A Deterministic Approach to Noise Attenuation in Oil and Gas Seismic Data Acquisition

Abstract

This paper presents an estimation of an oil and gas seismic data acquisition process which incorporates a priori knowledge of noise contamination in the measured data. A conceptual simplicity of parameter and state estimation by a least squares computational algorithm was developed and a filter was postulated to define the error covariance matrix which yielded unbiased estimates of the measured data.

Key words: Oil and gas seismic data acquisition, stochastic prediction, least squares estimates, linear time-invariant systems, measurement noise filtration, Kalman filter.