Assessment of seismic ambient noise parameter estimation and wavefield decomposition in WaveDec

2021 ◽  
Author(s):  
Mehrdad Fotouhimehr ◽  
Elham Shabani
Keyword(s):  
Model Selection ◽  
Surface Waves ◽  
Rayleigh Waves ◽  
Selection Criteria ◽  
Ambient Noise ◽  
Information Criterion ◽  
Optimization Methods ◽  
Seismic Ambient Noise ◽  
Ellipticity Angle ◽  
Truncated Newton

<p>Knowledge about seismic ambient noise wavefield through decomposition into different participant waves is of special importance in geophysical studies. In this study, WaveDec technique (Maranò et al., 2012) as an array statistical signal processing technique was used to decompose seismic ambient noise wavefield and to estimate wavefield parameters. In this method, the measurements from all components of stations and parameters of interest are modeled jointly which leads to significant improvement in extracting characteristics of surface waves. Considering the contribution of both Love and Rayleigh waves in the wavefield, the method estimates the desired parameters including amplitude, phase, azimuth, wave number and the ellipticity angle (for the Rayleigh wave only) based on the Maximum Likelihood Estimation method. One of the main characteristic of WaveDec is estimating the ellipticity angle of Rayleigh waves. This is very beneficial in determining retrograde and prograde particle motion and also in mode distinction.</p><p>In the WaveDec algorithm, the Truncated Newton method is used to optimize likelihood functions with respect to wavefield parameters. Furthermore, Bayesian Information Criterion (BIC) is used to select the best model and wave type determination (Rayleigh, Love, body wave or noise). Regarding a group of generated models for different wave types, the one with the smallest BIC is chosen.</p><p>We examined consistency of WaveDec algorithm by applying different numerical optimization methods; Truncated Newton, L-BFGS-B quasi-Newton and simplex-based Nelder-Mead methods. Furthermore, different model selection criteria; BIC, Akaike Information Criterion (AIC) and Hannan–Quinn Information Criterion (HQC) were examined to study the quality of generated models. They possess different penalty terms to avoid overfitting the models on data. All possible pairs of optimization methods and model selection criteria were utilized and replaced in WaveDec algorithm. In order to compare the resultant dispersion curves of surface waves and ellipticity angle curves of Rayleigh waves, SESAME model M2.1 synthetic data and some seismic ambient noise measurements in Colfiorito basin in Italy (Array B) were analyzed.</p>

Simultaneous body and surface wave retrieval from the seismic ambient field and discrimination from unavoidably arising spurious artifacts

2020 ◽  
Author(s):  
Ali Riahi ◽  
Zaher-Hossein Shomali ◽  
Anne Obermann ◽  
Ahmad Kamayestani
Keyword(s):  
Surface Waves ◽  
Rayleigh Waves ◽  
Ambient Noise ◽  
Low Frequency ◽  
The Body ◽  
Body Waves ◽  
Apparent Velocity ◽  
Noise Sources ◽  
P Waves ◽  
Seismic Ambient Noise

<p>We simultaneously extract both, direct P-waves and Rayleigh waves, from the seismic ambient noise field recorded by a dense seismic network in Iran. With synthetics, we show that the simultaneous retrieval of body and surface waves from seismic ambient noise leads to the unavoidable appearance of spurious arrivals that could lead to misinterpretations.</p><p>We work with 2 months of seismic ambient noise records from a dense deployment of 119 sensors with interstation distances of 2 km in Iran. To retrieve body and surface waves, we calculate the cross-coherency in low-frequency ranges, i.e. frequencies up to 1.2 Hz, to provide the empirical Green’s functions between each pair of stations. To separate the P and Rayleigh waves, we use the polarization method that also enhances the small amplitude body waves.</p><p>We observe both P and Rayleigh waves with an apparent velocity of 4.9±0.3 and 1.8±0.1 km/s in the studied area, respectively, as well as S or higher mode of Rayleigh waves, with an apparent velocity of 4.1±0.1 km/s. Besides these physical arrivals, we also observe two spurious arrivals with similar amplitudes before/after the P and/or Rayleigh waves that render the discrimination challenging.</p><p>To better understanding these arrivals, we perform synthetic tests. We show that simultaneously retrieving the body and surface waves from seismic ambient noise sources will unavoidably lead to the appearance of superior arrivals in the calculation of empirical Green’s functions.</p>

scholarly journals Using Model Selection Criteria to Choose the Number of Principal Components

2021 ◽  
Vol 20 (3) ◽  
pp. 450-461
Author(s):  
Stanley L. Sclove
Keyword(s):  
Model Selection ◽  
Principal Components ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Information Criteria ◽  
Akaike's Information Criterion ◽  
Model Selection Criteria ◽  
Adequate Number ◽  
Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

A Comparative Analysis and Evaluation of Model Selection Criteria

2017 ◽  
Author(s):  
Ahmed H. Kamel ◽  
Ali S. Shaqlaih ◽  
Arslan Rozyyev
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Selection Criteria ◽  
Oil And Gas ◽  
Information Criterion ◽  
Oil And Gas Industry ◽  
Absolute Error ◽  
Ongoing Research ◽  
Model Selection Criteria ◽  
Innovative Strategy

The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction. The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental data are employed. The results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but does not give much insight on their accuracy. Other criteria such as correlation ratio, mean absolute error, and standard deviation are also evaluated. The Akaike information criterion, AIC has shown its superiority to other selection criteria. The authors propose AIC as an alternative to use when fitting experimental data or evaluating existing correlations. Indeed, the AIC method is an information theory based, theoretically sound and stable. The paper presents a detailed discussion of the model selection criteria, their pros and cons, and how they can be utilized to allow proper comparison of different models for the best model to be inferred based on sound mathematical theory. In conclusion, model selection is an interesting problem and an innovative strategy to help alleviate similar challenges faced by the professionals in the oil and gas industry is introduced.

scholarly journals Improved Treatment of the Independent Variables for the Deployment of Model Selection Criteria in the Analysis of Complex Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1202
Author(s):  
Luca Spolladore ◽  
Michela Gelfusa ◽  
Riccardo Rossi ◽  
Andrea Murari
Keyword(s):  
Model Selection ◽  
Complex Systems ◽  
Selection Criteria ◽  
A Priori ◽  
Synthetic Data ◽  
Information Criterion ◽  
Model Selection Criteria ◽  
Dependent Variables ◽  
Chi Squared ◽  
Highly Correlated

Model selection criteria are widely used to identify the model that best represents the data among a set of potential candidates. Amidst the different model selection criteria, the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are the most popular and better understood. In the derivation of these indicators, it was assumed that the model’s dependent variables have already been properly identified and that the entries are not affected by significant uncertainties. These are issues that can become quite serious when investigating complex systems, especially when variables are highly correlated and the measurement uncertainties associated with them are not negligible. More sophisticated versions of this criteria, capable of better detecting spurious relations between variables when non-negligible noise is present, are proposed in this paper. Their derivation is obtained starting from a Bayesian statistics framework and adding an a priori Chi-squared probability distribution function of the model, dependent on a specifically defined information theoretic quantity that takes into account the redundancy between the dependent variables. The performances of the proposed versions of these criteria are assessed through a series of systematic simulations, using synthetic data for various classes of functions and noise levels. The results show that the upgraded formulation of the criteria clearly outperforms the traditional ones in most of the cases reported.

scholarly journals On the Use of Entropy to Improve Model Selection Criteria

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 394 ◽  
Author(s):  
Andrea Murari ◽  
Emmanuele Peluso ◽  
Francesco Cianfrani ◽  
Pasquale Gaudio ◽  
Michele Lungaroni
Keyword(s):  
Model Selection ◽  
Shannon Entropy ◽  
Selection Criteria ◽  
Mean Squared Error ◽  
Geodesic Distance ◽  
Information Criterion ◽  
Residual Distribution ◽  
Model Selection Criteria ◽  
Synthetic Indicators ◽  
Squared Error

The most widely used forms of model selection criteria, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), are expressed in terms of synthetic indicators of the residual distribution: the variance and the mean-squared error of the residuals respectively. In many applications in science, the noise affecting the data can be expected to have a Gaussian distribution. Therefore, at the same level of variance and mean-squared error, models, whose residuals are more uniformly distributed, should be favoured. The degree of uniformity of the residuals can be quantified by the Shannon entropy. Including the Shannon entropy in the BIC and AIC expressions improves significantly these criteria. The better performances have been demonstrated empirically with a series of simulations for various classes of functions and for different levels and statistics of the noise. In presence of outliers, a better treatment of the errors, using the Geodesic Distance, has proved essential.

scholarly journals Latent profile analysis of human values: What is the optimal number of clusters?

Methodology ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 127-148
Author(s):  
Mikkel N. Schmidt ◽  
Daniel Seddig ◽  
Eldad Davidov ◽  
Morten Mørup ◽  
Kristoffer Jon Albers ◽  
...  
Keyword(s):  
Model Selection ◽  
Selection Criteria ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Information Criterion ◽  
Optimal Number ◽  
Number Of Clusters ◽  
Model Selection Criteria ◽  
Optimal Number Of Clusters ◽  
Latent Profile

Latent Profile Analysis (LPA) is a method to extract homogeneous clusters characterized by a common response profile. Previous works employing LPA to human value segmentation tend to select a small number of moderately homogeneous clusters based on model selection criteria such as Akaike information criterion, Bayesian information criterion and Entropy. The question is whether a small number of clusters is all that can be gleaned from the data. While some studies have carefully compared different statistical model selection criteria, there is currently no established criteria to assess if an increased number of clusters generates meaningful theoretical insights. This article examines the content and meaningfulness of the clusters extracted using two algorithms: Variational Bayesian LPA and Maximum Likelihood LPA. For both methods, our results point towards eight as the optimal number of clusters for characterizing distinctive Schwartz value typologies that generate meaningful insights and predict several external variables.

Application of statistical model selection criteria to the Stock Synthesis assessment program

2000 ◽  
Vol 57 (9) ◽  
pp. 1784-1793 ◽  
Author(s):  
S Langitoto Helu ◽  
David B Sampson ◽  
Yanshui Yin
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Model Selection Criteria ◽  
Assessment Program ◽  
Correct Model ◽  
Complex Models ◽  
Multiple Data Sets

Statistical modeling involves building sufficiently complex models to represent the system being investigated. Overly complex models lead to imprecise parameter estimates, increase the subjective role of the modeler, and can distort the perceived characteristics of the system under investigation. One approach for controlling the tendency to increased complexity and subjectivity is to use model selection criteria that account for these factors. The effectiveness of two selection criteria was tested in an application with the stock assessment program known as Stock Synthesis. This program, which is often used on the U.S. west coast to assess the status of exploited marine fish stocks, can handle multiple data sets and mimic highly complex population dynamics. The Akaike information criterion and Schwarz's Bayesian information criterion are criteria that satisfy the fundamental principles of model selection: goodness-of-fit, parsimony, and objectivity. Their ability to select the correct model form and produce accurate estimates was evaluated in Monte Carlo experiments with the Stock Synthesis program. In general, the Akaike information criterion and the Bayesian information criterion had similar performance in selecting the correct model, and they produced comparable levels of accuracy in their estimates of ending stock biomass.

scholarly journals Monitoring of fields using body and surface waves reconstructed from passive seismic ambient noise

2019 ◽  
Author(s):  
Florent Brenguier ◽  
Aurélien Mordret ◽  
Richard Lynch ◽  
Roméo Courbis ◽  
Xander Campbell ◽  
...  
Keyword(s):  
Surface Waves ◽  
Ambient Noise ◽  
Seismic Ambient Noise ◽  
Passive Seismic

scholarly journals Performance comparison of model selection criteria by generated experimental data

2018 ◽  
Vol 16 ◽  
pp. 02006
Author(s):  
Radoslav Mavrevski ◽  
Peter Milanov ◽  
Metodi Traykov ◽  
Nevena Pencheva
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Mathematical Models ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Data Fitting ◽  
Performance Comparison ◽  
Artificial Data ◽  
Criteria For Evaluation

In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC.

scholarly journals Monitoring of fields using body and surface waves reconstructed from passive seismic ambient noise

2019 ◽  
Author(s):  
Florent Brenguier ◽  
Aurélien Mordret ◽  
Richard Lynch ◽  
Roméo Courbis ◽  
Xander Campbell ◽  
...  
Keyword(s):  
Surface Waves ◽  
Ambient Noise ◽  
Seismic Ambient Noise ◽  
Passive Seismic
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