A new seismic ray-tracing method is put forward based on parabolic travel-time interpolation(PTI) method, which is more accurate than the linear travel-time interpolation (LTI) method. Both PTI method and LTI method a...A new seismic ray-tracing method is put forward based on parabolic travel-time interpolation(PTI) method, which is more accurate than the linear travel-time interpolation (LTI) method. Both PTI method and LTI method are used to compute seismic travel-time and ray-path in a 2-D grid cell model. Firstly, some basic concepts are introduced. The calculations of travel-time and ray-path are carried out only at cell boundaries. So, the ray-path is always straight in the same cells with uniform velocity. Two steps are applied in PTI and LTI method, step 1 computes travel-time and step 2 traces ray-path. Then, the derivation of LTI formulas is described. Because of the presence of refraction wave in shot cell, the formula aiming at shot cell is also derived. Finally, PTI method is presented. The calculation of PTI method is more complex than that of LTI method, but the error is limited. The results of numerical model show that PTI method can trace ray-path more accurately and efficiently than LTI method does.展开更多
Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed...Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.展开更多
In this paper,we derived the relationships between the travel time difference of sPn and Pn and the local earthquake focal depth.In these equations,the travel time difference of sPn and Pn is not related to the epicen...In this paper,we derived the relationships between the travel time difference of sPn and Pn and the local earthquake focal depth.In these equations,the travel time difference of sPn and Pn is not related to the epicentral distance,but depends only on the regional crustal mode and the focal depth.According to the equations,we provided a simple and accurate method to determine local earthquake focal depth by using the travel time difference between phase sPn and Pn.This method has been used to determine the focal depths of two earthquake of MS6.1 and MS5.6 which occurred at the junction of Panzhihua and Huili,Sichuan on August 30 and 31,2008.The results were compared to those from other sources such as the China Earthquake Networks Center,and the comparison shows that the results are accurate and reliable.展开更多
Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. ...Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.展开更多
文摘A new seismic ray-tracing method is put forward based on parabolic travel-time interpolation(PTI) method, which is more accurate than the linear travel-time interpolation (LTI) method. Both PTI method and LTI method are used to compute seismic travel-time and ray-path in a 2-D grid cell model. Firstly, some basic concepts are introduced. The calculations of travel-time and ray-path are carried out only at cell boundaries. So, the ray-path is always straight in the same cells with uniform velocity. Two steps are applied in PTI and LTI method, step 1 computes travel-time and step 2 traces ray-path. Then, the derivation of LTI formulas is described. Because of the presence of refraction wave in shot cell, the formula aiming at shot cell is also derived. Finally, PTI method is presented. The calculation of PTI method is more complex than that of LTI method, but the error is limited. The results of numerical model show that PTI method can trace ray-path more accurately and efficiently than LTI method does.
文摘Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.
基金funded by the special support projectentitled "Sorting out and processing of seismic data " of central public-interest basic scientific and technological research of Institute of Crustal DynamicsChina Earthquake Administration (ZDJ2007-4)
文摘In this paper,we derived the relationships between the travel time difference of sPn and Pn and the local earthquake focal depth.In these equations,the travel time difference of sPn and Pn is not related to the epicentral distance,but depends only on the regional crustal mode and the focal depth.According to the equations,we provided a simple and accurate method to determine local earthquake focal depth by using the travel time difference between phase sPn and Pn.This method has been used to determine the focal depths of two earthquake of MS6.1 and MS5.6 which occurred at the junction of Panzhihua and Huili,Sichuan on August 30 and 31,2008.The results were compared to those from other sources such as the China Earthquake Networks Center,and the comparison shows that the results are accurate and reliable.
基金supported by the National Natural Science Foundation of China (Grant Nos.40334040 and 40974033)the Promoting Foundation for Advanced Persons of Talent of NCWU
文摘Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.
