期刊论文详细信息
Contributions to Geophysics and Geodesy
Matlab tool REGCONT2: effective source depth estimation by means of Tikhonov’s regularized downwards continuation of potential fields
Roman Pašteka^11 
[1] Department of Applied and Environmental Geophysics, Faculty of Natural Sciences, 842 48, Bratislava^1
关键词: gravimetry;    magnetometry;    interpretation;    analytical continuation;   
DOI  :  10.2478/congeo-2018-0010
学科分类:地球科学(综合)
来源: De Gruyter
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【 摘 要 】

Transformation based on downward continuation of potential fields is an important tool in their interpretation – depths of shallowest important sources can be determined by means of stable downward continuation algorithms. We analyse here selected properties of one from these algorithms (based on Tikhonov’s regularization approach) from the scope of two most important discretization parameters – dimensions of the areal coverage of the interpreted field and the sampling interval size. Estimation of the source depth is based on the analysis of computed LP-norms for various continuation depths. A typical local minimum of these norms disappears at the source depth. We show on several synthetic bodies (sphere, horizontal cylinder, vertical rod) and also real-world data-sets (results from a magnetic survey for unexploded ordnance detection) that there is a need for relatively large surroundings around the interpreted anomalies. Beside of this also the sampling step plays its important role – grids with finer sampling steps give better interpretation results, when using this downward continuation method. From this point of view, this method is more suitable for the interpretation of objects in near surface and mining geophysics (anomalies from cavities, unexploded ordnance objects and ore bodies). Anomalies should be well developed and separable, and densely sampled. When this is not valid, several algorithms of interpolation and extrapolation (grid padding methods) can improve the interpretation properties of studied downward continuation method.

【 授权许可】

CC BY-NC-ND   

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