Using an innovative workflow incorporating microseismic attributes and geomechanical well logs, we have defined major geomechanical drivers of microseismic expression to understand reservoir stimulation response in an engineering/geologic context. Microseismic data from a hydraulically fractured Marcellus well in the Appalachian Basin, central Pennsylvania, were sampled vertically through the event cloud, crossing shale, limestone, sandstone, and chert. We focused our analysis on the Devonian organic shale and created pseudologs of moment magnitude (Mw), b-value, and event count. The vertical moving-average sampling of microseismic data was completed such that the sample interval matched that of the geophysical well logs. This technique creates robust, high-resolution microseismic logs that show subtle changes in microseismic properties and allow direct crossplotting of microseismic versus geophysical logs. We chose five geomechanical properties to form the framework against which to interrogate the microseismic data: Young's modulus (YM), Poisson's ratio (PR), brittleness, lambda-rho, and mu-rho. Additionally, we included natural gamma as a useful measure of organic content. Having defined this microseismic-geomechanical crossplot space, we derived insights into the response of these units during hydraulic fracturing. Observations include: (1) larger magnitude microseismicity occurs in high PR rocks, and high event counts are found in low PR rocks; (2) low b-value (high in situ stress) is consistent with the occurrence of larger magnitude events and low event counts; and (3) YM and brittleness act as bounding conditions, creating “sweet spots” for high and low Mw, event count, and stress. In our crossplot space, there is a meaningful link between microseismicity and the elastic properties of the host rock. In light of this dependence of stimulation potential on elastic properties, the calculation of microseismic pseudologs at stimulation sites and application of our crossplot framework for microseismic-geomechanical analysis in unconventional shale will inform operators in planning and forecasting stimulation and production, respectively.