S U M M A R YIn this paper, we start with the implementation of a data space conjugate gradient (DCG) method for 3-D magnetotelluric (MT) data. This code will be referred to as WSDCG3DMT. It is an extension of the 2-D method previously developed. Several experiments on both synthetic and real data sets show that WSDCG3DMT usually needs more computational time than the data space Occam's inversion (OCCAM) for which the corresponding code is referred to as WSINV3DMT. However, the memory requirement of WSDCG3DMT is only a fraction of that of WSINV3DMT. Based on the knowledge gained from several studies of both codes, we have created a new hybrid scheme called the DCG Occam's inversion (DCGOCC) and the corresponding code, WSDCGOCC3DMT, from combining aspects of the OCCAM and DCG methods. As with OCCAM, the DCGOCC method divides the inversion into two phases. In Phase I the misfit is brought down to a desired level. In Phase II unnecessary structures are smoothed out. Because its mathematical basis is of a similar form to that of DCG, its memory requirement is similarly low but more stable. However, DCGOCC is significantly faster than both methods. We demonstrate the computational performances with comparisons of all three methods with both synthetic and EXTECH field data sets.

No abstract

No abstract

No abstract

No abstract

In this research, a new numerical method, called the hybrid finite difference-finite element (hybrid FD-FE) method, is developed to solve 2-D magnetotelluric modeling by taking advantage of both the finite difference (FD) and finite element (FE) methods. With the hybrid FD-FE method, the model is first discretized as rectangular blocks and separated into two zones: the FD and FE zones. The FD zone is set for the subregions where topography or bathymetry does not appear. The FD approximation, which is fast, accurate and requires less memory resources, is then applied. For the FE zones where topography or bathymetry exists, the rectangular blocks are transformed into quadrilateral elements to handle the topography or bathymetry appropriately. Then, the FE approximation with quadrilateral elements, which is more accurate for topography or bathymetry zones, is applied. The system of equations for the hybrid FD-FE method is then formed according to the FD and FE schemes. The obtained system is a combination of the FD and FE equations. Three numerical methods are applied to test models with and without topography and bathymetry. The accuracy and efficiency in terms of errors, computational time and memory storage are presented, compared and discussed. The numerical experiments indicate that the FD scheme has a shorter computational time than the other schemes when modeling without topography and retains accuracy equivalent to that of the FE method, whereas FE is more practical when modeling with topography and bathymetry. However, our proposed hybrid FD-FE method is efficient in both situations. Without topography or bathymetry, its efficiency and accuracy approach those of the FD scheme. With topography and bathymetry, the hybrid FD-FE method is as accurate as FE, but its speed is slightly slower than that of FD. In terms of memory storage, the hybrid FD-FE method consumes slightly more storage than the FD method. This hybrid FD-FE method can be further extended and implemented for 3-D magnetotelluric modeling for more efficient computation.

No abstract

This study aimed to investigate the success factors for transforming classrooms into learning communities in digital learning ecosystem (DLE) of Thailand’s secondary schools. Quantitative research was conducted by using a questionnaire as the research instrument to measure teachers’ evaluation of factors. Purposive sampling was applied to obtain a sample group of secondary schools. The questionnaires were sent to teachers at secondary schools in 20 provinces in Northeast Thailand that were employing the smart learning project for their teaching and learning. Data analysis was performed using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis. The results revealed that learning support technologies, teachers, and learners were the three most important factors influencing the development of DLE (<i>x</i> =4.64, <i>x</i> =4.61, and <i>x</i> =4.49, respectively). The findings of this study have implications for educators, administrators, and teachers to review and discover appropriate ways to invest the necessary conditions that can enhance the quality of DLEs and improve teaching and learning activities in the digital environment at secondary schools.