Objective Disaster risk identification and prediction serve as a prerequisite for disaster control. An engineering geological environment is identified as the fundamental condition inducing dynamic disasters like rock bursts in mines. Exploring the sedimentary genetic mechanisms of engineering geological environments holds great significance for predicting rock burst risks.

Methods With the Gaojiapu Coal Mine in the Binchang mining area, Shaanxi Province, as a case study, this study analyzed the engineering geological characteristics of rock masses under different sedimentary microfacies. Furthermore, it explored the deformation and failure characteristics of rocks in different sedimentary environments, along with the energy release patterns of rock masses.

Results and Conclusions The results indicate that the differences in sedimentary environments lead to different rock lithologies and microstructures. The energy evolutionary process during rock loading and failure can be roughly divided into three stages: energy dissipation fluctuation, energy dissipation stabilization, and energy dissipation. The differences in sedimentary microfacies result in significantly varying energy proportions of rocks in the energy dissipation stage. Specifically, fine- and medium-grained sandstones deposited in river channels, along with coarse-grained sandstones in mid-channel bars, contribute relatively more energy at rock failure, accounting for more than 24% of the total strain energy. In contrast, mudstones deposited in the flood plains contribute less energy, representing 14%. Accordingly, nonlinear identification models for rock burst risks were constructed using machine learning algorithms, as well as engineering geological environmental factors such as sedimentary facies, the thickness and burial depth of coal seams, the characteristic parameters of rock layer thickness of the roof, the thickness of hard rock layers in the roof and their distance from coal seams, parameters for quality assessment of rock masses, the capacity dimension of geological structures, the coefficient of lateral pressure, and elastic energy. The models built using four machine learning algorithms, namely backpropagation neural network (BPNN), support vector machine (SVM), decision tree (DT), and Bagging, were comparatively analyzed. They yielded accuracy, macro-F1 scores, and area UNDER the receiver operating characteristic (ROC) curve all exceeding 0.7, suggesting their high accuracy and stability. Moreover, the Bagging-based model outperformed the remaining models. The results demonstrate that rock burst risks can be accurately and effectively identified and predicted using engineering geological environmental factors. This study can provide a reference for the risk assessment of rock bursts in coal mines with similar geologic and mining conditions and offer guidance and a basis for the anti-rock burst pressure relief design for coal mines.