# Machine Learning Reveals Migmatic Fertility of Skarn-Type Tungsten Deposits  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; [EMAIL_ADDRESS] (R-C.T. ); [EMAIL_ADDRESS] (Y-J.S. ); [EMAIL_ADDRESS] (Z-W.F. ); [EMAIL_ADDRESS] (H.-F.D. ); [EMAIL_ADDRESS] (Z-J.W. ); [EMAIL_ADDRESS] (K-.Q.X)1  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; [EMAIL_ADDRESS] (R-C.T. ); [EMAIL_ADDRESS] (Y-J.S. ); [EMAIL_ADDRESS] (Z-W.F. ); [EMAIL_ADDRESS] (H.-F.D. ); [EMAIL_ADDRESS] (Z-J.W. ); [EMAIL_ADDRESS] (K-.Q.X)1  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; [EMAIL_ADDRESS] (R-C.T. ); [EMAIL_ADDRESS] (Y-J.S. ); [EMAIL_ADDRESS] (Z-W.F. ); [EMAIL_ADDRESS] (H.-F.D. ); [EMAIL_ADDRESS] (Z-J.W. ); [EMAIL_ADDRESS] (K-.Q.X)1  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; [EMAIL_ADDRESS] (R-C.T. ); [EMAIL_ADDRESS] (Y-J.S. ); [EMAIL_ADDRESS] (Z-W.F. ); [EMAIL_ADDRESS] (H.-F.D. ); [EMAIL_ADDRESS] (Z-J.W. ); [EMAIL_ADDRESS] (K-.Q.X)1  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; [EMAIL_ADDRESS] (R-C.T. ); [EMAIL_ADDRESS] (Y-J.S. ); [EMAIL_ADDRESS] (Z-W.F. ); [EMAIL_ADDRESS] (H.-F.D. ); [EMAIL_ADDRESS] (Z-J.W. ); [EMAIL_ADDRESS] (K-.Q.X)1  
[PERSON]  
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha 410083, China; tanrichang@sina. intrusive rocks and specific magmatic minerals, such as apatite [4] and zircon [5], have been widely used to evaluate the metallogenic potential of skarn-related tungsten deposits. Although the indicative roles of these minerals have been preliminarily recognized, systematically quantifying magmatic fertility based on their chemical signatures remains a significant research focus and challenge [6]. Apatite is a common accessory mineral in igneous rocks, characterized by the incorporation of trace elements such as Sr, Y, Mn, V, and rare earth elements (REEs) [7; 8]. Due to its resistance to weathering and hydrothermal alteration [9], apatite is an ideal indicator mineral for tracing magmatic processes and sources, monitoring physicochemical conditions, and constraining magmatic crystallization ages [10; 11; 12; 13; 14]. Consequently, trace elements in apatite can effectively discriminate rock types [15], determine ore deposit genesis [4], and assess the metallogenic potential of host rocks [16; 17]. Nevertheless, conventional analytical approaches for interpreting high-dimensional chemical data of apatite are often constrained by subjective interpretations and data complexity, limiting the accuracy and universality of assessments for ore-forming potential. Machine learning (ML), the science of enabling computers to learn from data, has emerged as a powerful method for decoding hidden patterns within high-dimensional datasets. Recently, with improved computational capabilities and the accumulation of large datasets, ML techniques have become increasingly prominent in geological research, achieving notable successes in predicting crustal thickness [18], global climate reconstructions [19; 20], tectonic settings [21], magmatic fertility [22; 23; 24; 25], and multiscale mineral-prospectivity analysis. At the regional scale, three-dimensional convolutional neural networks applied to fused ASTER-Landsat imagery reduce false-positive Fe-skarn detections by >40% in eastern China [26].