018/2019. The data were collected by observed and recorded to find the types of metaphor of modality being used and also to find out how the metaphor of modality is realized in classroom interaction. The data were analyzed by using Miles and Huberman data analysis technique. The findings of the study show that teacher and students used all the types of metaphor of modality in classroom interaction, such as modalization (probability 86,79% with 46 occurances, usuality 1,88% with 1 occurance) and modulation (obligation 3,77% with 2 occurances, inclination 7,54% with 4 occurances) . Metaphor of modality in classroom interaction was realized by the category probability (think, believe, possible), usuality (ussually), obligation (be obligied to) dan inclination (be allowed, recommend, permission). Key words: Teaching, English, Culinary.

Data-Driven Fault Detection and Classification for MTDC Systems by Integrating HCTSA and Softmax Regression

The requirement of fast fault isolation poses a great challenge to the safe operation of multi-terminal direct current (MTDC) systems. In order to make a better tradeoff between the speed and reliability of the protection scheme, it is imperative to mine more valuable information from fault transient signals. This paper puts forward a data-driven framework capable of digging out and synthesizing multi-dimensional features to achieve fast and reliable DC fault detection and classification in MTDC systems. Highly comparative time-series analysis (HCTSA) is first adopted to extract extensive features with clear physical interpretations from fault current waveforms, and a few features valuable to fault identification are then selected utilizing the greedy forward search. Based on the reduced features, a softmax regression classifier (SRC) is further proposed to calculate the probability of each fault category with a relatively minor on-line computational burden. Numerical simulations carried out in PSCAD/EMTDC have demonstrated the proposed approach is effective under different fault conditions, robust against noise corruptions as well as abnormal samplings, and replicable in various DC grids. In addition, comprehensive comparison studies with conventional derivative-based protection methods and some typical artificial intelligence based (AI-based) methods have been conducted. It is verified that the proposed method has the advantages of higher fault identification accuracy over conventional protections and shallow structure AI-based methods, better interpretability as well as lower on-line computing complexity over the deep architecture AI-based approaches.

Adoption of COBIT 5 Framework in Risk Management for Startup Company

The research method used is qualitative, where data collection is done by interviewing informants related to risk findings and identification of the root of the problem using fishbone analysis with category 6M (Man, Money, Machine, Material, Method, Measurement). The results of the identification of the root causes are included in the risk quadrant with the risk probability categories (high, medium, low) and risk impact categories (high, medium, low). After getting the data needed, the stages of creating a risk management model that is mapping the results of identifying the root causes with the COBIT 5 framework. The results of interviews related to the risks experienced by small and medium enterprises obtained as many as 19 risks and the results of fishbone analysis (identification of the root causes) obtained as many as 48 root causes, but this study took a quadrant I-VI with a total of 24 root causes. This research produces a risk management model in the form of COBIT 5 process that is in line with the root of the problem that occurs in small and medium-sized enterprises, namely EDM03 (Ensure Risk Optimization), APO12 (Manage Risks), BAI02 (Manage Requirements Definition), DSS05 (Manage Security Service) , MEA02 (Monitor, Evaluate and Assess the System of Internal Control).

One-month outcomes of patients with SARS-CoV-2 infection and their relationships with lung ultrasound signs

BackgroundThe role of lung ultrasound (LUS) in evaluating the mid- and long-term prognoses of patients with COVID-19 pneumonia is not yet known. The objectives of this study were to evaluate associations between LUS signs at the time of screening and clinical outcomes 1 month after LUS and to assess LUS signs at the time of presentation with known risk factors for COVID-19 pneumonia.MethodsThis was a retrospective study of data prospectively collected 1 month after LUS screening of 447 adult patients diagnosed with COVID-19 pneumonia. Sonographic examination was performed in screening tents with the participants seated. The LUS signs (B-lines > 2, coalescent B-lines, and subpleural consolidations) were captured in six areas of each hemithorax and a LUS aeration score was calculated; in addition, the categories of disease probability based on patterns of LUS findings (high-probability, intermediate-probability, alternate, and low-probability patterns) were evaluated. The LUS signs at patients’ initial evaluation were related to the following outcomes: symptomatology, the need for hospitalization or invasive mechanical ventilation (IMV), and COVID-19-related death.ResultsAccording to the evaluations performed 1 month after LUS screening, 36 patients were hospitalised, eight of whom required intensive care unit (ICU) admission and three of whom died. The presence of coalescent B-lines was associated with the need for hospitalization (p = 0.008). The presence of subpleural consolidations was associated with dyspnoea (p < 0.0001), cough (p = 0.003), the need for hospitalization (p < 0.0001), the need for ICU admission (p < 0.0001), and death (p = 0.002). A higher aeration score was associated with dyspnoea (p < 0.0001), the need for hospitalization (p < 0.0001), the need for ICU admission (p < 0.0001), and death (p = 0.003). In addition, patients with a high-probability LUS pattern had a higher aeration score (p < 0.0001) and more dyspnoea (p = 0.024) and more often required hospitalization (p < 0.0001) and ICU admission (p = 0.031).ConclusionsIn patients with COVID-19 pneumonia, LUS signs were related to respiratory symptoms 1 month after LUS screening. Strong relationships were identified between LUS signs and the need for hospitalization and death.

Validity of the French version of Catquest-9SF and use of an electronic notepad for entering patient-reported outcome measures

BackgroundThe Catquest-9SF questionnaire is a patient reported outcome measure that quantifies the visual benefits from cataract surgery. The purpose of this study was to translate and adapt the Catquest-9SF questionnaire for France, to assess its psychometric properties via Rasch analysis, and to assess its validity when completed using an electronic notepad.MethodsThe Catquest-9SF questionnaire was translated following the guidelines of the International Society for Pharmacoeconomics and Outcomes Research. Catquest-9SF and clinical data were collected from patients before and after routine cataract surgery. All questionnaire data were collected via an electronic notepad. Rasch analysis was performed to assess psychometric properties, and sensitivity to change was analysed for patients with complete paired pre- and post-operative questionnaires.ResultsA complete filled-in preoperative questionnaire was obtained for 848 patients. Rasch analysis showed good precision (person separation: 2.32, person reliability: 0.84), ordered category probability curves, no item misfit, and unidimensionality. The respondents were slightly more able than the level of item difficulty (targeting: −1.12 logits). Sensitivity was analysed on 211 paired questionnaires, and the postoperative questionnaires showed a clear ceiling effect. The effect size was 2.6. The use of an electronic notepad for completing the questionnaire worked out very well after some adjustments.ConclusionsThe French version of Catquest-9SF has good psychometric properties and is suitable for use in French-speaking patients. The use of the Catquest-9SF questionnaire in an electronic format showed good validity.

Voevodsky's unfinished project: Filling the gap between pure and applied mathematics

In a series of lectures given in 2003, soon after receiving the Fields Medal for his results in the Algebraic Geometry, Vladimir Voevodsky (1966–2017) identifies two strategic goals for mathematics, which he plans to pursue in his further research. The first goal is to develop a ‘‘computerised library of mathematical knowledge,’’ which supports an automated proof-verification. The second goal is to ‘‘bridge pure and applied mathematics.’’ Voevodsky's research towards the first goal brought about the new Univalent foundations of mathematics. In view of the second goal Voevodsky in 2004 started to develop a mathematical theory of Population Dynamics, which involved the Categorical Probability theory. This latter project did not bring published results and was abandoned by Voevodsky in 2009 when he decided to focus his efforts on the Univalent foundations and closely related topics. In the present paper, which is based on Voevodsky's archival sources, I present Voevodsky's views of mathematics and its relationships with natural sciences, critically discuss these views, and suggest how Voevodsky's ideas and approaches in the applied mathematics can be further developed and pursued. A special attention is given to Voevodsky's original strategy to bridge the persisting gap between pure and applied mathematics where computers and the computer-assisted mathematics play a major role.

Bibliomining and Comparison of Q4 and ESCI Indexed journals under Statistics and Probability Category

The field of ‘Statistics and Probability’ has expanded its scope over the last few decades and have become an integral part of many fields with continuously increasing demand. This manuscript aimed for at a bibliometric analysis and comparison of all published documents during 2015 – 2019, from journals in the study topic category of ‘Statistics and Probability’ for Q4 Impact Factor (IF) journals and Emerging Source Citation Index (ESCI) of Web of Science (WoS). Sources with incomplete data for study timeframe were excluded and 31 sources from Q4 IF and 32 from ESCI journals were selected yielding 12808 and 4294 documents respectively. After data extraction from WoS, the bibliometric analysis at; source, author and document levels, were performed using “Bibliometrix” R-package. Q4-IF sources produced around 3 times more documents than ESCI sources. Articles were the main document type for both categories. China and USA were leading countries for Q4-IF while India, USA and Korea were dominant among ESCI documents. Two authors, namely, ‘Cordeiro GM’ and ‘Alizadeh M’ were among the 10 most productive authors in both categories. Sources “Communications in Statistics-Theory and Methods” and “Korean Journal of Applied Statistics” were leading contributors for Q4-IF and ESCI category respectively. For both categories, mainly similar trends were observed for keywords and topic coverage. In both Q4-IF and ESCI journals ‘Maximum likelihood’ and ‘Ordered statistics’ were observed to be most predominant keywords. A consistent publication trend with few similarities was observed in terms of documents production over the years for these two categories.

Now You Hear Me, Later You Don’t: The Immediacy of Linguistic Computation and the Representation of Speech

What happens to an acoustic signal after it enters the mind of a listener? Previous work has demonstrated that listeners maintain intermediate representations over time. However, the internal structure of such representations—be they the acoustic-phonetic signal or more general information about the probability of possible categories—remains underspecified. We present two experiments using a novel speaker-adaptation paradigm aimed at uncovering the format of speech representations. We exposed adult listeners (N = 297) to a speaker whose utterances contained acoustically ambiguous information concerning phones (and thus words), and we manipulated the temporal availability of disambiguating cues via visually presented text (presented before or after each utterance). Results from a traditional phoneme-categorization task showed that listeners adapted to a modified acoustic distribution when disambiguating text was provided before but not after the audio. These results support the position that speech representations consist of activation over categories and are inconsistent with direct maintenance of the acoustic-phonetic signal.

Placenta accreta scoring system (PASS)\u2014assessment of a simplified clinico-radiological scoring system for antenatal diagnosis of placenta accreta

BackgroundPlacenta accreta spectrum (PAS) of disorders is an important cause of post-partum hemorrhage and resultant maternal morbidity and mortality. Imaging plays an indispensable role in antenatal diagnosis of PAS. However, diagnosis of PAS on both ultrasonography and magnetic resonance imaging (MRI) is reliant on recognition of multiple imaging signs each of which have a wide range of sensitivity and specificity. There is no single pathognomonic diagnostic feature. This results in interobserver variability. In our study, we aim to assess the accuracy of a combined clinico-radiological scoring system in predicting placenta accreta.ResultsThis retrospective study included 60 MRI examinations done for suspected placenta accreta (PA). MRI findings were assessed by two radiologists in consensus. Clinical details of the patients were obtained from the hospital information system. Two clinical and six imaging criteria were assessed and a total score was calculated for each patient. Patients were stratified into three groups—low, moderate or high probability for placenta accreta based on the total score. The presence of any statistically significant difference in prevalence of PA among these groups was assessed. Intra-operative findings/histopathology were considered the gold standard. The prevalence of PA was 3% (1/33), 28.5% (2/7) and 90% (18/20) in the low-, moderate- and high-risk groups respectively. There was a statistically significant difference in the prevalence between the three groups (chi-square statistic = 41.54, p value < 0.0001). A score of greater than or equal to 6 provided sensitivity, specificity and accuracy of 85.71, 94.87 and 92.5% respectively in diagnosing placenta accreta.ConclusionPASS provides a simple, objective and accurate way to stratify patients into low, intermediate and high probability categories for PA.

Managing patients with suspected postpartum retained products of conception using a novel sonographic classification.

Suspicion of retained products of conception (RPOC) often arises after delivery and still poses a diagnostic and management challenge. To prospectively evaluate a sonographic classification for the management of patients with suspected RPOC after delivery. Based on grayscale and Doppler ultrasound parameters, patients were classified into high, moderate, or low probability of RPOC. For the low and moderate probability groups, an ultrasound follow-up at the end of the puerperium was recommended. For the high probability group, a follow-up examination was conducted 10-14 days after the first ultrasound, and patients with persistent high probability findings were referred for surgical intervention. The sample was composed of 215 patients at risk of RPOC. Of these, 100, 93, and 22 patients were classified as having a low, moderate, or high probability of RPOC, respectively. Rates of RPOC were 55%, 2%, and 2% in the high, moderate, and low probability categories, respectively. When the categorization was based on the most recent ultrasound obtained during the puerperium, the adjusted RPOC prevalence rates were 71% in the high, 6% in the moderate, and 0% in the low probability groups. This study confirms the effectiveness of our sonographic classification for managing patients with suspected RPOC after delivery. In all three categories, it is recommended to adhere to a conservative management protocol in clinically stable women until the end of the puerperium. This approach provides good predictability for RPOC and can reduce unnecessary surgical interventions.

How to Interpret the Effect of Covariates on the Extreme Categories in Ordinal Data Models

This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the response categories both in standard cumulative link models under the proportional odds assumption and in the recent extension of the Combination of Uncertainty and Preference of the respondents models, the mixture models introduced to account for uncertainty in rating systems. The article shows by means of marginal effect measures that the effects of the covariates are underestimated when the uncertainty component is neglected. Visualization tools for the effect of covariates are proposed, and measures of relative size and partial effect based on rates of change are evaluated by the use of real data sets.

Идентификация и оценка социальных рисков государства в рамках территорий с особым экономическим статусом Дальнего Востока

The article presents a study of the social risks of the state in special economic, legal, and organizational regimes using the example of territories with a special economic status in the Russian Far East. An important condition for the creation of special territories for the state is the compensation for the provided preferences in full with benefits in the future. The article contains a content analysis of foreign and domestic literature, based on which the social risks of the state are identified taking into account the specificity of the special territories of the Far East. The authors compile a map of social risks based on the main goals of the creation and development of special territories, and highlight a separate classification feature of the state’s social risks – the non-fulfillment of guarantees for the population. The authors also reveal the problems of justifying indicators for assessing risks. Based on the grouping, analysis and comparison of the official statistical information of Rosstat and the RF Ministry of Economic Development, the authors assess the identified social risks of the state in the Russian Far East territories with a special economic status. The article defines the probability and hazard category of each risk.

Linear combination of chi-squares for multinomial process monitoring

Abstract: Marcucci (1985) proposed a chi square goodness of fit statistic based generalized p-chart for multinomial process monitoring. A chi square distribution quantile was considered as a control chart limit. A weighted chi square goodness of fit statistic-based control chart is proposed for multinomial process monitoring in this paper, where more important weights are advocated to poor quality categories. The statistic distribution is approximated by a well-known linear combination of chi squares distribution. The approximation is assessed through a simulation, an extreme percentile of the approximated distribution is used as an upper control chart limit and a comparison is carried out with a chi square goodness of fit statistic-based control chart. The average run length is used as a benchmark and the comparison is performed using simulations considering two process shifts scenarios. Under some restrictions, the weighted statistic-based control chart allows an earlier detection of process shift in case of deterioration and postpones out of control signals in case of improvement. This benefit is clearer when the process is improved by a decrease in the poor quality probability category and an increase in the best quality category probability.

Ensemble of Multiscale Fine-Tuning Convolutional Neural Networks for Recognition of Benign and Malignant Thyroid Nodules

<p indent=0mm>Aiming at the problem of poor classification performance due to the small number of training image samples and ignoring multiscale structure and texture information, in order to improve the accuracy of diagnosis of benign and malignant thyroid nodule, this paper proposes a method for thyroid nodule ultrasound image recognition based on ensemble of multiscale fine-tuning convolutional neural networks. Firstly, the image is converted into three different scales of information as input data, so that the model can learn the feature information of different scales of the image, and improve the feature extraction ability of the model. Secondly, nine fine-tuning models of three different scales were constructed by optimizing the full-connection layer structure of three kinds of pretraining models (AlexNet, VGG16 and ResNet50) and the transfer learning and fine-tuning strategy, so that the model could better learn the characteristic differences of source domain (ImageNet) and target domain (thyroid ultrasound image). Finally, the optimal fine-tuning model combination is selected and the final integration model is obtained by the weighted fusion method of model output category probability, and the classification performance is further improved by utilizing the diversity of models. The proposed algorithm was compared with other algorithms on the real data set, and the accuracy, sensitivity, specificity and area under curve (AUC) of benign and malignant thyroid nodules were 96.0%, 94.1%, 97.7% and 0.98. The experimental results show that the algorithm is superior to the traditional machine learning algorithm and other algorithms in the field of benign and malignant thyroid nodule identification, and can effectively extract complementary visual feature information with satisfactory classification performance.

Diagnostic Accuracy of D-Dimer Testing and the Revised Geneva Score in the Prediction of Pulmonary Embolism.

BackgroundPulmonary embolism (PE) diagnosis can sometimes be challenging due to the disease having nonspecific signs and symptoms at the time of presentation. The present study aimed to evaluate the validity of the D-dimer in combination with the revised Geneva score (RGS) in the prediction of pulmonary embolism.Patients and MethodsThis is a retrospective study of 2010 patients with suspected PE who had undergone both D-dimer testing followed by chest CT angiography (CTPA), irrespective of the D-dimer test results, at King Abdulaziz Medical City, Riyadh, Saudi Arabia, over 3 years, from Jan. 2016 to Jan. 2019. The predictive accuracy of D-dimer, adjusted D-dimer, and RGS was calculated. The receiver operating characteristic “ROC” curve was applied to allocate the optimum RGS cutoff for PE prediction.ResultsThe overall prevalence of PE was 16%. It was 0%, 25.8%, and 88.9% in low, intermediate, and high clinical probability categories of RGS, respectively. Both conventional and age-adjusted D-dimer thresholds showed significant level of agreement (kappa=0.81, p<0.001), high sensitivity (94% and 92.8%), high negative predictive value “NPV” (91.2% and 91.4%), low specificity (12.3% and 15.3%), and low positive predictive value “PPV” (17.5% and 17.8%), respectively. Combination of the age-adjusted D-dimer threshold and RGS at a cut-off of 5 points would provide 100% sensitivity and 61.7% specificity 34.1% PPV, 100% NPV, and 0.87 area under the curve “AUC”. At an RGS cutoff <5 points, PE could have been ruled out in more than one-half (1036, 51.5%) of all suspected cases, and would have saved the cost of CTPA.ConclusionConventional and age-adjusted D-dimer tests showed high levels of agreement in the prediction of PE, high sensitivity, and low specificity. RGS has a good performance in PE prediction. Using the revised Geneva score alone rules out PE for more than one-half of all suspected without further imaging.

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Author(s): Carle, SF; Fogg, GE | Abstract: Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.