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H Chauris  One of the best experts on this subject based on the ideXlab platform.

two dimensional velocity macro model estimation from seismic reflection Data by local differential semblance optimization applications to synthetic and Real Data Sets
Geophysical Journal International, 2001CoAuthors: H Chauris, And M NobleAbstract:SUMMARY The quality of the migration/inversion in seismic reflection is directly related to the quality of the velocity macro model. We present here an extension of the differential semblance optimization method (DSO) for 2D velocity field estimation. DSO evaluates via local measurements (horizontal derivatives) how flat events in commonimage gathers are. Its major advantage with respect to the usual cost functions used in reflection seismic inverse problems is that it is—at least in the 1D case—unimodal and thus allows a local (gradient) optimization process. Extension of DSO to three dimensions in Real cases involving a large number of inverted parameters thus appears much more feasible, because convergence might not require a random search process (global optimization). Our differential semblance function directly measures the quality of the commonimage gathers in the depthmigrated domain and does not involve demigration. An example of inversion on a 2D synthetic Data set shows the ability of DSO to handle 2D media with local optimization algorithms. The horizontal derivatives have to be carefully calculated for the inversion process. However, the computation of only a few commonimage gathers is sufficient for a stable inversion. As a Kirchhoff scheme is used for migration, this undersampling largely reduces the computational cost. Finally, we present an application to a Real North Sea marine Data set. We prove with this example that DSO can provide velocity models for typical 2D acquisition that improve the quality of the final prestack depth images when compared to the quality of images migrated with a velocity model obtained by a classical NMO/DMO analysis. Whilst random noise is not a Real difficulty for DSO, coherent noise, however, has to be carefully eliminated before or during inversion for the success of the velocity estimation.

two dimensional velocity macro model estimation from seismic reflection Data by local differential semblance optimization applications to synthetic and Real Data Sets
Geophysical Journal International, 2001CoAuthors: H Chauris, Mark NobleAbstract:SUMMARY The quality of the migration/inversion in seismic reflection is directly related to the quality of the velocity macro model. We present here an extension of the differential semblance optimization method (DSO) for 2D velocity field estimation. DSO evaluates via local measurements (horizontal derivatives) how flat events in commonimage gathers are. Its major advantage with respect to the usual cost functions used in reflection seismic inverse problems is that it is—at least in the 1D case—unimodal and thus allows a local (gradient) optimization process. Extension of DSO to three dimensions in Real cases involving a large number of inverted parameters thus appears much more feasible, because convergence might not require a random search process (global optimization). Our differential semblance function directly measures the quality of the commonimage gathers in the depthmigrated domain and does not involve demigration. An example of inversion on a 2D synthetic Data set shows the ability of DSO to handle 2D media with local optimization algorithms. The horizontal derivatives have to be carefully calculated for the inversion process. However, the computation of only a few commonimage gathers is sufficient for a stable inversion. As a Kirchhoff scheme is used for migration, this undersampling largely reduces the computational cost. Finally, we present an application to a Real North Sea marine Data set. We prove with this example that DSO can provide velocity models for typical 2D acquisition that improve the quality of the final prestack depth images when compared to the quality of images migrated with a velocity model obtained by a classical NMO/DMO analysis. Whilst random noise is not a Real difficulty for DSO, coherent noise, however, has to be carefully eliminated before or during inversion for the success of the velocity estimation.
Mark Noble  One of the best experts on this subject based on the ideXlab platform.

two dimensional velocity macro model estimation from seismic reflection Data by local differential semblance optimization applications to synthetic and Real Data Sets
Geophysical Journal International, 2001CoAuthors: H Chauris, Mark NobleAbstract:SUMMARY The quality of the migration/inversion in seismic reflection is directly related to the quality of the velocity macro model. We present here an extension of the differential semblance optimization method (DSO) for 2D velocity field estimation. DSO evaluates via local measurements (horizontal derivatives) how flat events in commonimage gathers are. Its major advantage with respect to the usual cost functions used in reflection seismic inverse problems is that it is—at least in the 1D case—unimodal and thus allows a local (gradient) optimization process. Extension of DSO to three dimensions in Real cases involving a large number of inverted parameters thus appears much more feasible, because convergence might not require a random search process (global optimization). Our differential semblance function directly measures the quality of the commonimage gathers in the depthmigrated domain and does not involve demigration. An example of inversion on a 2D synthetic Data set shows the ability of DSO to handle 2D media with local optimization algorithms. The horizontal derivatives have to be carefully calculated for the inversion process. However, the computation of only a few commonimage gathers is sufficient for a stable inversion. As a Kirchhoff scheme is used for migration, this undersampling largely reduces the computational cost. Finally, we present an application to a Real North Sea marine Data set. We prove with this example that DSO can provide velocity models for typical 2D acquisition that improve the quality of the final prestack depth images when compared to the quality of images migrated with a velocity model obtained by a classical NMO/DMO analysis. Whilst random noise is not a Real difficulty for DSO, coherent noise, however, has to be carefully eliminated before or during inversion for the success of the velocity estimation.
And M Noble  One of the best experts on this subject based on the ideXlab platform.

two dimensional velocity macro model estimation from seismic reflection Data by local differential semblance optimization applications to synthetic and Real Data Sets
Geophysical Journal International, 2001CoAuthors: H Chauris, And M NobleAbstract:SUMMARY The quality of the migration/inversion in seismic reflection is directly related to the quality of the velocity macro model. We present here an extension of the differential semblance optimization method (DSO) for 2D velocity field estimation. DSO evaluates via local measurements (horizontal derivatives) how flat events in commonimage gathers are. Its major advantage with respect to the usual cost functions used in reflection seismic inverse problems is that it is—at least in the 1D case—unimodal and thus allows a local (gradient) optimization process. Extension of DSO to three dimensions in Real cases involving a large number of inverted parameters thus appears much more feasible, because convergence might not require a random search process (global optimization). Our differential semblance function directly measures the quality of the commonimage gathers in the depthmigrated domain and does not involve demigration. An example of inversion on a 2D synthetic Data set shows the ability of DSO to handle 2D media with local optimization algorithms. The horizontal derivatives have to be carefully calculated for the inversion process. However, the computation of only a few commonimage gathers is sufficient for a stable inversion. As a Kirchhoff scheme is used for migration, this undersampling largely reduces the computational cost. Finally, we present an application to a Real North Sea marine Data set. We prove with this example that DSO can provide velocity models for typical 2D acquisition that improve the quality of the final prestack depth images when compared to the quality of images migrated with a velocity model obtained by a classical NMO/DMO analysis. Whilst random noise is not a Real difficulty for DSO, coherent noise, however, has to be carefully eliminated before or during inversion for the success of the velocity estimation.
Petros Daras  One of the best experts on this subject based on the ideXlab platform.

An Improved Tobit Kalman Filter with Adaptive Censoring Limits
Circuits Systems and Signal Processing, 2020CoAuthors: Kostas Loumponias, Nicholas Vretos, George Tsaklidis, Petros DarasAbstract:This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman filter residual. The designed algorithm is tested using both synthetic and Real Data Sets. The Real Data set includes human skeleton joints’ coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain Reallife situations that cause problems in human skeleton tracking, such as (self)occlusions, closely interacting persons, etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall rootmeansquare error for synthetic and Real Data Sets.

An Improved Tobit Kalman Filter with Adaptive Censoring Limits
arXiv: Signal Processing, 2019CoAuthors: Kostas Loumponias, Nicholas Vretos, George Tsaklidis, Petros DarasAbstract:This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman residual. The designed algorithm is tested using both synthetic and Real Data Sets. The Real Data set includes human skeleton joints' coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain Reallife situations that cause problems in human skeleton tracking, such as (self)occlusions, closely interacting persons etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall Root Mean Square Error (RMSE) for synthetic and Real Data Sets.
F. Piccinini  One of the best experts on this subject based on the ideXlab platform.

Unsupervised spatial pattern classification of electricalwafersorting maps in semiconductor manufacturing
Pattern Recognition Letters, 2005CoAuthors: F. Di Palma, G. De Nicolao, G. Miraglia, E. Pasquinetti, F. PiccininiAbstract:In semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In the literature, Kohonen's Self Organizing Feature Maps (SOM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the Real Data Sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOM as wafer classifiers is reassessed on much more extensive simulated and Real Data Sets. We conclude that ART1 is not adequate, whereas SOM provide completely satisfactory results including visually effective representation of spatial failure probability of the pattern classes.

Unsupervised Spatial Pattern Classification of Electrical Failures in Semiconductor Manufacturing
2003CoAuthors: G. De Nicolao, G. Miraglia, E. Pasquinetti, F. PiccininiAbstract:In semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In particular, the use of unsupervised learning is a promising strategy towards the development of fully automated classification tools. In the literature, Kohonen’s Self Organizing Feature Maps (SOFM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the Real Data Sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOFM as wafer classifiers is reassessed on much more extensive simulated and Real Data Sets. We conclude that ART1 is not adequate, whereas, SOFM provide completely satisfactory results including visually effective representations of the spatial failure probabilities of the pattern classes.