Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates havi...Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.展开更多
Marine seismic reflection surveys are often masked by strong water-bottom multiples that limit the use of data beyond the first multiple waves. In this study, we have successfully suppressed much of the multiple artif...Marine seismic reflection surveys are often masked by strong water-bottom multiples that limit the use of data beyond the first multiple waves. In this study, we have successfully suppressed much of the multiple artifacts in the depth images of two of the marine seismic reflection profiles from the Los Angeles regional seismic experiment （LARSE） by applying reverse time migration （RTM）. In contrast to most seismic reflection methods that use only primary reflections and diffractions, the two-way RTM migrates both primaries and multiple reflections to their places of origination： seabed multiples to the sea bottom and primaries to the reflecting interfaces. Based on the RTM depth sections of LARSE lines 1 and 2, we recognize five stratigraphic units from the sea bottom to a depth of 6 km. These units are Pliocene and younger strata, probably Miocene syntectonic strata, two deeper sequences of unknown age and lithology as well as Miocene volcanic layers on Catalina ridge. Several inferred igneous intrusions in the upper crust comprise a sixth unit. The existence of a thick sedimentary section in the Catalina Basin, which might include Paleogene and Cretaceous fore-arc strata, has important geologic significance. If borne out by further studies, significant revisions of current structural and stratigraphic interpretations of the California borderland would be warranted.展开更多
文摘Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.
基金partially supported by the National Natural Science Foundation of China (Nos.41230318 and 41304109)
文摘Marine seismic reflection surveys are often masked by strong water-bottom multiples that limit the use of data beyond the first multiple waves. In this study, we have successfully suppressed much of the multiple artifacts in the depth images of two of the marine seismic reflection profiles from the Los Angeles regional seismic experiment （LARSE） by applying reverse time migration （RTM）. In contrast to most seismic reflection methods that use only primary reflections and diffractions, the two-way RTM migrates both primaries and multiple reflections to their places of origination： seabed multiples to the sea bottom and primaries to the reflecting interfaces. Based on the RTM depth sections of LARSE lines 1 and 2, we recognize five stratigraphic units from the sea bottom to a depth of 6 km. These units are Pliocene and younger strata, probably Miocene syntectonic strata, two deeper sequences of unknown age and lithology as well as Miocene volcanic layers on Catalina ridge. Several inferred igneous intrusions in the upper crust comprise a sixth unit. The existence of a thick sedimentary section in the Catalina Basin, which might include Paleogene and Cretaceous fore-arc strata, has important geologic significance. If borne out by further studies, significant revisions of current structural and stratigraphic interpretations of the California borderland would be warranted.
