Several major earthquakes have jolted Pakistan during the last 30 years, destroyed infrastructure and severe damage to the economy. Despite advancement in data sciences and its linkages with other domains, no such study has been conducted in Pakistan, which incorporates the application of machine learning techniques on ambient noise data. This study presents the application of machine learning techniques on the ambient noise data to establish the micro-zones within the urban settlements of northern Potwar region of Pakistan, based on the site response parameters and seismic vulnerability. The ambient noise data from 148 sites in the study area, are acquired, processed and interpreted. Different clustering techniques namely, K-mean, fuzzy c-means and hierarchical clustering have been applied to the interpreted ambient noise data set. Arc GIS maps of the study area have been developed by making use of the interpretation of the ambient noise data and the resulted clustering solutions. The results showed that the fundamental frequency f₀ ranges between 0.5 and 15 Hz, the H/V spectral amplification factor ranges between 0.8 and 5.9; the soft sediment thickness ranges from 1.6 to 316 m, whereas, the soil vulnerability index is observed between 0.1 and 63. These site response parameters indicated that the study area is moderate to highly vulnerable to site amplification, and any seismic event can lead to catastrophe within the study area. The clustering techniques also detected three groups from the interpretation of the ambient noise data set by separating the locations according to their vulnerability due to site amplification. The quality of cluster solutions was evaluated using cluster validity indexes and the results of these techniques were compared. These results present the similarities and dissimilarities among different sites and indicate the sites which are geographically distant but have very similar vulnerability characteristics or vice versa. The Arc GIS tool showed the spatial distribution of site response parameters and three zones were established as zone 1, zone 2 and zone 3 with low, intermediate and high values, respectively. The spatial distribution maps showed that the northeastern and northwestern parts of the study area are more vulnerable to site amplification.

在过去的30年中，巴基斯坦发生了几次大地震，破坏了基础设施并严重破坏了经济。尽管数据科学及其与其他领域的联系取得了进步，但巴基斯坦尚未进行过此类研究，该研究将机器学习技术应用于环境噪声数据中。这项研究基于场地响应参数和地震易损性，介绍了机器学习技术在环境噪声数据上的应用，以在巴基斯坦北部Potwar地区的城市居民区内建立微区。采集，处理和解释了研究区域中148个站点的环境噪声数据。已将不同的聚类技术（即K均值，模糊c均值和分层聚类）应用于已解释的环境噪声数据集。通过利用对环境噪声数据的解释以及由​​此产生的聚类解决方案，开发了研究区域的Arc GIS地图。结果表明，基本频率f₀范围在0.5到15 Hz之间，H / V频谱放大系数在0.8到5.9之间；软沉积物厚度在1.6到316 m之间，而土壤脆弱性指数在0.1到63之间。这些场地响应参数表明研究区域是中等到高度易受场地放大的影响，任何地震事件都可能导致灾难在研究区域内。聚类技术还根据环境噪声数据集的解释，通过根据位置放大导致的脆弱性将位置分开来检测出三组。使用聚类有效性指标评估聚类解决方案的质量，并比较这些技术的结果。这些结果显示了不同站点之间的相似性和相异性，并指出了地理上相距较远但具有非常相似的脆弱性特征的站点，反之亦然。Arc GIS工具显示了站点响应参数的空间分布，并建立了三个区域，分别是区域1，区域2和区域3，分别具有低，中和高值。空间分布图显示，研究区域的东北部和西北部更易受站点放大的影响。