Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known bef...Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.展开更多
The vertical ionogram can provide the important ionospheric parameters, such as critical frequency, virtual height and electron density, for ionospheric research. The oblique ionosonde has the ability to detect the io...The vertical ionogram can provide the important ionospheric parameters, such as critical frequency, virtual height and electron density, for ionospheric research. The oblique ionosonde has the ability to detect the ionosphere over sea and other terrain where it is not practical to deploy vertical sounder and provide more ionograms with less transmitting and receiving devices. Therefore, the conversion of the oblique ionogram to vertical ionogram for obtaining the important ionospheric parameters is a very useful inversion technology. The experimental comparison between oblique and vertical detections was carried out in the equatorial ionospheric anomaly (EIA) region of south China on 25 and 26 August 2010. The oblique detecting path was from Wuhan to Shenzhen and the VI ionosonde was located in the midpoint of the oblique path. The oblique ionogram reversion results showed a small deviation of the critical frequency, minimum virtual height as well as the electron density profile of the ionospheric F layer, as compared with the real vertical observations.展开更多
Since S-wave velocity of the subsurface is an important parameter in near surface applications,many studies have been conducted for its estimation.Among the various methods that use surface waves or body waves,Rayleig...Since S-wave velocity of the subsurface is an important parameter in near surface applications,many studies have been conducted for its estimation.Among the various methods that use surface waves or body waves,Rayleigh wave inversion is the most popular.In practice,the densities and P-wave velocities of different layers are usually assumed to be known to avoid ill-posed problems,as they have less influence on the dispersion curves.However,improper assignment of these two groups of parameters leads to inaccurate estimation of the S-wave velocity profile.In order to address this problem,the all-parameters Rayleigh wave inversion strategy is proposed in which the S-wave velocities,layer thicknesses,densities and P-wave velocities of different layers are included as the unknown parameters for inversion.Meanwhile,the transitional Markov Chain Monte Carlo(TMCMC)algorithm is applied for the implementation of all-parameters Rayleigh wave inversion.One simulated example and two real-test applications are demonstrated to verify the capability of the proposed method in the estimation of the S-wave velocity profile,the densities and the P-wave velocities.Furthermore,it is verified that the proposed method achieved more accurate S-wave velocity profile estimation than the traditional approach.展开更多
基金provided through research grant No.0035/2019/A1 from the Science and Technology Development Fund,Macao SARthe assistantship from the Faculty of Science and Technology,University of Macao。
文摘Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.
基金supported by the National Natural Science Foundation of China (Grant Nos. 40804042 and 41074115)the Post Doctor Foundation of China (Grant No. 200902445)the Fundamental Research Funds for the Central Universities (Grant No. 4081004)
文摘The vertical ionogram can provide the important ionospheric parameters, such as critical frequency, virtual height and electron density, for ionospheric research. The oblique ionosonde has the ability to detect the ionosphere over sea and other terrain where it is not practical to deploy vertical sounder and provide more ionograms with less transmitting and receiving devices. Therefore, the conversion of the oblique ionogram to vertical ionogram for obtaining the important ionospheric parameters is a very useful inversion technology. The experimental comparison between oblique and vertical detections was carried out in the equatorial ionospheric anomaly (EIA) region of south China on 25 and 26 August 2010. The oblique detecting path was from Wuhan to Shenzhen and the VI ionosonde was located in the midpoint of the oblique path. The oblique ionogram reversion results showed a small deviation of the critical frequency, minimum virtual height as well as the electron density profile of the ionospheric F layer, as compared with the real vertical observations.
基金University of Macao(File No.MYRG2018-00048-AAO)the Science and Technology Development Fund,Macao SAR(File No.SKL-IOTSC-2018-2020)。
文摘Since S-wave velocity of the subsurface is an important parameter in near surface applications,many studies have been conducted for its estimation.Among the various methods that use surface waves or body waves,Rayleigh wave inversion is the most popular.In practice,the densities and P-wave velocities of different layers are usually assumed to be known to avoid ill-posed problems,as they have less influence on the dispersion curves.However,improper assignment of these two groups of parameters leads to inaccurate estimation of the S-wave velocity profile.In order to address this problem,the all-parameters Rayleigh wave inversion strategy is proposed in which the S-wave velocities,layer thicknesses,densities and P-wave velocities of different layers are included as the unknown parameters for inversion.Meanwhile,the transitional Markov Chain Monte Carlo(TMCMC)algorithm is applied for the implementation of all-parameters Rayleigh wave inversion.One simulated example and two real-test applications are demonstrated to verify the capability of the proposed method in the estimation of the S-wave velocity profile,the densities and the P-wave velocities.Furthermore,it is verified that the proposed method achieved more accurate S-wave velocity profile estimation than the traditional approach.
