本文提出一种自适应计算变化幅度的方法，用于提取直流视电阻率中短期异常。采用傅立叶滑动方法计算日均值或月均值曲线的年变化成分，将观测值减去年变化得到去年变数据；之后采用小波分解方法提取去年变数据的趋势变化，以去年变后曲线每一次穿过趋势线时的观测值为起点，计算之后数据相对于该起点值的变化幅度，并采用整个分析时段内变化幅度的2.5倍累计均方差均值作为异常阈值线。该方法有效缓解了在传统原始曲线分析中对异常起始时间的界定问题；基于多年常态变化幅度的异常阈值线，可用于提取变化幅度低于现有异常阈值1%的中短期异常。

In this paper, we proposed a method of adaptive variation amplitude, which can be used to extract medium and short-term anomalies of apparent resistivity. The Fourier sliding method is applied to calculate the annual variation component of the daily or monthly average data, and the observed value is subtracted from the annual variation to obtain the annual-subtraction data. Then the wavelet decomposition method is used to extract the trend change of the annual-subtraction data. When the annual-subtraction data curve crosses the trend line each time, the corresponding data in the annual-subtraction data set is regarded as the starting point. Then the relative variation amplitude of the later data relative to the starting point is calculated. The abnormal threshold line is obtained when the cumulative mean square deviation of the change range in the entire analysis period reaches 2.5 times high. This method effectively alleviates the problem of defining the abnormal start time in traditional original curve analysis. The abnormal threshold line based on the multi-year normal variation range can be used to extract medium and short-term anomalies in which variation range is less than 1%.

P315

国家自然科学基金项目（42104075）、国家重点研发计划（2017YFC1500502）共同资助