No abstract

S U M M A R YThe effects of fractures on the seismic velocity and attenuation of a rock are investigated using theoretical results and experimental data. Fractures in a rock mass influence the traveltimes and amplitudes of seismic waves that have propagated through them. The displacement discontinuity model, recently employed in fracture investigations, is modified to describe the effect of fractures on seismic-wave velocity and attenuation. This new model, the modified displacement discontinuity model (MDD), is formulated in a way analogous to transmission-line analysis. The fractures are treated as transmission lines for the passage of seismic waves. The M D D takes into consideration realistic fracture parameters which include the fracture length, the fractional area of a fracture surface in contact, and the nature of the infilling material. A single fracture of varying geometric and material properties is shown to affect dramatically the transmission properties of a propagating waveform, and hence the seismic velocity and attenuation. These effects have been shown to result in a frequency-dependent velocity and attenuation. The sensitivity of the fracture parameters to seismic-wave velocity and attenuation was investigated and interesting results were obtained. Fracture parameters used in designing experimental models consisting of synthetically manufactured cracks were fed into the M D D and a well-known crack model, Hudson's model, for comparison. Velocities as a function of the incident-wave angle were obtained from both numerical models and were compared with the results from the experimental modelling. For P waves, the M D D model results show better agreement with those of the experimental model for all crack densities investigated than those from Hudson's model.

Using laboratory data, the influence of rock parameters on seismic attenuation has been analyzed using artificial neural networks and regression models. The predictive capabilities of the neural networks and multiple linear regresssion were compared. The neural network outperforms the multiple linear regression in predicting attenuation values, given a set of input of rock parameters. The neural network can make complex decision mappings and this capability is exploited to examine the influence of various rock parameters on the overall seismic attenuation. The results indicate that the most influential rock parameter on the overall attenuation is the clay content, closely followed by porosity. Though grain size contribution is of lower importance than clay content and porosity, its value of 16 percent is sufficiently significant to be considered in the modeling and interpretation of attenuation data.

No abstract

The inversion of fracture density from field measured P-and S-wave seismic velocities is performed using a neural network trained with an output from the modified displacement discontinuity fracture model. The basic idea is to use input-output pairs generated by the fracture model to train the neural network. Once the neural network is trained, inversion of fracture density from field-measured seismic velocities is performed very quickly. The overall performance of the neural network in the inversion process is assessed by means of a loss function. The results indicate that both sources of field information (P-and S-wave velocities) predict the field fracture density with reasonable accuracy. The performance of the neural network was compared to the prediction from least-squares fitting. It is shown that the neural network out performs the least-squares fitting in predicting the field-fracture density values.