4.6 Article

Detection of geological boundaries by 3D gravity inversion for density gradients in different directions

Journal

FRONTIERS IN EARTH SCIENCE
Volume 10, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/feart.2022.967771

Keywords

gravity; 3D inversion; density gradient; dip recognition; joint inversion

Funding

  1. National Key R&D Program of China
  2. China Geological Survey Project [2018YFE0208300]
  3. [DD20221639-03]
  4. [DD20221638]
  5. [DD20190030]

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The spatial position and dip feature of density boundaries are crucial for the study of fault and tectonic frameworks. A density gradient inversion method is proposed to quantitatively and qualitatively identify the strike and dip features of faults, overcoming the limitations of sparse distribution and poor lateral resolution of existing seismic data.
The spatial position and dip feature of the density boundary are significant to the study of fault and tectonic frameworks. Edge detection methods generally attach importance to the horizontal position of the boundary, but it is difficult to determine the dip feature expressly. A density gradient inversion method was proposed based on the corresponding relationship among the gravity forward field, forward kernel matrix, and model attributes. The inversion result of this method is that the density gradient value is different from the conventional gravity inversion. It can directly display the 3D distribution features integrated with 3D inversion results of the density gradient in different directions. The theoretical model means that the inversion results can not only identify the horizontal position of the boundary but also qualitatively determine the dip feature of faults. It has been widely applied to fault identification in the Songliao Basin. According to the joint inversion results, the strike feature and the dip feature can be quantitatively and qualitatively identified, respectively, making up for the shortcomings of sparse distribution and poor lateral resolution of existing seismic data.

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