We present a procedure for producing a Bayesian DHI for low frequency passive seismic (LFPS) data. The approach utilizes<br>two LFPS attributes to classify and determine the likelihood of hydrocarbon presence in the subsurface. The attributes are<br>based on strength and variability of the empirically observed hydrocarbon tremor. An improved, more robust tremor energy<br>measure based on the temporal characteristics of the signal is presented and used. An interpreter-driven Bayesian<br>classification is employed both to accommodate uncertainties in the data and to provide a risk estimate. Prior knowledge<br>from wells or structural information from active seismic can be incorporated into the analysis through interpretative<br>interaction.


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