Pure and Applied Geophysics

, Volume 175, Issue 8, pp 2769–2784 | Cite as

Detection of Natural Fractures from Observed Surface Seismic Data Based on a Linear-Slip Model

  • Huaizhen Chen
  • Guangzhi Zhang


Natural fractures play an important role in migration of hydrocarbon fluids. Based on a rock physics effective model, the linear-slip model, which defines fracture parameters (fracture compliances) for quantitatively characterizing the effects of fractures on rock total compliance, we propose a method to detect natural fractures from observed seismic data via inversion for the fracture compliances. We first derive an approximate PP-wave reflection coefficient in terms of fracture compliances. Using the approximate reflection coefficient, we derive azimuthal elastic impedance as a function of fracture compliances. An inversion method to estimate fracture compliances from seismic data is presented based on a Bayesian framework and azimuthal elastic impedance, which is implemented in a two-step procedure: a least-squares inversion for azimuthal elastic impedance and an iterative inversion for fracture compliances. We apply the inversion method to synthetic and real data to verify its stability and reasonability. Synthetic tests confirm that the method can make a stable estimation of fracture compliances in the case of seismic data containing a moderate signal-to-noise ratio for Gaussian noise, and the test on real data reveals that reasonable fracture compliances are obtained using the proposed method.


Fracture detection Azimuthal seismic data Bayesian inference Seismic inversion Linear-slip model 



We would like to acknowledge the support from SINOPEC Key Lab of Multi-Component Seismic Technology. We want to thank David Henley who is working in CREWES Project, University of Calgary, for helping us to improve the editing and writing. We also thank the reviewers for their valuable and constructive suggestions and the editor Dr. Andrew Gorman for handling this paper.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.China University of PetroleumQingdaoChina
  2. 2.Department of GeoscienceUniversity of CalgaryCalgaryCanada

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