Velocity calibration for microseismic monitoring: A very fast simulated annealing approach

2009 ◽  
Author(s):  
Donghong Pei ◽  
John A. Quirein ◽  
Bruce E. Cornish ◽  
Dan Quinn ◽  
Norman R. Warpinski
Keyword(s):  
Simulated Annealing ◽  
Microseismic Monitoring ◽  
Very Fast Simulated Annealing ◽  
Velocity Calibration

Velocity calibration for microseismic monitoring: A very fast simulated annealing (VFSA) approach for joint-objective optimization

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCB47-WCB55 ◽  
Author(s):  
Donghong Pei ◽  
John A. Quirein ◽  
Bruce E. Cornish ◽  
Dan Quinn ◽  
Norman R. Warpinski
Keyword(s):  
Simulated Annealing ◽  
Velocity Structure ◽  
Velocity Model ◽  
Microseismic Monitoring ◽  
S Wave ◽  
Wave Velocities ◽  
Very Fast Simulated Annealing ◽  
Occam’S Inversion ◽  
Synthetic Test ◽  
Ray Parameter

To accurately locate microearthquakes that are genetically related to hydraulic fracture stimulation, a thorough knowledge of the velocity structure between monitoring and fracturing treatment wells is essential. Very fast simulated annealing (VFSA) is implemented to invert for a flat-layered velocity model between wells using perforation or string-shot data. A two-point ray-tracing method is used to find the ray parameter [Formula: see text] for a ray traveling from a source to a receiver. The original traveltime-calculation formula is modified to account for the borehole source-receiver geometry. VFSA is used as a tool to optimize P- and S-wave velocities simultaneously. Unlike previous applications of VFSA, two improvements result from a new study: (1) both P- and S-wave arrival-time misfits are considered in a joint-objective function, and (2) P- and S-wave velocities are perturbed simultaneously during annealing. The inverted velocities follow the true values closely with a very small root-mean-square error, indicating the inverted model is close to the global minimum solution whose rms error should be zero for synthetic examples. Data noise contaminates inverted models, but not substantially in synthetic test results. A comparison of models inverted using VFSA and Occam’s inversion technique indicates that inverted models using VFSA are superior to those using Occam’s method in terms of velocity accuracy.

scholarly journals Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeon-Kyu Park ◽  
Jae-Hyeok Lee ◽  
Jehyun Lee ◽  
Sang-Koog Kim
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Permanent Magnets ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Ndfeb Magnets ◽  
Average Grain Size ◽  
Macroscopic Properties ◽  
Very Fast Simulated Annealing

AbstractThe macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets’ microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets’ macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ0Hc) and maximum magnetic energy product (BHmax) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We conducted several tests of a variety of supervised machine learning (ML) models including kernel ridge regression (KRR), support vector regression (SVR), and artificial neural network (ANN) regression. The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule. In our datasets of randomly generated 1,000 polycrystalline NdFeB cuboids with different microstructural attributes, all of the models yielded similar results in predicting both μ0Hc and BHmax. Furthermore, some outliers, which deteriorated the normality of residuals in the prediction of BHmax, were detected and further analyzed. Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets.

Hybrid phase retrieval algorithm based on modified very fast simulated annealing

2018 ◽  
Vol 10 (9) ◽  
pp. 1072-1080 ◽  
Author(s):  
Yueshu Xu ◽  
Qian Ye ◽  
Guoxiang Meng
Keyword(s):  
Simulated Annealing ◽  
High Speed ◽  
Phase Retrieval ◽  
Global Minimum ◽  
High Efficiency ◽  
Surface Deformation ◽  
Digital Simulation ◽  
Retrieval Algorithm ◽  
Very Fast Simulated Annealing ◽  
Short Time

AbstractThe Misell algorithm is one of the most widely used phase retrieval holography methods for large reflector antennas to measure surface deformation. However, it usually locks in a local minimum because it heads downhill from an initial estimation without any consideration whether it heads for a global minimum or not. The core problem of the Misell algorithm is to find an initial estimation near the global minimum to avoid local stagnation. To cope with the problem, we construct a hybrid Misell algorithm, named modified very fast simulated annealing (MVFSA)-Misell algorithm, to search for the global minimum with a high efficiency. The algorithm is based on the combination of the MVFSA algorithm and Misell algorithm. Firstly, the MVFSA is utilized to obtain a rough position near the global minimum in limited steps. Then, the Misell algorithm starts from the rough position to converge to the global minimum with high speed and accuracy. The convergence characteristic of the proposed algorithm was discussed in detail through digital simulation. Simulation results show that the algorithm can reach global minimum in a very short time. Unlike the traditional Misell algorithm, the hybrid algorithm is not influenced by initial phase estimation.

Simulated annealing velocity analysis: automating the picking process

Geophysics ◽  
2020 ◽  
pp. 1-48
Author(s):  
Danilo Velis
Keyword(s):  
Simulated Annealing ◽  
Prior Information ◽  
Simulated Annealing Algorithm ◽  
Piecewise Linear ◽  
Velocity Analysis ◽  
3D Space ◽  
Common Depth Point ◽  
Automated Method ◽  
Very Fast Simulated Annealing ◽  
Nmo Correction

We propose an automated method for velocity picking that allows to estimate appropriate velocity functions for the normal moveout (NMO) correction of common depth point (CDP) gathers, valid for either hyperbolic or nonhyperbolic trajectories. In the hyperbolic velocity analysis case the process involves the simultaneous search (picking) of a certain number of time-velocity pairs where the semblance, or any other coherence measure, is high. In the nonhyperbolic velocity analysis case, a third parameter, usually associated with the layering and/or the anisotropy, is added to the searching process. The proposed technique relies on a simple but effective search of a piecewise linear curve defined by a certain number of nodes in a 2D or 3D space that follows the semblance maxima. The search is carried out efficiently using a constrained very fast simulated annealing algorithm. The constraints consist of static and dynamic bounding restrictions, which are viewed as a means to incorporate prior information about the picking process. This allows to avoid those maxima that correspond to multiples, spurious, and other meaningless events. Results using synthetic and field data show that the proposed technique permits to automatically obtain accurate and consistent velocity picks that lead to flattened events, in agreement with the manual picks. As an algorithm, the method is very flexible to accommodate additional constraints (e.g. preselected events) and depends on a limited number of parameters. These parameters are easily tuned according to data requirements, available prior information, and the user's needs. The computational costs are relatively low, ranging from a fraction of a second to, at most, 1-2 seconds per CDP gather, using a standard PC with a single processor.

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