Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

Abstract

ABSTRACT Geographical information systems (GIS) are essential tools for mineral prospectivity modeling (MPM). Three-dimensional (3D) MPM is able to learn the association between geological evidence and mineralization in shallow zones and thereby build a prospectivity model for deep zones, making it a desirable technique to target deep-seated orebodies. However, existing 3D MPM methods directly generalize the model learned in shallow zones to the deep zones without attention to model transferability caused by the different metallogenic mechanisms between the two zones. In this study, we aim to robustly transfer the prospectivity model learned from shallow zones to deep zones. We cast the 3D MPM as a domain adaptation problem, which is an important realm of transfer learning. Because the metallogenic mechanism can be closely associated with spatial locations, we specifically focus on domain adaption concerning the spatial locations that are ignored by conventional domain adaptation methods. To measure the spatial-associated domain discrepancy, we propose a novel spatial-associated maximum mean discrepancy (SAMMD), which compares the joint distributions of features and spatial locations across domains. Based on the SAMMD criterion, a deep neural network, referred to as the spatial-associated domain adaptation network, is devised to learn cross-domain but mineralization-indicative features for building prospectivity model that is transferable to deep zones. A case study of the world-class Sanshandao gold deposit, in eastern China, was carried out to validate the effectiveness of the proposed methods. The results show that compared with other leading MPM methods and other domain adaption variants, the proposed method has superior prediction accuracy and targeting efficiency, demonstrating the effectiveness and robustness of the proposed method in targeting deep-seated orebodies in areas with different metallogenic mechanisms and no labeled data.