摘要
基于碳酸盐含量与地层速度、密度之间的关系,在井资料约束下,使用人工神经网络方法反演高分辨率地震资料所反映的地层碳酸盐含量,并应用于南海北部陆坡ODP184航次1146和1148孔区,取得较好效果.方法的关键是从井旁地震道中提取多种属性,利用逐步回归法,确定6种与碳酸盐含量相关性最好的地震属性,分别是平均频率、道积分绝对振幅、主频、时间、道微分瞬时振幅和瞬时频率,然后进行地层碳酸盐含量反演.反演结果相对于岩心分析的碳酸盐含量的误差大多在±5%之内,较为精确地揭示了地震地层剖面上碳酸盐含量的分布.
Based upon the relationship between carbonate content and stratal velocity and density, we attempted to apply the artificial neural network to the inversion of carbonate content summarized from the high-resolution seismic data limited by controlled well measurements. The method was applied to the slope area of the northern South China Sea near ODP Sites 1146 and 1148, with satisfactory results. The key to this method is the collection of several properties from seismic profiles across or near the wells. Then the progressive regression method was primarily applied to the determination of six seismic properties, most closely related to carbonate content variations, which are defined as average frequency, integrated absolute amplitude, dominating frequency, reflection time, derivative instantaneous amplitude, and instantaneous frequency. Finally, the stratal carbonate content is reversed. The reversal results thus obtained, with the errors of carbonate content mostly ranging within ±5% relative to those measured from sediment samples, show a relative accurate picture of carbonate-content distribution along the slope profile.
出处
《地球科学（中国地质大学学报）》
EI
CAS
CSCD
北大核心
2006年第6期851-856,共6页
Earth Science-Journal of China University of Geosciences
基金
国家自然科学基金项目(Nos.40476030
40576031)
国家重点基础研究发展规划项目(No.G2000078501).
