Respecting the critical role of the low-frequency model in the reliability of seismic inversion, any modification to the process of building this model can contribute to higher accuracy of the subsequent seismic geomechanics modelling. Next, a novel simple approach was formulated to incorporate laboratory information directly into a seismic low-frequency model using an artificial neural network. This achievement was obtained from triaxial deformation tests and ultrasonic measurements on core plugs and revealed that the static Youngs modulus deviates from the dynamic one in porous media, especially in particular ranges of depth and pressure, although conventional regression relationships suggest the opposite, i.e., similar trends for the static and dynamic Youngs moduli. I developed a novel insight into the differences between static and dynamic moduli and their effects on the performance of seismic geomechanics inversion.
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