## 1 Introduction  
A pay zone is a section of a reservoir that contains economically recoverable hydrocarbons. Finding the pay zone is critical for determining the optimal position of the production well. These can help to mitigate the uncertainty and risk associated with drilling a new well. However, pay zone determination is often carried out at the well log scale using petrophysical analysis. Meanwhile, only seismic data are available during the early stages of investigation.  
A direct hydrocarbon indicator (DHI) is a high-amplitude seismic response anomaly generated by the presence of hydrocarbons [1]. These phenomena occur because of the presence of gas, which is significantly more compressible than brine and, hence, decreases its bulk modulus. DHI responses are most frequently observed in gas saturated sand; however, oil can also exhibit DHI responses. The high amplitude is often triggered by a quick decrease in impedance. However, the presence of hydrocarbons does not always impact DHI anomalies. To determine the presence of DHI, AVO analysis and characteristics are typically used. Nonetheless, there are various difficulties associated with these DHIassessments, one of which is that low gas saturation typically exhibits equivalent reactions to high saturation gas [2]. Additionally, this procedure demands considerable effort and experience, not to mention the subjectivity of the analysis.  
Unsupervised learning has been commonly used in the geoscience field of research in recent years because of its ability to find new patterns and information from the combination of features, which is typically met with unlabeled data problems. This method may be used as a substitute or a solution in the absence of prior information to identify the pay zone during the pre-drilling exploration phase. Unsupervised learning approaches are being used in geoscience to assist AVO cross-plotting in characterizing its background trend [3]. Another example is that unsupervised learning can make recommendations about which attributes to use in multi-attribute analysis [4; 5]. Unsupervised learning can also be utilized to detect the geological depositional environment [6; 7].  
Since there is still a lack of labelling and prior information in the early stage of exploration, applying unsupervised learning would be suitable as an early detection technique to detect the presence of hydrocarbons when no well is accessible. Where supervised machine learning needs labels to train the model, unsupervised machine learning does not need labels to train the model. Unsupervised learning allows us to identify unrecognizable patterns and detect anomalies, providing more understanding through the datasets. The model may be able to reduce the amount of time required for the analysis or interpretation that is typically performed by humans by combining the available AVO features to provide more objective analysis to assist in seismic interpretation and zone delineation [8; 9].  
This research provides an alternative way of delineating hydrocarbons from a conventional method, where several AVO attributes are being analyzed independently and manually. This will result in potential subjectivity and bias in the analysis. Meanwhile, by using our proposed method, the SOM selected and combined several AVO attributes and specified to delineate specific clusters, and, in this research, it is the anomaly of the AVO attributes that is related to the indication of hydrocarbons.  
This research's workflow can be utilized for other potential anomaly detection problems. For example, it can be applied to detect the overpressure zone before drilling by using multiple seismic attributes [10]. Other potential research on detecting the leakage of CO2 storage monitoring from seismic data can also be done by using this workflow [11].