Feasibility Analysis on Practical Application of Virtual Events Method in Predicting Seismic Interbed Multiples

doi:10.13209/j.0479-8023.2016.089

Abstract

Abstract:

There are problems such as efficient match of multiples, influence of other noise and geometry when the virtual events method is used in real data. By studying on model data, an efficient matching method based on high-order and high-resolution Radon transform was presented, which had advantages in matching time and effect. There are also some quantitative analyses and parametric optimization on filter matrix and length of time window of the traditional matching methods. The authors made some quantitative analyses on the anti-noise capacity of virtual events method and discussed its range of application. The results have some referential meaning when the method is used in real data.

Key words: virtual events, interbed multiple, multiple match, noise interference

摘要：

针对虚同相轴法在实际应用中存在多次波的高效匹配、其他噪声的干扰以及观测系统的影响等问题, 通过对模型数据的研究, 提出一种基于高阶高分辨率Radon变换的高效匹配方法, 在匹配时间和效率两方面有很大提高。对传统范数匹配方法中的滤波矩阵、时窗长度进行定量分析和优化选取, 对虚同相轴方法的抗噪能力以及观测系统的影响进行定量分析, 并讨论其适用范围。研究结果对该方法的实际应用有一定的参考意义。

关键词: 虚同相轴法, 层间多次波, 多次波匹配, 噪声干扰

CLC Number:

P315

Lu LIU, Tianyue HU, Shengpei AN, Jing WU. Feasibility Analysis on Practical Application of Virtual Events Method in Predicting Seismic Interbed Multiples[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(1): 26-36.

刘璐, 胡天跃, 安圣培, 吴静. 虚同相轴法预测地震层间多次波在实际应用中的可行性分析[J]. 北京大学学报自然科学版, 2017, 53(1): 26-36.

Figures/Tables 18

Fig. 1 Schematic diagram of virtual events method

Fig. 2 Oblique model for producing internal multiple

Fig. 3 Synthetic record of oblique layer model and its predicting and matching result

Fig. 4 Simulated South Yellow Sea geological model (after Ref. [16])

Fig. 5 Synthetic record and multiple elimination result

Fig. 6 Deconvolution effect diagram

Fig. 7 Single L2 norm matching result before and after improving M martrix

Fig. 8 Matching effect of different time windows in each trace

Fig. 9 Higher-order high resolution parabolic Radon transform of raw data (a) and predicted data (b)

Fig. 10 Result of adding different Gauss white noise and its predicted consequence

Fig. 11 Residual comparison of raw data and the predicted consequence of different white noise added data

Fig. 12 Result of adding different low-frequence background noise and its predicted consequence

Fig. 13 Residual comparison of raw data and the predicted consequence of different low-frequence background noise added data

Fig. 14 Forward data including surface wave and its predicted consequence

Fig. 15 Residual comparison of raw data and the predicted consequence of different low-frequence linear noise added data

Fig. 16 The predicted consequence of different geometries

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