Shao-yu Lv, Mu-yuan Jiang, Yuzhuo Chen, Yunsheng Wang
{"title":"利用解空间约束自动选取最佳速度","authors":"Shao-yu Lv, Mu-yuan Jiang, Yuzhuo Chen, Yunsheng Wang","doi":"10.1109/ICCEA53728.2021.00054","DOIUrl":null,"url":null,"abstract":"Picking the best velocity from the velocity spectrum is one of the keys to process seismic data. Aiming at the problems of lower efficiency of manual picking and poor precision of general automatic picking, a solution space constraint method to pick the best velocity automatically was proposed. Firstly, according to the signal similarity coefficient criterion, the original velocity solution space P is constrained to obtain the space P’; Secondly, using the signal in-phase criterion perform the peak match based on kd-Tree’s nearest neighbor search, the space P’ is changed into the space P” by the matching results; Finally, in accordance with the objective function, the automatic picking of the optimal velocity is achieved by the improved particle swarm model in constraint space P”. Experimental results show that the calculation speed of this algorithm is faster, and the error between the automatic picking result and the real reflected signal value is smaller, which meets the needs of actual engineering.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using the Solution Space Constraint to Pick the Best Velocity Automatically\",\"authors\":\"Shao-yu Lv, Mu-yuan Jiang, Yuzhuo Chen, Yunsheng Wang\",\"doi\":\"10.1109/ICCEA53728.2021.00054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Picking the best velocity from the velocity spectrum is one of the keys to process seismic data. Aiming at the problems of lower efficiency of manual picking and poor precision of general automatic picking, a solution space constraint method to pick the best velocity automatically was proposed. Firstly, according to the signal similarity coefficient criterion, the original velocity solution space P is constrained to obtain the space P’; Secondly, using the signal in-phase criterion perform the peak match based on kd-Tree’s nearest neighbor search, the space P’ is changed into the space P” by the matching results; Finally, in accordance with the objective function, the automatic picking of the optimal velocity is achieved by the improved particle swarm model in constraint space P”. Experimental results show that the calculation speed of this algorithm is faster, and the error between the automatic picking result and the real reflected signal value is smaller, which meets the needs of actual engineering.\",\"PeriodicalId\":325790,\"journal\":{\"name\":\"2021 International Conference on Computer Engineering and Application (ICCEA)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computer Engineering and Application (ICCEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEA53728.2021.00054\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer Engineering and Application (ICCEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEA53728.2021.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using the Solution Space Constraint to Pick the Best Velocity Automatically
Picking the best velocity from the velocity spectrum is one of the keys to process seismic data. Aiming at the problems of lower efficiency of manual picking and poor precision of general automatic picking, a solution space constraint method to pick the best velocity automatically was proposed. Firstly, according to the signal similarity coefficient criterion, the original velocity solution space P is constrained to obtain the space P’; Secondly, using the signal in-phase criterion perform the peak match based on kd-Tree’s nearest neighbor search, the space P’ is changed into the space P” by the matching results; Finally, in accordance with the objective function, the automatic picking of the optimal velocity is achieved by the improved particle swarm model in constraint space P”. Experimental results show that the calculation speed of this algorithm is faster, and the error between the automatic picking result and the real reflected signal value is smaller, which meets the needs of actual engineering.