甚于集成fl子神经网络的大地构造环境判別与分析

Quantum geoscience represents a cutting-edge interdisciplinary field that leverages quantum computing and quantum machine learning algorithms to revolutionize the analysis of geological data. Among these advancements, the quantum neural network stands out for its efficiency and accuracy in processing complex multi-source data. This study focuses on addressing the challenge of discriminating tectonic settings, enhancing the quantum neural network (S-QNN) with an ensemble strategy to differentiate between basalt, gabbro, and spinel settings. Comparative analyses are conducted with four traditional algorithms (SVM. RF. KNN. NB). artificial neural network (ANN) . and traditional quantum neural network (QNN). Results demonstrate that the S-QNN model outperforms the optimal traditional algorithm by 5.67%, 6.19%, and 13.34% in the respective cases, and surpasses the QNN by 3.11%, 4.99%, and 3.84%. The S-QNN model exhibits robustness and versatility, highlighting its superiority in data processing. This study underscores the potential of quantum machine learning algorithms in geoscience research, showcasing the advantages of S-QNN and paving the way for innovative integration of quantum science and geoscience.