多分量重力梯度数据联合欧拉反褶积与软件系统设计

doi:10.11720/wtyht.2022.1500

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摘要

和传统欧拉反褶积相比,重力梯度数据联合欧拉反褶积具有更高的计算精度和反演分辨率。为了消除计算产生的发散解,在应用中须使用不同的筛选方法,使得计算流程变得相对繁琐。可见提供有效的筛选方法与开发一个易用的可视化软件有利于提高该方法的准确性、便捷性和使用效果。因此,本文提出基于相关系数边界识别约束的重力梯度数据联合欧拉反褶积,并依据界面直观、功能实用、代码简洁的设计原则,针对算法流程与功能需求,利用Python语言及其函数库设计了一种支持数据/文件管理、二/三维可视化、边界识别、重力梯度数据联合欧拉反褶积等功能的软件系统。通过理论模型与实测数据试验,验证了计算的准确性和软件的实用性,设计的软件系统能够提高应用效果。

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	孙伯轩
	侯振隆
	周文月
	巩恩普
	郑玉君
	程浩

关键词 ：重力梯度数据, 欧拉反褶积, 边界识别, 软件系统设计

Abstract：

Compared with Euler deconvolution, joint Euler deconvolution of multi-component gravity gradient data features higher calculation accuracy and inversion resolution. To eliminate the divergent solutions of calculation, different screening methods must be used in the application of joint Euler deconvolution of multi-component gravity gradient data, making the calculation process cumbersome. It is evident that effective screening methods and developing a piece of easy-to-use and visual software can improve the accuracy, convenience, and effects of the joint Euler deconvolution of multi-component gravity gradient data. Therefore, this study proposed the joint Euler deconvolution of gravity gradient data with the constraint of edge detection based on correlation coefficients. Moreover, on the design principles of the intuitive interfaces, practical functions, and concise codes, this study designed a software system with functions such as data/file management, two-dimensional/three-dimensional visualization, edge detection, and joint Euler deconvolution of multi-component gravity gradient data using Python language and its function library to meet the requirements of algorithm flow and functions. The accuracy of calculations and the practicability of the software have been verified through a theoretical model and tests of measured data, proving that the software designed in this study can improve the application effects.

Key words： gravity gradient data Euler deconvolution edge detection software design

收稿日期: 2021-09-03 修回日期: 2022-02-18 出版日期: 2022-10-20

ZTFLH:

P631

基金资助:中央高校基本科研业务专项资金项目(N2101007);国家重点研发计划项目(2017YFC1503101);国家自然科学基金NSFC-山东联合基金项目(U1806208)

通讯作者: 侯振隆

作者简介: 孙伯轩(1993-),男,辽宁沈阳人,硕士研究生,主要从事重磁勘探数据处理解释研究工作。Email:960143992@qq.com

引用本文:

孙伯轩, 侯振隆, 周文月, 巩恩普, 郑玉君, 程浩. 多分量重力梯度数据联合欧拉反褶积与软件系统设计[J]. 物探与化探, 2022, 46(5): 1241-1250.
SUN Bo-Xuan, HOU Zhen-Long, Zhou Wen-Yue, GONG En-Pu, Zheng Yu-Jun, CHENG Hao. Joint Euler deconvolution of multi-component gravity gradient data and software design. Geophysical and Geochemical Exploration, 2022, 46(5): 1241-1250.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2022.1500 或 https://www.wutanyuhuatan.com/CN/Y2022/V46/I5/1241

Fig.1 边界识别结果可视化(图中红色线框表示立方体边界)
a—总水平导数法;b—方向总水平导数法;c—解析信号法;d—改进的方向总水平导数法;e—归一化方向总水平导数法;f—倾斜角法

Fig.2 软件结构

Fig.3 软件主页面

Fig.4 算法流程

Fig.5 文件预览窗口

Fig.6 数据操作窗口

Fig.7 文件储存路径选择窗口

Fig.8 测线切换窗口

Fig.9 软件输出的边界识别结果

Fig.10 划定地质体范围输入界面

Fig.11 重力梯度数据联合欧拉反褶积计算参数输入界面

Fig.12 重力梯度数据联合欧拉反褶积初步计算结果

Fig.13 结果筛选标准输入界面

Fig.14 重力梯度数据联合欧拉反褶积计算结果可视化

Fig.15 重力异常数据与重力梯度数据等值线(图中红色线框表示立方体边界)
a—V_xx分量;b—V_xy分量;c—V_xz分量;d—V_yy分量;e—V_yz分量;f—V_zz分量;g—V_z

Fig.16 辅助线框坐标输入界面

立方体模型参数表

Fig.17 边界识别结果可视化(图中红色线框表示立方体边界)

Fig.18 筛选后的重力梯度数据联合欧拉反褶积计算结果

Fig.19 文顿盐丘数据联合欧拉反褶积计算结果
a—三维显示;b—z方向视图

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