Contents
Academic literature on the topic 'INVERSION-COMPRESSION METHOD'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'INVERSION-COMPRESSION METHOD.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "INVERSION-COMPRESSION METHOD"
1
Li, Shouju, De Li, Lijuan Cao, and Zichang Shangguan. "Parameter estimation approach for particle flow model of rockfill materials using response surface method." International Journal of Computational Materials Science and Engineering 04, no. 01 (2015): 1550003. http://dx.doi.org/10.1142/s2047684115500037.
Full textAbstract:
Particle flow code (PFC) is widely used to model deformation and stress states of rockfill materials. The accuracy of numerical modeling with PFC is dependent upon the model parameter values. How to accurately determine model parameters remains one of the main challenges. In order to determine model parameters of particle flow model of rockfill materials, some triaxial compression experiments are performed, and the inversion procedure of model parameters based on response surface method is proposed. Parameters of particle flow model of rockfill materials are determined according to the observed data in triaxial compression tests for rockfill materials. The investigation shows that the normal stiffness, tangent stiffness and friction coefficient of rockfill materials will slightly increase with increase of confining pressure in triaxial compression tests. The experiments in laboratory show that the proposed inversion procedure behaves higher computing efficiency and the forecasted stress–strain relations agree well with observed values.
2
Chen, Dai Heng, and Lu Yang. "Inversion Behavior for Cylindrical Tubes under Axial Compression." Advanced Materials Research 430-432 (January 2012): 512–16. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.512.
Full textAbstract:
This paper presents an analysis of tube inversion under axial compression by a finite element method with the aim of expressing and calculating the axial stress from the stress equilibrium equation. The state of a cylinder under internal stress and the inversion transformation mechanism are then clarified, and verification of the proposed theoretical model is examined.
3
Habashy, T. M., A. Abubakar, G. Pan, and A. Belani. "Source-receiver compression scheme for full-waveform seismic inversion." GEOPHYSICS 76, no. 4 (2011): R95—R108. http://dx.doi.org/10.1190/1.3590213.
Full textAbstract:
We have developed a source-receiver compression approach for reducing the computational time and memory usage of the acoustic and elastic full-waveform inversions. By detecting and quantifying the extent of redundancy in the data, we assembled a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers used in the survey. Because the numbers of these simultaneous sources and receivers could be significantly less than those of the physical sources and receivers, the computational time and memory usage of any gradient-type inversion method such as steepest descent, nonlinear conjugate gradient, contrast-source inversion, and quasi-Newton methods could be reduced. The scheme is based on decomposing the data into their principal components using a singular-value decomposition approach, and the data reduction is done through the elimination of the small eigenvalues. Consequently, this would suppress the effect of noise in the data. Moreover, taking advantage of the redundancy in the data, this compression scheme effectively stacks the redundant data, resulting in an increased signal-to-noise ratio. For demonstration of the concept, we produced inversion results for the 2D acoustic Marmousi and BP models for surface measurements and an elastic model for crosswell measurements. We found that this approach has the potential to significantly reduce computational time and memory usage of the Gauss-Newton method by 1–2 orders of magnitude.
4
Foks, Nathan Leon, Richard Krahenbuhl, and Yaoguo Li. "Adaptive sampling of potential-field data: A direct approach to compressive inversion." GEOPHYSICS 79, no. 1 (2014): IM1—IM9. http://dx.doi.org/10.1190/geo2013-0087.1.
Full textAbstract:
Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.
5
Portniaguine, Oleg, and Michael S. Zhdanov. "3‐D magnetic inversion with data compression and image focusing." GEOPHYSICS 67, no. 5 (2002): 1532–41. http://dx.doi.org/10.1190/1.1512749.
Full textAbstract:
We develop a method of 3‐D magnetic anomaly inversion based on traditional Tikhonov regularization theory. We use a minimum support stabilizing functional to generate a sharp, focused inverse image. An iterative inversion process is constructed in the space of weighted model parameters that accelerates the convergence and robustness of the method. The weighting functions are selected based on sensitivity analysis. To speed up the computations and to decrease the size of memory required, we use a compression technique based on cubic interpolation. Our method is designed for inversion of total magnetic anomalies, assuming the anomalous field is caused by induced magnetization only. The method is applied to synthetic data for typical models of magnetic anomalies and is tested on real airborne data provided by ExxonMobil Upstream Research Company.
6
Pan, Shiyang, Tongchun Li, Guicai Shi, Zhen Cui, Hanjing Zhang, and Li Yuan. "The Inversion Analysis and Material Parameter Optimization of a High Earth-Rockfill Dam during Construction Periods." Applied Sciences 12, no. 10 (2022): 4991. http://dx.doi.org/10.3390/app12104991.
Full textAbstract:
Inversion analysis is usually an efficient solution to process the monitoring data of earth-rockfill dams. However, it is still difficult to obtain calculation results that are consistent with monitoring data due to different construction statuses. To deal with this situation and to introduce a new solution to improve calculation accuracy, the general method of inversion analysis based on back-propagation neural networks and the original step-by-step inversion method assuming that the parameters of the constitutive model vary with construction periods are introduced and verified in this work. Then, both methods are applied in the inversion analysis of a high gravelly soil core rock-fill dam during construction periods. Moreover, the relationship between the inversed material parameters and the stress values of the core wall is discussed. The material parameters are further optimized to obtain more accurate displacement values. The results show that the step-by-step inversion method has a higher accuracy in vertical compression values compared with the conventional inversion method, the trend of material parameter K is more significant than other parameters, and the proposed variable parameter constitutive model has an accuracy between the step-by-step and conventional inversion methods. Conclusions can be drawn that the original step-by-step inversion method has more advantages than the conventional method and the variable parameter constitutive model proposed in this paper might be more suitable for the analysis of a high earth-rockfill dam during construction periods.
7
Lin, Cherng-Yuan, and Keng-Hung Lin. "Comparison of the Engine Performance of Soybean Oil Biodiesel Emulsions Prepared by Phase Inversion Temperature and Mechanical Homogenization Methods." Processes 11, no. 3 (2023): 907. http://dx.doi.org/10.3390/pr11030907.
Full textAbstract:
The engine performance and emission characteristics of burning emulsions of soybean oil biodiesel in a compression-ignition diesel engine prepared through the phase inversion temperature method were compared with those of neat soybean oil biodiesel and the emulsion prepared by the mechanical homogenization method. The engine torque was set constantly at 98 N·m with varying engine speeds. The experimental results show that the emulsion prepared by the method of phase inversion temperature had higher O2 and NOx emissions, a higher excess air ratio, a higher exhaust gas temperature, and a higher brake fuel conversion efficiency than the emulsion prepared by the mechanical homogenization method, which had lower CO and CO2 emissions, a lower equivalence ratio, and lower brake-specific fuel consumption. While the neat soybean oil biodiesel was found to have the lowest fuel consumption rate, brake-specific fuel consumption, and CO and CO2 emissions, it had the highest exhaust gas temperature and brake fuel conversion efficiency, NOx and O2 emissions, and excess air ratio among those three fuels. Therefore, the phase inversion temperature method is considered promising for preparing fuel emulsions as an alternative to petro-derived diesel for compression-ignition engines.
8
Jiang, Dandan, Zhaofa Zeng, Shuai Zhou, Yanwu Guan, Tao Lin, and Pengyu Lu. "Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression." Applied Sciences 10, no. 21 (2020): 7636. http://dx.doi.org/10.3390/app10217636.
Full textAbstract:
Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.
9
Liu, Weiqiang, and Liangyong Yang. "A New Method for Geophysical Induced Polarization Inversion Based on Stochastic Medium Model and Sample-Compressed Artificial Neural Network." Journal of Physics: Conference Series 2025, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2025/1/012010.
Full textAbstract:
Abstract Induced polarization (IP) is a near-surface geophysical exploration method. Inverting the electrical properties of the underground medium from surface apparent IP parameters is a highly nonlinear problem. To further improve the accuracy, the artificial neural network (ANN) algorithm is applied to the two-dimensional (2D) IP data inversion for the first time. We firstly produced smooth geo-electric models based on the stochastic medium theory, and obtained the corresponding theoretical responses through forward modelling. Then, we compressed the responses and models through image compression technology. Finally, the above compressed responses and models were used as input and output samples to train an optimal network system for inversion. We tested the algorithm with synthetic examples. The results show that ANN can improve the longitudinal resolution of the inversion results and make the inversion results more focused.
10
Abubakar, A., T. M. Habashy, Y. Lin, and M. Li. "A model-compression scheme for nonlinear electromagnetic inversions." GEOPHYSICS 77, no. 5 (2012): E379—E389. http://dx.doi.org/10.1190/geo2011-0494.1.
Full textAbstract:
We have developed a model-compression scheme for improving the efficiency of the regularized Gauss-Newton inversion algorithm for marine controlled-source electromagnetic applications. In this scheme, the unknown model parameters (the conductivity/resistivity distribution) are represented in terms of a basis such as Fourier and wavelet (Haar and Daubechies). By applying a truncation criterion, the model may then be approximated by a reduced number of basis functions, which is usually much less than the number of the model parameters. Further, because the controlled-source electromagnetic measurements have low resolution, it is sufficient for inversion to only keep the low-spatial-frequency part of the image. This model-compression scheme accelerates the computational time and also reduces the memory usage of the Gauss-Newton method. We are able to significantly reduce the algorithm computational complexity without compromising the quality of the inverted models.