The Most Frequent Value: Introduction to a Modern Conception of Statistics.

Kemp, Adrienne W.; Steiner, F.

doi:10.2307/2532629

Cited by 4 publications

(6 citation statements)

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“…The most frequent value method is based on the weighting of the calculated distance between data points and centroids where the weights are calculated automatically from an iterative algorithm [17]. The weighted average is calculated with the symmetric weight function.…”

Section: Mfv Clusteringmentioning

confidence: 99%

A case study of petrophysical prediction using machine learning integrated with interval inversion in a tight sand reservoir in Egypt

Abdelrahman,

Szabó

2024

IOP Conf. Ser.: Earth Environ. Sci.

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This study presents a new algorithm for reservoir characterization using borehole logging data, which integrates unsupervised machine learning techniques and interval inversion to automatically determine layers’ boundaries and petrophysical parameters. The research aims to reduce the time and manual input required for borehole inversion to estimate petrophysical parameters. The algorithm was used to predict different layer boundaries of sand-shale intercalations for both synthetic and field wireline log data. Field well logging measurements were obtained from an oil and gas field in Egypt, specifically the Jurassic reservoir. The reservoir is composed of a dense sandstone layer with significant heterogeneity due to diagenesis, which converts kaolinite into illite. The algorithm was used to predict petrophysical parameters, resulting in a decrease in porosity and permeability. The field data from the well reveals that the reservoir is made up of varying-quality sandstone, impacting storage capacity and hydrocarbon saturation. The algorithm demonstrates consistent convergence of the data at 7.5%. Overall, the integration of the new cluster technique and interval inversion can improve the time-intensive and laborious process of borehole data inversion to estimate petrophysical parameters.

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Section: Mfv Clusteringmentioning

confidence: 99%

A case study of petrophysical prediction using machine learning integrated with interval inversion in a tight sand reservoir in Egypt

Abdelrahman,

Szabó

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Based on field experiences, it is useful to presume the appearance of both data types along the same profile. Steiner's Most Frequent Value (MFV) method takes into account the difference from the 2D structure by the automatic weighting of the data (Steiner, 1988(Steiner, , 1991(Steiner, , 1997, a useful process for the separation of measured data and the determination of the structure with a combined inversion technique available (Gyulai et al, 2017).…”

Section: Combined Geoelectric Weighted Inversion (Cgwi)mentioning

confidence: 99%

Results from the further development of CGI inversion on simulation and field data systems

Gyulai,

Turai,

Baracza

et al. 2024

CEuGeol

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A new series expansion-based method, the Combined Geoelectric Weighted Inversion (CGWI) procedure is presented and tested by using synthetic and in-field measured datasets. The method is an improved version of the Combined Geoelectric Inversion (CGI) robustified by involving Cauchy-Steiner weights in an Iteratively Reweighted Least Squares technique. The new procedure is compared to the Fourier series expansion-based 1.5D and the CGI methods as well as to the broadly applied RES2DINV inversion procedure. The field measurements are performed during stone exploration in an active quarry on the south-western slopes of the Mátra mountains, in northern Hungary. It is shown that the CGWI method gives stable and robust parameter estimation with acceptable accuracy. The comparison with other inversion methods is based on data distances, estimation errors and correlation parameters calculated on the base of the parameter correlation matrix.

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“…An alternative approach could involve using the most-frequent-value (MFV) method [21][22][23], as demonstrated in recent studies. The MFV approach, based on minimizing information loss, has been successfully applied in various fields [24].…”

Section: Most-frequent-value Approachmentioning

confidence: 99%

“…Steiner's MFV method [21][22][23] can handle this situation. It identifies the most-common value, even if it is not the same as the traditional mode.…”

Section: Most-frequent-value Approachmentioning

confidence: 99%

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Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis

Golovko,

Kamaev,

Sun

2023

Sensors

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The paper explores the application of Steiner’s most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method’s versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 μGy for 1342 h of exposure, with an uncertainty range of +3.41 to −3.59μGy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 μGy, with an uncertainty range of +3.58 to −3.48μGy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research.

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The Most Frequent Value: Introduction to a Modern Conception of Statistics.

Cited by 4 publications

References 0 publications

A case study of petrophysical prediction using machine learning integrated with interval inversion in a tight sand reservoir in Egypt

A case study of petrophysical prediction using machine learning integrated with interval inversion in a tight sand reservoir in Egypt

Results from the further development of CGI inversion on simulation and field data systems

Unveiling Insights: Harnessing the Power of the Most-Frequent-Value Method for Sensor Data Analysis

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