Effective stress coefficient
Presentation Date: Tuesday, October 18, 2016
Start Time: 1:00:00 PM
Presentation Type: ORAL
An, Shengpei (Peking University) | Hu, Tianyue (Peking University) | Wang, Weizhong (Peking University) | Cui, Yongfu (Research Institutes of Exploration and Development, Tarim Oilfield, CNPC) | Duan, Wensheng (Research Institutes of Exploration and Development, Tarim Oilfield, CNPC) | Peng, Gengxin (Research Institutes of Exploration and Development, Tarim Oilfield, CNPC)
Recognition and identification of effective signals with low signal-to-noise ratio (SNR) is one of the difficulties for seismic data processing. The super-virtual interferometry (SVI) method is able to promote SNR of interferometric signals which satisfy stationarity conditions, and achieves good results of building velocity models and diffraction imaging with low SNR data. Either the correlation or convolution based SVI method is susceptible to additive noise and is only discussed in 2D cases. To overcome these limitations, this paper develops the higher-order cumulant based coherent integration (HOCCI) method to enhance interferometric signals by substituting the process of correlation and convolution in the SVI method by higherorder cumulant and multidimensional convolution. The 3D synthetic data examples demonstrate that the HOCCI method, compared with the SVI method, has better performance in promoting SNR of interferometric signals and suppressing coherent noise, and the 3D field data examples further confirm the accuracy of the 3D scheme of HOCCI method.
The super-virtual interferometry (SVI) method can enhance interferometric signals satisfying stationarity conditions, which partly share the common raypaths (Snieder et al, 2006). The commonly discussed interferometric signals include refractions from the same layer and the diffracted waves from the same diffraction point. The SVI method is implemented by two steps: correlation and summation of seismic data to generate virtual traces with enhanced interferometric signals, followed by convolution with actual traces to obtain super-virtual traces with further enhanced interferometric signals (Mallinson et al., 2011; Bharadwaj et al., 2012). This method achieves good results of extracting diffracted waves to image deep structures (Dai et al., 2011) and of promoting the SNR of first breaks in low SNR traces for undulate surface areas (An et al., 2014).
However, the SVI method has some limitations. (1) The irregular geometry, which is designed to adapt to undulate surface conditions, may lead to few available sources and receivers which generate interferometric signals, such that the SNR enhancement of the SVI method is limited in this case. (2) The process of correlation in the SVI method is susceptible to additive noise, especially when additive noise in different traces is coherent. (3) The SVI method is only discussed in 2D cases and cannot be completely implemented in 3D cases.
Summary Arbitrary Difference Precise Integration (ADPI) method is evolved from Finite Difference (FD) method, and it adopts integration scheme in time domain. In this paper, we deduce the formula of ADPI method based on 1-D elastic equation. The numerical comparison shows that ADPI is more stable than FD method. In forward modeling cases, ADPI method is applied in 2D and 3D elastic wave equation forward modeling. Results show that the travel time of reflected seismic wave is accurate and the method can be easily applied to elastic wave equation forward modeling for geological models.
Seismic modeling is an effective method for studying the propagation of seismic waves within complex structures. Based on finite difference method, the arbitrary difference precise integration (ADPI) for seismic forward modeling was developed for 3-D seismic modeling in this paper. When it comes to cases of 3-D modeling, compared with CPU single-core or multi-core processors, graphic processing unit (GPU) parallel calculation shows its outstanding ability of fast calculation to make a seismic forward modeling closer to real seismic records at very low cost of personal computer. Cases study of 3D seismic forward modeling confirm the correction and efficiency about the methodology of ADPI techniques and its GPU algorithms.
Tang, Genyang (School of Earth and Space Sciences, Peking University, China.) | Hu, Tianyue (School of Earth and Space Sciences, Peking University, China.) | Yang, Jinhua (School of Earth and Space Sciences, Peking University, China.)