Radon域虚震源法重构VSP数据  

Reconstruction of VSP data by virtual source method in the Radon domain

在线阅读下载全文

作  者:陈颖 许卓[1] 韩立国 巩向博[1] CHEN Ying;XU Zhuo;HAN Liguo;GONG Xiangbo(College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China)

机构地区:[1]吉林大学地球探测科学与技术学院,长春130026

出  处:《世界地质》2023年第2期357-366,共10页World Geology

基  金:国家自然科学基金项目(42274164、42130805、42074151)。

摘  要:垂直地震剖面(VSP)数据通常比常规地面地震数据(SSP)具有更高的勘探精度,但受其观测系统限制,具有成像范围有限等问题。为解决VSP数据成像范围有限的问题,笔者将Radon变换与互相关型地震干涉技术和褶积型地震干涉技术相结合,通过Radon域虚震源法将VSP数据重构为虚拟SSP数据,并利用Radon变换方法压制随机噪声的特性,提高虚拟SSP炮集记录的信噪比。通过数值模拟,验证了Radon域虚震源法的有效性,并进一步用于添加了随机噪声的原始VSP数据。结果表明,相比于传统虚震源法,Radon域虚震源法能够有效压制随机噪声,提高虚拟炮集记录的信噪比,与VSP原始数据相比,所构建的虚拟SSP炮集记录经偏移成像后可以有效扩大成像范围并提高成像分辨率。With respects to the conventional surface seismic profile(SSP)data,the vertical seismic profile(VSP)data usually have higher exploration accuracy,but the imaging range of VSP data is limited due to the limited observation system.In order to solve the problem of limited imaging range of VSP data,the authors have combined Radon transform with cross-correlation seismic interferometry and convolution seismic interferometry.The VSP data are transformed into virtual SSP data by virtual source method in the Radon domain.The characteristics of random noise is suppressed and the signal/noise ratio of virtual SSP records is improved by using the Radon transform method.The effectiveness of virtual source method in the Radon domain is verified by numerical simulation,which is further applied to the original VSP data with the added random noise.The results show that compared with the traditional virtual source method,the virtual source method in the Radon domain can effectively suppress random noise and improve the signal/noise ratio of virtual source records.With respects to the original VSP data,the newly formed virtual SSP records after migration imaging can effectively expand the image range and improve the resolution.

关 键 词:地震干涉技术 虚源法 线性Radon变换 VSP 

分 类 号:P631.4[天文地球—地质矿产勘探]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象