地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算...地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.展开更多
In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network plann...In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.展开更多
文摘地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.
基金supported by the National Natural Science Foundation of China (61761004)the Natural Science Foundation of Guangxi Province,China (2019GXNSFAA245045)。
文摘In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.
