Guofa Li

Tomographic inversion of near-surface Q factor by combining surface and cross-hole seismic surveys

The estimation of the quality factor Q plays a fundamental role in enhancing seismic resolution via absorption compensation in the near-surface layer. We present a new geometry that can be used to acquire field data by combining surface and cross-hole surveys to decrease the effect of geophone coupling on Q estimation. In this study, we drilled number of receiver holes around the source hole, each hole has different depth and each geophone is placed geophones into the bottom of each receiver hole to avoid the effect of geophone coupling with the borehole wall on Q estimation in conventional cross-hole seismic surveys. We also propose a novel tomographic inversion of the Q factor without the effect of the source signature, and examine its stability and reliability using synthetic data. We estimate the Q factors of the near-surface layer in two different frequency bands using field data acquired in the Dagang Oilfield. The results show that seismic absorption in the near-surface layer is much greater than that in the subsurface strata. Thus, it is of critical practical importance to enhance the seismic solution by compensating for near-surface absorption. In addition, we derive different Q factors from two frequency bands, which can be treated, to some extent, as evidence of a frequency-dependent Q.

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.