Drilling and blasting is still the most efficient way to explore deep phosphate mine excavation and mining. There is a severe constraint on the efficiency of phosphate mine digging as its level remained at 70 to 80 meters every month for many years. Therefore, the ore rock blastability classification is critical for the deep phosphate mine working face. The longitudinal wave velocity tests of the rock body in an underground phosphate mine in Yichang, Hubei Province, and measurements of physical and mechanical properties such as rock density, uniaxial compressive strength and tensile strength were carried out. The rock density, uniaxial compressive strength, tensile strength, and rock integrity coefficient were obtained for four types of rocks, namely, dolomitic striped phosphorite, dense striped phosphorite, argillaceous striped phosphorite, and carbon-bearing argillaceous dolomite. To complete the deep phosphorite workings of the mine rock blastability classification, a BP neural network model was established by stochastic functions to generate a large number of learning and testing samples using the Matlab neural network toolbox as taking the pre-measured rock density, uniaxial compressive strength, tensile strength and rock integrity coefficients as inputs and the rock blastability classification as outputs. The grading results show that dolomite-banded phosphorite and mud-banded phosphorite are moderately blastable, and dense-banded phosphorite and carbonaceous mud dolomite are difficult to blast. According to the classification results, the blasting parameters of the stope can be optimized to enhance the blasting effect, reduce the single consumption and the bulk rate of explosives, and improve the safety and economic benefits of deep phosphate mining.

Rock blastability classification is a prerequisite for determining labour quotas, designing blasting programmes and controlling the unit consumption of explosives. In order to realize a real-time grading of rock explodability, a measurement of in-situ drilling parameters of carbon-bearing muddy dolomite during the excavation process of $P{h}_1^3$ ore body and roadway in the Shukongping phosphorus mine has been carried out based on the KJ212-1 full-hydraulic boring drilling truck. Combined with the indoor uniaxial compressive strength test, the relationship model between the uniaxial compressive strength R_c and the drilling speed V, the drilling hole diameter D and the rotary pressure M was respectively established and verified. Finally, the model is substituted into the solidity coefficient f relationship equation to derive a model for the relationship between the blasthole drill-following parameters and the rock blastability classification. The results of the study show that the average rate of difference between the uniaxial compressive strength calculated by the relational model and the results of the indoor uniaxial compression tests is 5.5%, which demonstrates the reasonableness of applying the model to the real-time prediction of rock blastability. This model provides a more convenient and fast method for real-time grading prediction of rock blastability. The results show that the dolomitic banded phosphorite, mud banded phosphorite and dense banded phosphorite are medium explosive, carbon-bearing mud dolomite is difficult to explode.