Huicong Mou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Huicong Mou has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Cai Jia
School of Geography and Tourism, Anhui Normal University, Huajin Campus, South 189 Jiuhua Rd, Wuhu 241002, China
1 shared publication - Haiyang He
School of Resource & Environment and Safety Engineering, University of South China, Hengyang 421001, China
1 shared publication - Wenru Hao
School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
1 shared publication - Xiaole Qiu
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
1 shared publication - Xiaoqiang Zhu
Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China
1 shared publication - Yongjian Gu
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
1 shared publication
Latest Publications
- Open Access
Mineral Prospectivity Mapping of the Kalatag Cu–Polymetallic District, Eastern Tianshan, NW China, Using a Maximum Entropy Model
The Kalatag Cu–polymetallic district in the Eastern Tianshan Orogen, NW China, is characterized by complex tectono-magmatic evolution and diverse mineralization styles, including porphyry-skarn, hydrothermal-vein, and volcanogenic massive sulfide (VMS) systems. However, only nine verified mineral occurrences were available for calibration of the present regional prospectivity model. This small occurrence dataset, together with the heterogeneous spatial distribution of geological and geochemical indicators, poses a challenge for regional prospectivity mapping. In this study, a Maximum Entropy (MaxEnt) model was developed to evaluate Cu–polymetallic prospectivity using presence-only mineral occurrence data. Seven rock-debris geochemical predictors, including Cu, Zn, Au, Mo, Ag, Ni, and Sb, were selected based on statistical characteristics and metallogenic relevance, while two geological predictors representing proximity to faults and intrusions were derived from regional geological maps. Nine-fold leave-one-out cross-validation (LOOCV) was used to evaluate out-of-sample discrimination and model sensitivity to regularization. The interpretation-oriented RM = 0.5 configuration yielded a mean LOOCV test AUC of 0.665, whereas the final model fitted using all nine verified occurrences achieved a training AUC of 0.850. High-prospectivity zones occupied 12.20% of the study area and contained 88.89% of known mineral occurrences. Jackknife analysis and response curves indicate that distance to intrusions, distance to faults, and Au anomalies provide the strongest independent spatial information within the predictor system. Prospectivity is generally enhanced near fault zones and intrusive contacts, highlighting the importance of tectono-magmatic architecture in controlling the spatial distribution of Cu–polymetallic mineralization. The resulting prospectivity map identifies regional high-prospectivity zones where favorable geological and geochemical conditions coincide, providing a quantitative basis for exploration prioritization. The study indicates that MaxEnt is an effective approach for integrating multisource geoscientific data under small-sample and presence-only conditions, while emphasizing that model outputs require further validation through geological, geochemical, geophysical, and drilling investigations.
Open AccessMineral Prospectivity Mapping of the Kalatag Cu–Polymetallic District, Eastern Tianshan, NW China, Using a Maximum Entropy Model
The Kalatag Cu–polymetallic district in the Eastern Tianshan Orogen, NW China, is characterized by complex tectono-magmatic evolution and diverse mineralization styles, including porphyry-skarn, hydrothermal-vein, and volcanogenic massive sulfide (VMS) systems. However, only nine verified mineral occurrences were available for calibration of the present regional prospectivity model. This small occurrence dataset, together with the heterogeneous spatial distribution of geological and geochemical indicators, poses a challenge for regional prospectivity mapping. In this study, a Maximum Entropy (MaxEnt) model was developed to evaluate Cu–polymetallic prospectivity using presence-only mineral occurrence data. Seven rock-debris geochemical predictors, including Cu, Zn, Au, Mo, Ag, Ni, and Sb, were selected based on statistical characteristics and metallogenic relevance, while two geological predictors representing proximity to faults and intrusions were derived from regional geological maps. Nine-fold leave-one-out cross-validation (LOOCV) was used to evaluate out-of-sample discrimination and model sensitivity to regularization. The interpretation-oriented RM = 0.5 configuration yielded a mean LOOCV test AUC of 0.665, whereas the final model fitted using all nine verified occurrences achieved a training AUC of 0.850. High-prospectivity zones occupied 12.20% of the study area and contained 88.89% of known mineral occurrences. Jackknife analysis and response curves indicate that distance to intrusions, distance to faults, and Au anomalies provide the strongest independent spatial information within the predictor system. Prospectivity is generally enhanced near fault zones and intrusive contacts, highlighting the importance of tectono-magmatic architecture in controlling the spatial distribution of Cu–polymetallic mineralization. The resulting prospectivity map identifies regional high-prospectivity zones where favorable geological and geochemical conditions coincide, providing a quantitative basis for exploration prioritization. The study indicates that MaxEnt is an effective approach for integrating multisource geoscientific data under small-sample and presence-only conditions, while emphasizing that model outputs require further validation through geological, geochemical, geophysical, and drilling investigations.