Log Normalization Research Articles

CROSSLINK DENSITY AND ITS DISTRIBUTION IN HEAT AND OIL RESISTANT ELASTOMERS BY DOUBLE QUANTUM NUCLEAR MAGNETIC RESONANCE

ABSTRACT Double quantum (DQ) nuclear magnetic resonance (NMR) was used to characterize the crosslink density, crosslink density distribution, and defect level in a series of heat and oil resistant elastomers. A wide range of defect levels, crosslink densities, and crosslink density distributions was measured, and results depended on elastomer type and compound formulations, including the vulcanization system. The sol fraction defect level generally correlated with the concentration of added plasticizer in the formulation. The presence of polar side chains appeared to cause additional dynamic contributions to the dangling chain end fraction. The large differences in elastomer composition and rubber formulations prevented meaningful correlation of the measured crosslink densities with the low strain modulus. Fast Tikhonov regularization and log normalization fitting of the corrected DQ build-up curve was extremely useful to provide insight into the modality and widths of the crosslink density distributions. A high degree of heterogeneity of the crosslink network of heat and oil resistant elastomers was found. Crosslink density distributions were explained in terms of the polymer chain structure comprised of monomer sequencing coupled with the position of the crosslinking sites. The type of vulcanization system had a lesser effect of the nature of the crosslink density distribution. The primary polymer chain crosslinking sites may become segregated from the continuous phase due to polarity differences seen in the microstructure of oil and heat resistance elastomers. The development of such micromorphologies can favor curative partitioning. The sole use of DQ NMR can provide valuable insight into the nature of the polymer chain structure and crosslink network in rubber.

ScLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Multiclass liver disease prediction with adaptive data preprocessing and ensemble modeling

Liver disease includes a range of health conditions resulting from various factors that impact the normal functioning of the liver over an extended period. Effective treatment of Hepatitis C, a type of liver disease, can be significantly enhanced by accurately and timely predicting risk factors and severity, covering different stages like fibrosis and cirrhosis. This research utilizes the Hepatitis C dataset from the UCI repository to present a comprehensive framework for the prediction of liver disease across various stages. We have proposed an adaptive data preprocessing technique designed to enhance the efficacy of our foundational ML models. This method incorporates class specific mean imputation, outlier rejection, log normalization, feature selection, feature scaling and data balancing. The proposed preprocessing technique underwent rigorous analysis. Furthermore, we also proposed a set of ensemble models by combining basic ML classifiers to further improve the accuracy of liver disease predictions. The best model underwent rigorous parameter optimization and exhibited remarkable training and testing accuracy, reaching up to 99.87% and 99.80% respectively, outperforming earlier research on the dataset. A user-friendly interface was developed to enhance user interaction and practical use, enabling medical professionals to effortlessly enter patient information and promptly receive assessments regarding the risk factors associated with liver disorders.

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.

Machine Learning-Based Intrusion Detection on Multi-Class Imbalanced Dataset Using SMOTE

The rapid development of information technology has brought numerous benefits to society, but it has also led to increased security vulnerabilities in network systems. Intrusion detection systems (IDS) play a crucial role in identifying malicious activities, but they face challenges due to imbalanced datasets where the number of attack samples outweighs normal activities. This paper explores the performance of an IDS using SMOTE (Synthetic Minority Over-sampling Technique) and various classification algorithms to address imbalanced datasets and enhance detection of multi-class intrusions. Related works in the field of intrusion detection are reviewed, highlighting the effectiveness of different algorithms and techniques. The proposed work presents a model that combines SMOTE with log normalization and feature selection to improve IDS performance. Experiments are conducted on the NSL-KDD and CIC-IDS2017 datasets, evaluating different oversampling configurations and machine learning models. The results show that applying SMOTE improves overall performance, with high accuracy, precision, recall, and F1-score. Feature selection has minimal impact on model performance, suggesting the presence of redundant features. The study concludes that SMOTE effectively addresses class imbalance and enhances IDS performance, emphasizing the importance of incorporating oversampling techniques in intrusion detection systems.

МЕТОД ТА ЗАСІБ МОНІТОРИНГУ БЕЗПЕКИ В КОМП'ЮТЕРНІЙ МЕРЕЖІ ЗАСОБАМИ SIEM

This work focuses on researching, analyzing, and enhancing methods and tools for security monitoring in computer networks. The study develops security monitoring tools and methods based on SIEM agents, improving the data normalization process from security logs. The research explores SIEM's role in the SIEM-EDR-NDR triad perspective to accelerate responses to network security threats. The investigation is grounded in the experiences of foreign companies and domestic banking networks. The interaction of SIEM-EDR-NDR components, forming a SOC triad, is examined. SIEM is utilized for centralized data analysis, including EDR and NDR, providing a comprehensive security overview. EDR detects and responds to threats on endpoints, complemented by NDR, extending SIEM analysis. This combination ensures effective response to cyberattacks, reducing "dwell time" until detection. The formulation of tasks for EDR components in the SIEM-EDR-NDR triad is discussed. Emphasis is placed on the importance of protecting endpoints at all stages of an attack, and effective strategies, such as traffic analysis, application control, and centralized cybersecurity management, are identified. Integration of EDR with existing security tools to create a comprehensive system is highlighted. Within the SIEM context, data processing stages, from log collection and normalization to event classification and correlation, are illuminated. The role of correlation in incident formation and investigation is underscored. An enhanced normalization scheme with an expanded agent deployment and key data processing stages within the SIEM system is proposed. The work addresses the improvement of event log processing in SIEM for effective network security monitoring and timely threat mitigation. The achieved goal accelerates threat response processes through SIEM agent integration, facilitating the organization and classification of information flows for prompt threat mitigation.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Abstract As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.

Optimization of Security Information and Event Management (SIEM) Infrastructures, and Events Correlation / Regression Analysis for Optimal Cyber Security Posture

This work integrates logical and physical security processes, and simplifies the manageability of the security infrastructure. The process increases visibility to resources, which makes it easier to prevent security incidents, and provides a platform to manage the response and recovery after an incident occur. Log collection is the heart and soul of a SIEM. Log correlation is employed to identify particular sequences of log events from devices. The comparison between network level and host level events automatically perform initial validation that would not normally be performed. It considers movement of data between systems where it would not normally accounts logging on at unusual times or from unusual places, these may not generate specific security alerts, but can be much more easily spotted and flagged by a log correlation solution that sees everything in the environment. It shows some enhancements to event log normalization and significantly improves correlation rule execution. The event monitoring algorithm and SIEM correlation rules result in false positives or false negatives. Security managers, therefore, may waste time and resources that could be used to respond to real threats and assaults if there are too many false positives. This study hereby, strikes a compromise between lowering false positive alerts and not ignoring any potential abnormalities that could indicate a cyberattack when establishing SIEM correlation rules. In order to decide which data is pertinent and which data is irrelevant in an event pipeline, this research employs the use of filters. Through this examination, it can be inferred that the conditions are advantageous for promoting investment in the growth and enhancement of this technology as an essential component of industrial control systems with security operation centers, as well as offering cyber security management for small and medium-sized enterprises (SMEs) with restricted security expertise and capabilities.

Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction

The economic value of a mineral resource is highly dependent on the accuracy of grade estimations. Accurate predictions of mineral grades can help businesses decide whether to invest in a mining project and optimize mining operations to maximize the resource. Conventional methods of predicting gold resources are both costly and time-consuming. However, advances in machine learning and processing power are making it possible for mineral estimation to become more efficient and effective. This work introduces a novel approach for predicting the distribution of mineral grades within a deposit. The approach integrates machine learning and optimization techniques. Specifically, the authors propose an approach that integrates the random forest (RF) and k-nearest neighbor (kNN) algorithms with the marine predators optimization algorithm (MPA). The RFKNN_MPA approach uses log normalization to reduce the impact of extreme values and improve the accuracy of the machine learning models. Data segmentation and the MPA algorithm are used to create statistically equivalent subsets of the dataset for use in training and testing. Drill hole locations and rock types are used to create each model. The suggested technique’s performance indices are superior to the others, with a higher R-squared coefficient of 59.7%, a higher R-value of 77%, and lower MSE and RMSE values of 0.17 and 0.44, respectively. The RFKNN_MPA algorithm outperforms geostatistical and conventional machine-learning techniques for estimating mineral orebody grades. The introduced approach offers a novel solution to a problem with practical applications in the mining sector.

FastRNA: An efficient solution for PCA of single-cell RNA-sequencing data based on a batch-accounting count model

Almost always, the analysis of single-cell RNA-sequencing (scRNA-seq) data begins with the generation of the low dimensional embedding of the data by principal-component analysis (PCA). Because scRNA-seq data are count data, log transformation is routinely applied to correct skewness prior to PCA, which is often argued to have added bias to data. Alternatively, studies have proposed methods that directly assume a count model and use approximately normally distributed count residuals for PCA. Despite their theoretical advantage of directly modeling count data, these methods are extremely slow for large datasets. In fact, when the data size grows, even the standard log normalization becomes inefficient. Here, we present FastRNA, a highly efficient solution for PCA of scRNA-seq data based on a count model accounting for both batches and cell size factors. Although we assume the same general count model as previous methods, our method uses two orders of magnitude less time and memory than the other count-based methods and an order of magnitude less time and memory than the standard log normalization. This achievement results from our unique algebraic optimization that completely avoids the formation of the large dense residual matrix in memory. In addition, our method enjoys a benefit that the batch effects are eliminated from data prior to PCA. Generating a batch-accounted PC of an atlas-scale dataset with 2 million cells takes less than a minute and 1 GB memory with our method.

Grabbing the Long Tail: A data normalization method for diverse and informative dialogue generation

Recent neural models have shown significant progress in dialogue generation. Among those models, most of them are based on language models, yielding the generation word by word according to the previous context. Due to the inherent mechanism in language models, as well as the most frequently used cross-entropy function (making the distribution of generations approximate that of training data continuously), trained generation models inevitably tend to generate frequent words in training datasets, leading to low diversity and poor informativeness issues. By investigating a few mainstream dialogue generation models, we find that the probable cause is the intrinsic Long Tail Phenomenon in linguistics. To address these issues of low diversity and poor informativeness, we explore and analyze a large corpus from Wikipedia, and then propose an efficient frequency-based data normalization method, i.e., Log Normalization. Furthermore, we explore another two methods, Mutual Normalization and Log-Mutual Normalization, to eliminate the mutual information effect. In order to validate the effectiveness of the proposed methods, we conduct extensive experiments on three datasets with different subjects, including social media, film subtitles, and online customer service. Compared with the vanilla transformers, generation models augmented with our proposed methods achieve significant improvements in generated responses, in terms of both diversity and informativeness. Specifically, the unigram and bigram diversity in the responses are improved by 8.5%–14.1% and 19.7%–25.8% on the three datasets, respectively. The informativeness (defined as amounts of nouns and verbs) is increased by 13.1%–31.0% and 30.4%–59.0%, respectively. Moreover, our methods can be adapted to new generation models efficiently and effectively, with their model-agnostic characteristics.

ЕКОНОМІЧНА АКТИВНІСТЬ І ЗАБРУДНЕННЯ НАВКОЛИШНЬОГО СЕРЕДОВИЩА. ДОСЛІДЖЕННЯ ЄВРОПЕЙСЬКИХ КРАЇН ДО І ПІСЛЯ СПАЛАХУ ЕПІДЕМІЇ COVID-19

It may be said that COVID-19 eased the nature from human pollution. Present paper may argue this perception by analyzing the connection between the economic impact before and after the COVID-19 outbreak, measured by national GDP and the level of pollution in the European countries, referring to air and water pollution. A simple linear regression model with two control variables was applied in order to obtain the results (the control variable were the number of COVID-19 cases and the austerity measures applied in the countries, in the specified period, classified by the impact over the labor productivity). Data was aggregated using the population values for each country and normalized using log normalization method. The paper also engages a multivariable regression, with exogenous variables composed of all NACE REV 2 activities that contribute to national GDP and endogenous variable being the composite pollution index (based on air and water pollution indicators). The control variables applied in the multivariable regression are the same as the ones described below. Results show that DGP dropped with 16.26 % from Q4 of 2019 to Q2 of 2020 and with 5.86 % from the same period of last year till second semester of this year, based on the austerity measures taken in order to stop the spreading SARS-CoV-2, especially the ones considering non trivial activity closure or entire sector closure. Findings include also a percent of 30% of air quality improvement (in terms of particle matter presence) during COVID-19 lockdown in Europe, that had a positive impact even after lockdown was suspended and a general water quality improvement of 32 % from 2018 to 2020. A moral problem is presented in the present paper: did COVID-19 killed or saved many lives, due to air pollution reduction, while fatality rate of COVID-19 is 1.4 % [40] and air pollution fatality rate was 7.6 % in 2016 [18], before air quality improved?

The characteristics of tight oil sand reservoir-source assemblage in lacustrine basins: A case study of the Cretaceous Qingshankou Formation, Songliao Basin, northeast China

The complicated source-reservoir-assemblage characteristics of lacustrine tight oil sand in China are the main controlling factors of tight reservoir oiliness (i.e., oil richness). Several studies have focused on qualitative description of source-reservoir-assemblage characteristics without quantitative assessment. In this study, reservoir-source-assemblage (RSA) has been evaluated quantitatively by fitting the RSA log in the evaluation of Qijia Depression in the Songliao Basin. Total organic carbon (TOC) and sand volume (Vs) logs are used to fit the RSA log in three steps: (1) TOC and Vs log fitting and normalization, (2) RSA log fitting, and (3) extraction of root-mean-square (rms) amplitude and frequency (Frq(0)) information from the RSA log. The rms represents the reservoir capability and hydrocarbon potential, and Frq(0) represents the interbedding frequency that changes with the lake level. Positive values (0–1] of the RSA log correspond to a high lake level, whereas negative values [[Formula: see text], 0) correspond to a low lake level. Based on RSA log values, we defined the parameter RSAsuf, a product of rms and Frq(0), to quantitatively evaluate the tight oil sweet spot. RSAsurf serves as tight oil sweet spot indicator and correlates positively to oil richness. As a result, four types of effective reservoirs (RI, RII, RIII, and RIV), two types of effective sources (SI and SII), and three types of RSAs (R-S-R, S-R-S, and S-S-R) are identified based on cores and RSA logs. High RSAsuf values on the isoline map indicate the sweet spot zones around the G933 and J392 well areas, which correlates very well with the oilfield test data. The approach is appropriate for lacustrine basins with complicated RSA, in which RSA logs serve as indicator for the sedimentary rhythm, reservoir capability, and hydrocarbon potential.

Lumbar Puncture Test in Normal Pressure Hydrocephalus: Does the Volume of CSF Removed Affect the Response to Tap?

There is limited evidence to support the use of high-volume lumbar taps over lower-volume taps in the diagnosis of normal pressure hydrocephalus. The purpose of this study is to detect whether the volume of CSF removed from patients undergoing high-volume diagnostic lumbar tap test for normal pressure hydrocephalus is significantly associated with post-lumbar tap gait performance. This retrospective study included 249 consecutive patients who underwent evaluation for normal pressure hydrocephalus. The patients were analyzed both in their entirety and as subgroups that showed robust response to the lumbar tap test. The volume of CSF removed was treated as both a continuous variable and a discrete variable. Statistical tests were repeated with log-normalized volumes. This study found no evidence of a relationship between the volume of CSF removed during the lumbar tap test and subsequent gait test performance in the patient population (Pearson coefficient r = 0.049-0.129). Log normalization of the volume of CSF removed and controlling for age and sex failed to yield a significant relationship. Subgroup analyses focusing on patients who showed greater than 20% improvement in any of the gait end points or who were deemed sufficiently responsive clinically to warrant surgery also yielded no significant relationships between the volume of CSF removed and gait outcomes, but there were preliminary findings that patients who underwent tap with larger-gauge needles had better postprocedure ambulation among patients who showed greater than 20% improvement in immediate time score (P = .04, n = 62). We found no evidence to support that a higher volume of CSF removal impacts gait testing, suggesting that a high volume of CSF removal may not be necessary in a diagnostic lumbar tap test.

News Category Classification Using Mutual Information and Log Normalization

최근 트위터, 페이스북, 인스타그램 등 사용자들이 쉽게 이용할 수 있는 소셜 네트워크 발달로 카테고리 종류가 다양해져 정확한 카테고리를 분류하기 어려운 상황이다. 기존의 카테고리 분류 방법의 하나인 빈도수 기반 지도학습은 기준 데이터별 단어 개수에 따라 중복 단어 개수가 많은 카테고리로 분류되었다. 빈도수 차이에 따른 카테고리 오분류를 개선하기 위해 본 논문에서는 단어 개수가 작은 카테고리에 높은 가중치를 부여하는 로그 정규화와 두 문서의 정보를 확률 기반으로 비교하는 상호 정보량을 이용해 뉴스 웹 문서의 카테고리 분류방법을 제시하고, 빈도수 기반 지도학습 연구와 비교 실험한다. 실험 결과 기존 연구인 빈도수 기반 지도학습의 카테고리 분류 보다 본 연구의 카테고리 분류 결과의 정확도 성능이 3.28% 향상되었다.

A Novel Work Flow of Density-Log Normalization for Coalbed-Methane Wells: An Example From the Surat Basin in Australia

Summary In coalbed-methane (CBM) projects, conventional logs, such as density were used heavily for coal identification, coal-property derivation, and also potentially for coal ranking. This paper presents a novel work flow of density-log normalization for CBM wells. It addresses two major challenges to current normalization methods: absence of a pervasive reference layer and removal of geological effects after normalization. To overcome these disadvantages, the proposed work flow undertakes six steps, which are data quality control (QC), geological-area segmentation, subarea type-well selection, density-control points, variable linear transformation and normalized-log result QC with box plots, matrix crossplots, and several histograms. This work flow removes the systematic errors caused by acquiring logs at different times with different logging instruments provided by different vendors. Furthermore, it possesses the advantages of the automated QC work flow, type-well control, and reference-layer methods without incurring their respective disadvantages. It was applied successfully to approximately 600 wells covering a large area in the Surat basin of Australia. Proper log normalization will reduce the uncertainties involved in 3D geological modeling and will increase the accuracy of reservoir evaluation. This paper should therefore be of interest to petrophysicists and geologists involved in CBM-field characterization.

A Best Practice in Static Modeling of a Coalbed-Methane Field: An Example From the Bowen Basin in Australia

Summary The development of a coalbed-methane (CBM) field in its early stage is often plagued by the lack of well control and the scarcity of geological data across a large geographical area. Therefore, constructing a representative static model to estimate the in-place volume presents a formidable challenge. In this paper, we propose a work flow to overcome this challenge and apply it to a CBM field in the northern Bowen basin of Australia. One may consider this work flow as a best practice for the following reasons. First, it makes use of data from various sources including cores, well logs, seismic interpretation, and topography. Second, it performs rigorous quality control on these data, such as depth shift and log normalization. Third, coal-ply division and correlation and subsequent structural modeling are based on three types of correlation: well-to-well, well-to-seismic, and well-seismic-geographic information system. Fourth, it establishes the low, base, and high trends for the most-important reservoir properties. Fifth, it constructs a base-case static model by combining the aforementioned structural and reservoir-property models. Sixth, it uses sensitivity analysis, which varies one reservoir parameter at a time, to rank the impact of reservoir parameters on in-place volume. Seventh, it uses uncertainty analysis that varies all reservoir parameters simultaneously to arrive at the P10, P50, and P90 in-place volumes and their corresponding static models that one can use for reservoir simulations to estimate the recoverable volumes.

WE-G-BRF-07: Non-Circular Scanning Trajectories with Varian Developer Mode

Purpose: Cone-beam CT (CBCT) in image-guide radiation therapy (IGRT) typicallyacquires scan data via the circular trajectory of the linearaccelerator's (linac) gantry rotation. Though this lends itself toanalytic reconstruction algorithms like FDK, iterative reconstructionalgorithms allow for a broader range of scanning trajectories. Weimplemented a non-circular scanning trajectory with Varian's TrueBeamDeveloper Mode and performed some preliminary reconstructions toverify the geometry. Methods: We used TrueBeam Developer Mode to program a new scanning trajectorythat increases the field of view (FOV) along the gantry rotation axiswithout moving the patient. This trajectory consisted of moving thegantry in a circle, then translating the source and detector along theaxial direction before acquiring another circular scan 19 cm away fromthe first. The linear portion of the trajectory includes an additional4.5 cm above and below the axial planes of the source's circularrotation. We scanned a calibration phantom consisting of a lucite tubewith a spiral pattern of CT spots and used the maximum-likelihoodalgorithm to iteratively reconstruct the CBCT volume. Results: With the TrueBeam trajectory definition, we acquired projection dataof the calibration phantom using the previously described trajectory.We obtained a scan of the treatment couch for log normalization byscanning with the same trajectory but without the phantom present.Using the nominal geometric parameters reported in the projectionheaders with our iterative reconstruction algorithm, we obtained acorrect reconstruction of the calibration phantom. Conclusion: The ability to implement new scanning trajectories with the TrueBeamDeveloper Mode enables us access to a new parameter space for imagingwith CBCT for IGRT. Previous simulations and simple dual circle scanshave shown iterative reconstruction with non-circular trajectories canincrease the axial FOV with CBCT. Use of Developer Mode allowsexperimentally testing these and other new scanning trajectories. Support was provided in part by the University of Chicago Research Computing Center, Varian Medical Systems, and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the supporting organizations.

Method of Secondary Logging Interpretation of Heterogeneous Reservoirs in the Huan 26 Block, Liaohe Huanxiling Oilfield

The reservoir units in Liaohe oilfield showed strong heterogeneities. A process of log normalization and a set of logging interpretation models appropriate to the reservoir bed characters of this section were established based on the first logging interpretation results and the core analysis. After normalization of all log curves, and according to the interpretation models that have been established, the secondary logging interpretation was applied in the study area and the interpretation models were proved to be feasible and reliable by the application of the first logging interpretation results and the core analysis. The method can not only provide instruction for further reserves calculation and geological modeling, but also be a foundation for numerical simulation.

Application of well log normalization in coalfield seismic inversion

During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.