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本文着重讨论在已知气温、地温、蒸发、降雨等主要气象参数的前提下,用统计回归方法排除其干扰影响。对于有记忆影响的降雨因子采用褶积滤波方法进行处理,并顾及干扰因子的不同影响,采用多元回归方法进行综合分析,以求达到排除干扰识别异常的目的。并通过算例分别探讨了降雨因素对土层点与基岩点观测数据的影响;数据处理后,证实跨断层测线反映异常信息优于不跨断层测线;对于异常年如何参加回归也提出了一些看法。
This paper focuses on the known temperature, ground temperature, evaporation, rainfall and other major meteorological parameters under the premise of using statistical regression method to exclude the interference. For memorized rainfall factors, a convolution filter method is used to deal with them. Taking into account the different influences of interference factors, multiple regression methods are used for comprehensive analysis in order to achieve the goal of eliminating interference identification anomalies. The effects of rainfall on the observation data of soil layer and bedrock are discussed respectively through numerical examples. After data processing, it is confirmed that the cross-fault survey reflects that the anomaly information is better than the one without cross-fault survey. Some comments.