Field quality evaluation method of massive seismic acquisition data

被引：0

作者：

Xu L. ^{[1
]}

Xu W. ^{[1
]}

机构：

[1] Shengli Branch, Geophysical Company of Sinopec, Dongying

来源：

Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2021年 / 56卷 / 06期

关键词：

Evaluation method of single shot record; Field quality evaluation; Intelligent single shot record classification; Massive seismic data; Multi-attribute evaluation mo-del; Random forest;

D O I：

10.13810/j.cnki.issn.1000-7210.2021.06.001

中图分类号：

学科分类号：

摘要：

With the development of high-efficiency acquisition and wide frequency, wide azimath, high density seismic exploration technology, seismic acquisition data shows exponential growth, which brings challenges to the field quality evaluation of such data. The present theoretical field evaluation method of massive seismic data and its adaptability in quality control of massive seismic acquisition data are studied in this paper. It is difficult for single attribute models to fully characterize the quality of seismic data. A multi-attribute discriminant analysis model for single shot records and its production process are designed. Given the severe subjectivity of the multi-attribute discriminant analysis model that completely relies on the standard records and the threshold value, an intelligent quality classification model for massive seismic data is put forward. With feature analysis of massive seismic data, an intelligent evaluation process of single shot records based on the random forest is proposed. Three sample enhancement techniques are used to solve the problem of small and unbalanced samples of single shot records. The random forest classification algorithm of single shot records and its key technologies are studied, including the branch node construction based on continuous seismic attributes, the selection of modeling parameters, and the evaluation of the classification results. The application of experimental data shows that the results of the new method are correct and ready to be highly parallelized. Finally, according to the analysis of the correlations of these models as well as their adaptability and timeliness, the combined application of multiple models can meet the requirements of field quality control of massive seismic acquisition data. © 2021, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.

引用

页码：1205 / 1213

页数：8

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