The Wulong gold mine in Liaodong has been mined for more than 80 years，the reserve of mining resources has seriously declined，and the problem of insufficient reserve resources is particularly prominent.It is urgent to identify the deep and peripheral mineral resources in the mining area.Through the measurement of transient electromagnetic method（TEM）in the northwest and southeast sides of the v163 ore vein and the south side of the No.100 fault，the corresponding 2D and 3D geoelectric models were constructed，and the charac-teristics of low resistance anomalies in the mining area were analyzed.Combined with previous achievements，the genetic types of gold deposits and the next exploration direction were inferred.The results show that the low resistance area on the P0 section in the northwest direction of the v163 ore vein is caused by a group of fine-grained diorite veins，including the northwest section of the v163 ore vein，which is highly likely to belong to the quartz vein type mineralization.The low resistance area in the 3D geoelectric model on the south side of the No.100 fault corresponds to the Dagudingzi gold deposit.The deep scale of the low resistance area on the north side increases，and the anomaly is not closed，indicating that the deep part of the north side of the area has a good prospecting prospects and it is worthy of further work.In the 3D geoelectric model in the southeast direction of the v163 ore vein，the low resistance anomaly value is significantly lower in magnitude than the former two，suggesting that it is caused by an alteration zone.After the engineering verification hole，it is revealed that there are multiple layers of discontinuous silicified altered rocks vertically in this area，with a maximum thickness of 41 m.The alteration types include silicification，sericitization，chloritization，and varying degrees of pyrite mineralization.This research indicates that there are not only quartz vein type gold orebodies in the Wulong gold deposit，but also altered rock type gold，which may be the main direction of the next pros-pecting in the mining area.

During deep tunnelling using drill-and-blast method，excavation damaged zone （EDZ） is inevitably induced in surrounding rocks due to the coupled impacts of blast loading and dynamic initial stress unloading and thus affect the structure stability.Therefore，it is very important to predict EDZ depth before roadways excavation.Relying on the field measurements of EDZ in several underground mines as the research object，300 data samples were collected.Four mainstream hyperparametric optimization algorithms，i.e.，genetic algorithm （GA），gray wolf optimization algorithm（GWO），particle swarm optimization algorithm（PSO），and salp swarm algorithm （SSA），were used to optimize the XGBoost algorithm and to construct four hybrid models for EDZ prediction.Comparative analysis of predictive model performance was conducted in terms of R²，RMSE，MAE and MAPE，along with a sensitivity analysis of the influencing parameters.Finally，the optimal PSO-XGBoost model was applied to a transportation roadway in an underground mine for engineering validation.The results show that the GA-XGBoost，GWO-XGBoost，PSO-XGBoost，and SSA-XGBoost models achieve the best predictive performance with swarm sizes of 90，70，60 and 100，respectively.Among them，the PSO-XGBoost model demonstrates the best predictive performance with correlation coefficients of 0.9244 and 0.8787 in the training and testing sets，respectively.Moreover，compared to bench models（XGBoost，RF，SVM and LightGBM），both the prediction accuracy and stability of the optimized models are improved.The tunnel diameter（TD） and rock mass geological strength index（GSI） have the most significant influence on the loosened zone thickness，along with a noticeable impact from the vertical principal stress.The application results of the optimized XGBoost model in practical engineering show that the error between the measured value and the predicted value is within 10%，indicating that the PO-XGBoost is of significance for engineering application.