海域地震对我国海洋资源开发和沿海地区的经济建设形成严重威胁，开展相关地震活动性研究的重要前提是编译我国海域及邻区的地震目录。我国常用的震级标度为面波震级（M_S），而国际上最新的地震活动模型多采用矩震级（M_W），因此在应用这些模型时需要拟合面波震级与矩震级之间的转换关系。本文以中国海域及邻区为研究区，收集了1988—2020年中国地震台网的面波震级和全球矩心矩张量（GCMT）项目的矩震级数据，从中提取年份、深度、经度、纬度、面波震级作为影响因子，以实际记录的矩震级值作为标记，训练BP神经网络建立以GCMT的矩震级为目标的震级转换模型。同时，使用最小二乘回归和正交回归建立线性模型作为对比。结果显示，最小二乘回归和正交回归的平均绝对误差和均方根误差比BP神经网络高40%左右。此外，BP神经网络的残差绝对值更小、分布更集中。

Earthquakes in sea areas pose a serious threat to the development of China's marine resources and the economic construction of coastal areas. A crucial prerequisite for conducting relevant seismicityresearch is to compile an earthquake catalog for China's seas and adjacent regions. The commonly used magnitude scale in China is surface wave magnitude(M_S), however moment magnitude(M_W) has beencommonly used in latest seismic activity models andrecommended as a recognized magnitude standard worldwide. Therefore, it is necessary to fit the conversion relationship between surface wave magnitude of China and moment magnitude. Taking China's seas and neighboring regions as the study area, we collected the earthquake records from 1988 to 2020 with surface wave magnitudes from the China Earthquake Networks Center(CENC) and moment magnitudes from the Global Centroid Moment Tensor(GCMT). Based on the dataset, the year, depth, longitude, latitude, and M_S of CENC were extracted as impact factors, and the corresponding M_W of GCMT were used as markers. Then aBP neural network was trained to establish a moment magnitude conversion model. Meanwhile, least square regression and orthogonal regression were also applied to buildtraditionallinear models for comparisons. The results show that mean absolute error and root mean square error of least square regression and orthogonal regression are about 40% higher than that of BP neural network. Furthermore, the residual error of our proposed method has smaller absolute value and more concentrated distribution.

P315

国家重点研发项目（2017YFC1500402）、中国地震局地质研究所基本科研业务专项（IGCEA1823、IGCEA1901）共同资助