The present study aims at developing a prediction model for ground motion intensity measures using the artificial neural network (ANN) technique for active shallow crustal earthquakes in India. The database for the study consists of 659 ground motion records collected from 138 earthquakes recorded by various seismic networks in the study region. Owing to the lack of near-field data, we have added 116 records from seven earthquakes over a distance < 30 km and M > 6 from the NGA database. The developed model predicts 21 ground motion parameters (GMPs) in both horizontal and vertical directions, with input predictor variables of magnitude (M), hypocentral distance (R), site condition (S), and flag for the region (f). A multi-layer perceptron (MLP), with a total of 276 unknowns, constitutes the architecture of the model. The residuals associated with the GMPs are analyzed in detail to aid in hazard calculations. In addition, a comparison of the developed model with global relations is performed. Further, the model is demonstrated by performing seismic hazard analysis for GMPs for 2% and 10% probability of exceedance in 50 years. The ANN model is a first version and has to be improved as more strong motion data becomes available for the region. The developed ground motion model must be combined along with other global models in seismic hazard analysis.

本研究旨在为印度活动浅层地壳地震开发一个使用人工神经网络（ANN）技术的地面运动强度测度的预测模型。该研究的数据库包含从138个地震中收集的659个地面运动记录，这些地震是由研究区域的各种地震网络记录的。由于缺乏近场数据，我们 从NGA数据库中添加了来自30个距离<30 km和M > 6的7次地震的116条记录。所开发的模型可预测水平和垂直方向的21个地面运动参数（GMP），其中输入预测变量的大小（M），下心距（R），工地条件（S）和区域标志（f）。总共有276个未知数的多层感知器（MLP）构成了模型的体系结构。详细分析了与GMP相关的残留物，以帮助进行危害计算。另外，将开发的模型与全局关系进行比较。此外，该模型通过对GMP进行地震危险性分析来证明，其在50年内的超标概率为2％和10％。ANN模型是第一个版本，必须随着该区域可获得更强大的运动数据而加以改进。在地震灾害分析中，必须将开发的地面运动模型与其他全局模型结合起来。