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An improved HASM method for dealing with large spatial data sets
- Source :
- Science China Earth Sciences. 61:1078-1087
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.
- Subjects :
- Surface (mathematics)
010504 meteorology & atmospheric sciences
Computer science
010502 geochemistry & geophysics
01 natural sciences
Least squares
Data set
Matrix (mathematics)
Scalability
General Earth and Planetary Sciences
Spatial analysis
Algorithm
Linear equation
0105 earth and related environmental sciences
Block (data storage)
Subjects
Details
- ISSN :
- 18691897 and 16747313
- Volume :
- 61
- Database :
- OpenAIRE
- Journal :
- Science China Earth Sciences
- Accession number :
- edsair.doi...........1491b6474ed8c6e9b3a0c553853f7c8c