Stress, rock microstructure and frequency are the three key factors that influence the velocities of elastic waves and, hence, are sensitive to the Biot effective stress parameter (α) in porous rocks. The effective stress in an isotropic poroelastic medium relates to applied pressure and pore pressure, with the Biot parameter (α) as a scaling factor of the pore pressure. This paper provides an independent derivation of the tensor characteristics of α through elastic moduli, a microscopic effective medium derivation, and frequency‐dependent behaviour of α for an anisotropic medium. We provide an explicit expression, especially for isotropic rock under uniaxial stress, considering the nonlinear part of elastic constants. In the effective medium derivation, we assumed that the rock contained both isolated pores and connected pores saturated with liquid. To support our theoretical formulation, we calculated the Biot tensor of sandstone and shale by inverting the ultrasonic velocities of transversely isotropic rock under uniaxial stress where mineralogical composition and porosity are known. Even though porosity and rock microstructure play significant roles in α as stress varies, we also see as much as a 21% difference between horizontal and vertical components of α for rocks with transversely isotropic symmetry. We then estimated the frequency‐dependent Biot tensor for transversely isotropic models using numerical calculations. We noticed significant differences between vertical (α33) and horizontal (α11) components of α, especially at the surface seismic frequency band. However, uniaxial stress and horizontally aligned microstructure influence the elastic moduli and Biot tensor contrarily. In general, anisotropy due to uniaxial stress shows lower α33 and higher α11. The proposed method shows an excellent prediction of α33 and α11 for given data of uniaxial stress and vice versa.

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The article examines the problem of processing microseismic noise (MN) to identify and evaluate occurrence depth of contrasting geological objects – intersalt interlayers with a potentially high formation pressure. If it is impossible to use artificial wave sources, statistic processing of passive seismic data becomes critical. Due to the accumulation of power spectral density (PSD) during a long-term recording of MN the deterministic medium effect on a random signal spectrum is identified. PSD modulation when the surface or the bottom of the layered medium is exposed to white noise is expressed in terms of the Green’s function (GF) of a wave equation. Relevant GF variations corresponding to the layers form the basis for accumulated PSD approximation, and indicate the depth and contrast of the target features.

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