Improvements in land seismic static calculation via simultaneous joint inversion and integrated earth modeling

Abstract

With the growth in geographic scale of land seismic exploration, increasingly complex near-surface modeling capabilities are being employed for static corrections in seismic data processing. Conventional techniques making use of first-break refraction arrivals tend to fail when first-arrival quality is poor (as is common with vibroseis sources) and are also challenged by geological complexity of the near-surface such as presence of velocity inversions or low-velocity zones. Automated first-arrival picking can introduce systematic error to the process, but the introduction of other data types can be used to improve the quality of the result. In GLI Refraction Statics or GRM information on the velocity of the weathered near-surface layer is required. In the case of vibroseis acquisition, near-surface velocity information typically requires a separate uphole survey. Simultaneous Joint Inversion (SJI) is an emerging technique that allows exploitation of multiple data types linked through the earths' geometrical or petrophysical properties. Used as static solver, the tool allows replacement of uphole shots by more economical surface soundings such as gravity, electromagnetics, and/or Rayleigh waves to build a velocity model. Single domain inversions of the individual data types are used to determine a near-surface starting model which is then refined through joint inversion with links provided by rock physics relations. This approach has proved effective and robust in overcoming local or systematic errors in seismic first-break interpretation. While penetration depth and resolution will vary with the type of complimentary data available, the P-Velocity obtained through SJI of surface data generally extends deeper than what is required for static correction. Velocity models developed through this approach do not suffer from velocity-depth ambiguity and provide a well resolved shallow model for seismic depth imaging. We will present the theory of this approach with examples drawn from recent projects.