{"title":"利用可变邻域搜索确定基于 GAN 的地震内插器的超参数","authors":"","doi":"10.1016/j.cageo.2024.105689","DOIUrl":null,"url":null,"abstract":"<div><p>We propose an automatic global search algorithm based on the Variable Neighborhood Search (VNS) metaheuristic for tuning the hyperparameters of a generative adversarial network (GAN) seismic interpolator. We perform an exhaustive search to study the influence of each hyperparameter in the training process, and compare the proposed method with Random search and Bayesian Search. The seismic data set used for this study was synthetically modeled from a typical velocity model, estimated from a pre-salt field of the Brazilian cost. We also employ the proposed method with a real field data to show the importance and applicability of the search for optimum hyperparameters of GAN. The training data was constructed with decimated seismic data and the results were tested by comparing the reconstructed data with the original one. We performed two hyperparameter impact analyses: the first consists of an exhaustive grid exploration and the second consists of our proposed automatic exploration method using the VNS algorithm, comparing it with the other two algorithms. We concluded that the proposed method, which has a user-friendly usage, as it is almost parameter-free, can reach solutions with very good quality quickly, in any range of hyperparameter values. When compared with other methods of hyperparameter tuning, the one we propose proves to be better in the ease of configuration, while being efficient in the search process.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperparameter determination for GAN-based seismic interpolator with variable neighborhood search\",\"authors\":\"\",\"doi\":\"10.1016/j.cageo.2024.105689\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We propose an automatic global search algorithm based on the Variable Neighborhood Search (VNS) metaheuristic for tuning the hyperparameters of a generative adversarial network (GAN) seismic interpolator. We perform an exhaustive search to study the influence of each hyperparameter in the training process, and compare the proposed method with Random search and Bayesian Search. The seismic data set used for this study was synthetically modeled from a typical velocity model, estimated from a pre-salt field of the Brazilian cost. We also employ the proposed method with a real field data to show the importance and applicability of the search for optimum hyperparameters of GAN. The training data was constructed with decimated seismic data and the results were tested by comparing the reconstructed data with the original one. We performed two hyperparameter impact analyses: the first consists of an exhaustive grid exploration and the second consists of our proposed automatic exploration method using the VNS algorithm, comparing it with the other two algorithms. We concluded that the proposed method, which has a user-friendly usage, as it is almost parameter-free, can reach solutions with very good quality quickly, in any range of hyperparameter values. When compared with other methods of hyperparameter tuning, the one we propose proves to be better in the ease of configuration, while being efficient in the search process.</p></div>\",\"PeriodicalId\":55221,\"journal\":{\"name\":\"Computers & Geosciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Geosciences\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0098300424001729\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Geosciences","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0098300424001729","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
摘要
我们提出了一种基于可变邻域搜索(VNS)元启发式的自动全局搜索算法,用于调整生成式对抗网络(GAN)地震内插器的超参数。我们进行了一次穷举搜索,以研究每个超参数在训练过程中的影响,并将所提出的方法与随机搜索和贝叶斯搜索进行了比较。本研究使用的地震数据集是根据典型的速度模型合成的,该速度模型由巴西成本的盐前油田估算得出。我们还利用真实油田数据采用了所提出的方法,以显示搜索 GAN 最佳超参数的重要性和适用性。训练数据是用去矩化地震数据构建的,并通过比较重建数据和原始数据对结果进行了测试。我们进行了两项超参数影响分析:第一项包括详尽的网格探索,第二项包括我们提出的使用 VNS 算法的自动探索方法,并与其他两种算法进行了比较。我们得出的结论是,所提出的方法使用方便,几乎不需要参数,在任何超参数值范围内,都能快速获得质量非常高的解决方案。与其他超参数调整方法相比,我们提出的方法在配置简便性方面更胜一筹,同时在搜索过程中也很高效。
Hyperparameter determination for GAN-based seismic interpolator with variable neighborhood search
We propose an automatic global search algorithm based on the Variable Neighborhood Search (VNS) metaheuristic for tuning the hyperparameters of a generative adversarial network (GAN) seismic interpolator. We perform an exhaustive search to study the influence of each hyperparameter in the training process, and compare the proposed method with Random search and Bayesian Search. The seismic data set used for this study was synthetically modeled from a typical velocity model, estimated from a pre-salt field of the Brazilian cost. We also employ the proposed method with a real field data to show the importance and applicability of the search for optimum hyperparameters of GAN. The training data was constructed with decimated seismic data and the results were tested by comparing the reconstructed data with the original one. We performed two hyperparameter impact analyses: the first consists of an exhaustive grid exploration and the second consists of our proposed automatic exploration method using the VNS algorithm, comparing it with the other two algorithms. We concluded that the proposed method, which has a user-friendly usage, as it is almost parameter-free, can reach solutions with very good quality quickly, in any range of hyperparameter values. When compared with other methods of hyperparameter tuning, the one we propose proves to be better in the ease of configuration, while being efficient in the search process.
期刊介绍:
Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.