In marine seismic sections, features like “chimneys”, characterized by low similarity chaotic zones or vertical disordered characteristics arise due to migration of gas. Numerous interpretation tools have been developed for the identification of chimney features from seismic data. Here, a study over a 2D seismic reflection profile, acquired by CSIR-National Geophysical Research Institute (NGRI), Hyderabad in the Krishna-Godavari (KG) basin in 2010 was carried out. The data is optimally conditioned or post-processed before extracting the attributes such as the similarity, dip variance, energy, and frequency washout, to capture the chimneys on seismic data. Finally, the attributes are combined and trained over interpreter’s acquaintances through a non-linear multi-layer perceptron (MLP) neural network to compute a hybrid attribute, defined as the chimney meta-attribute. The neural training results into an overall normalized root mean squared (RMS) error of 0.6 to 0.7 between the train and test data sets. Three chimneys of ∼1.5, ∼0.9, ∼1.3 km heights are observed over the seismic line. As chimneys represent gas migration paths, the computation of chimney attribute helps in understanding the genesis of gas-hydrates not only in the KG basin, studied here, but also in any other prospective basin.

在海洋地震剖面中，由于天然气的运移，出现了诸如“烟囱”之类的特征，其特征是低相似的混沌带或垂直无序特征。已经开发了许多解释工具，用于从地震数据中识别烟囱特征。在这里，进行了一项二维地震反射剖面的研究，该地震剖面是由CSIR国家地球物理研究所（NGRI）在2010年在克里希纳-戈达瓦里（KG）盆地的海得拉巴进行的。在提取属性（例如相似性，倾角方差，能量和频率冲刷）之前，对数据进行最佳条件处理或后处理，以捕获地震数据上的烟囱。最后，通过非线性多层感知器（MLP）神经网络对属性进行组合，并在口译人员的熟识中对其进行训练，以计算混合属性，定义为烟囱元属性。神经训练导致训练和测试数据集之间的总体归一化均方根（RMS）误差为0.6到0.7。在地震线上观测到三个分别为〜1.5，〜0.9，〜1.3 km高的烟囱。由于烟囱代表了气体的迁移路径，烟囱属性的计算不仅有助于了解此处研究的KG盆地中的天然气水合物的成因，而且还有助于理解任何其他潜在盆地中的天然气水合物的成因。