【摘要】 以油气化探异常和干扰频率特征分析为基础 ,建立了应用小波分析进行干扰排除的新方法 ,提出异常类型及其形成机制的理论模型 ,并在此基础上建立了以统计学、分形几何学、人工神经网络以及计算机技术为支撑的异常识别新方法。应用这些新方法 ,不仅可以区分油气化探中的背景异常、微渗逸异常和渗逸异常 ,而且有利于研究断层属性及其与油气的关系 ,有利于研究圈闭的封盖条件及含油气性。二连盆地、冀中坳陷和济阳坳陷的应用实践表明 ,应用上述油气化探新方法 ,不仅可以大大减少化探异常解释的多解性 ,而且与地质、地震资料相结合进行综合勘探能够有效降低勘探风险 ,提高勘探成效更多还原

【Abstract】 Based on the characterization of anomalies and interference frequency in geochemical exploration, a new method to eliminate interference by using wavelet analysis is established, and a theoretical model of anomaly types and their formation mechanisms is developed. With these foundations, a set of anomaly recognition methods is proposed, which is supported by statistics, fractal geometry, artificial neural network and computer technique. These new methods can not only recognize background, micro_seepage and seepage anomalies, but also are helpful to understanding faults nature and their relationship with hydrocarbons and to investigating seal conditions of traps and their hydrocarbons abundance. The application of these methods in Erlian Basin, Jizhong Depression and Jiyang Depression has demonstrated that these new methods can substantially reduce the uncertainty of geochemical anomaly explanation, and may be combined with geological and seismic data to reduce exploration risk and increase success rate.更多还原