Application of the maximum‐likelihood method (MLM) for sonic velocity logging

Geophysics ◽  
1986 ◽  
Vol 51 (3) ◽  
pp. 780-787 ◽  
Author(s):  
Kai Hsu ◽  
Arthur B. Baggeroer

Modern digital sonic tools can record full waveforms using an array of receivers. The recorded waveforms are extremely complicated because wave components overlap in time. Conventional beamforming approaches, such as semblance processing, while robust, sometimes do not resolve the interfering wave components propagating at similar speeds, such as multiple compressional arrivals due to the formation alteration surrounding the borehole. Here the maximum‐likelihood method (MLM), a high‐resolution array processing algorithm, is modified and applied to process borehole array sonic data. Extensive modifications of the original MLM algorithm were necessary because of the transient character of the sonic data and its effect upon the spectral covariance matrix. We applied MLM to several array sonic data sets, including laboratory data, synthetic waveforms, and field data taken by a Schlumberger array sonic tool. MLM’s slowness resolution is demonstrated in resolving a secondary compressional arrival from the primary compressional arrival in an altered formation, and the formation compressional arrival in the presence of a stronger casing arrival in an unbonded cased hole. In comparison with the semblance processing results, the MLM results clearly show a better slowness resolution. However, in the case of a weak formation arrival, the semblance processing tends to enhance and resolve the weak arrival by the semblance normalization procedure, while the MLM, designed to estimate the signal strength, does not. The heavy computational requirement (mainly, many matrix inversions) in the MLM makes it much slower than semblance processing, which may prohibit implementation of the MLM algorithm in a real‐time environment.

Author(s):  
Fiaz Ahmad Bhatti ◽  
G. G. Hamedani ◽  
Haitham M. Yousof ◽  
Azeem Ali ◽  
Munir Ahmad

A flexible lifetime distribution with increasing, decreasing, inverted bathtub and modified bathtub hazard rate called Modified Burr XII-Inverse Weibull (MBXII-IW) is introduced and studied. The density function of MBXII-IW is exponential, left-skewed, right-skewed and symmetrical shaped.  Descriptive measures on the basis of quantiles, moments, order statistics and reliability measures are theoretically established. The MBXII-IW distribution is characterized via different techniques. Parameters of MBXII-IW distribution are estimated using maximum likelihood method. The simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). The potentiality of MBXII-IW distribution is demonstrated by its application to real data sets: serum-reversal times and quarterly earnings.


Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 440 ◽  
Author(s):  
Abdulhakim A. Al-babtain ◽  
I. Elbatal ◽  
Haitham M. Yousof

In this article, we introduced a new extension of the binomial-exponential 2 distribution. We discussed some of its structural mathematical properties. A simple type Copula-based construction is also presented to construct the bivariate- and multivariate-type distributions. We estimated the model parameters via the maximum likelihood method. Finally, we illustrated the importance of the new model by the study of two real data applications to show the flexibility and potentiality of the new model in modeling skewed and symmetric data sets.


1986 ◽  
Vol 16 (1) ◽  
pp. 63-68 ◽  
Author(s):  
B. Ajne

AbstractThree methods for fitting multiplicative models to observed, cross-classified risk data are compared. They are the method of Bailey–Simon, the method of marginal totals and a maximum likelihood method. The methods are applied to a number of risk data sets and compared with respect to balance and goodness-of-fit.


Author(s):  
Jamilu Yunusa Falgore ◽  
Sani Ibrahim Doguwa

A new generator of continuous distributions called the Inverse Lomax-Exponentiated G family, which has three extra positive parameters is proposed. The structural properties of the new family that holds for any continuous baseline model including explicit density function expressions, moments, inequality measurements, moment generating function, reliability functions, Renyi and Shanon entropies, and distribution of order statistics are derived. A Monte Carlo simulation to test the efficiency of the maximum likelihood estimates is conducted. The application of the new sub-model to the two data sets using the maximum likelihood method indicates that the new model is better than the existing competitors.


2020 ◽  
Vol 9 (5) ◽  
pp. 179-184
Author(s):  
Kamlesh Kumar Shukla

In this paper, Truncated Akash distribution has been proposed. Its mean and variance have been derived. Nature of cumulative distribution and hazard rate functions have been derived and presented graphically. Its moments including Coefficient of Variation, Skenwness, Kurtosis and Index of dispersion have been derived. Maximum likelihood method of estimation has been used to estimate the parameter of proposed model. It has been applied on three data sets and compares its superiority over one parameter exponential, Lindley, Akash, Ishita and truncated Lindley distribution.


Author(s):  
Muhammad Aslam ◽  
Zawar Hussain ◽  
Zahid Asghar

In this article, we propose a new family of distributions using the T-X family named as modified generalized Marshall-Olkin family of distributions. Comprehensive mathematical and statistical properties of this family of distributions are provided. The model parameters are estimated by maximum likelihood method. The maximum likelihood estimation under Type-II censoring is also discussed. Two lifetime data sets are used to show the suitability and applicability of the new family of distributions. For comparison purposes, different goodness of fit tests are used.  


2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876737 ◽  
Author(s):  
Junhua Yang ◽  
Yong Li ◽  
Wei Cheng

Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration localization method based on greedy algorithm in order to use all of the effective anchor points. Comparing to trilateration, fingerprint, and maximum-likelihood method, the bilateral greed iteration method improves the localization accuracy and reduces complexity of localization process. The method proposed, coupled with measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms trilateration and maximum-likelihood receive signal strength indicator–based indoor location methods without using any radio map information nor a complicated algorithm. Extensive experiment results in a Wi-Fi coverage office environment indicate that the proposed bilateral greed iteration method reduces the localization error, 63.55%, 9.93%, and 47.85%, compared to trilateration, fingerprint, and maximum-likelihood method, respectively.


Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1231
Author(s):  
Guillermo Martínez-Flórez ◽  
Roger Tovar-Falón

In this paper, two new distributions were introduced to model unimodal and/or bimodal data. The first distribution, which was obtained by applying a simple transformation to a unit-Birnbaum–Saunders random variable, is useful for modeling data with positive support, while the second is appropriate for fitting data on the (0,1) interval. Extensions to regression models were also studied in this work, and statistical inference was performed from a classical perspective by using the maximum likelihood method. A small simulation study is presented to evaluate the benefits of the maximum likelihood estimates of the parameters. Finally, two applications to real data sets are reported to illustrate the developed methodology.


2021 ◽  
Vol 9 (4) ◽  
pp. 942-962
Author(s):  
Mohamed Abo Raya

This work introduces a new one-parameter compound G family. Relevant statistical properties are derived. The new density can be “asymmetric right skewed with one peak and a heavy tail”, “symmetric” and “left skewedwith one peak”. The new hazard function can be “upside-down”, “upside-down-constant”, “increasing”, “decreasing” and “decreasing-constant”. Many bivariate types have been also derived via different common copulas. The estimation of the model parameters is performed by maximum likelihood method. The usefulness and flexibility of the new family is illustrated by means of two real data sets.


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