Ground motion models (GMMs) are critical for seismic risk mitigation and infrastructure design. Machine learning (ML) is increasingly applied to GMM development due to expanding strong motion databases. However, existing ML-based GMMs operate as 'black boxes,' creating opacity that undermines confidence in engineering decisions. Moreover, seismic datasets exhibit severe imbalance, with scarce large-magnitude near-field records causing systematic underprediction of critical high-hazard ground motions. Despite these limitations, research addressing both interpretability and data imbalance remains limited. This study develops an inherently interpretable neural network employing independent additive pathways with novel HazBinLoss and concurvity regularization. HazBinLoss integrates physics-constrained weighting with inverse bin count scaling to address underfitting in sparse, high-hazard regions. Concurvity regularization enforces pathway orthogonality, reducing inter-pathway correlation. The model achieves robust performance: mean squared error = 0.6235, mean absolute error = 0.6230, and coefficient of determination = 88.48%. Pathway scaling corroborates established seismological behaviors. Weighted hierarchical Student-t mixed-effects analysis demonstrates unbiased residuals with physically consistent variance partitioning: sigma components range from 0.26-0.38 (inter-event), 0.12-0.41 (inter-region), 0.58-0.71 (intra-event), and 0.68-0.89 (total). The lower inter-event and higher intra-event components have implications for non-ergodic hazard analysis. Predictions exhibit strong agreement with NGA-West2 GMMs across diverse conditions. This interpretable framework advances GMMs, establishing a transparent, physics-consistent foundation for seismic hazard and risk assessment.

翻译：地震动模型（GMMs）对于地震风险缓解和基础设施设计至关重要。随着强震数据库的不断扩展，机器学习（ML）在地震动模型开发中的应用日益增多。然而，现有的基于机器学习的地震动模型以'黑箱'方式运行，其不透明性削弱了工程决策的可信度。此外，地震数据集表现出严重的不平衡性，大震级近场记录稀缺导致对关键高危害地震动的系统性低估。尽管存在这些局限性，同时解决可解释性和数据不平衡问题的研究仍然有限。本研究开发了一种固有可解释的神经网络，采用具有独立加性路径的新型HazBinLoss和共曲率正则化方法。HazBinLoss将物理约束加权与逆箱计数缩放相结合，以解决稀疏高危害区域的欠拟合问题。共曲率正则化强制路径正交性，降低路径间相关性。该模型实现了稳健的性能：均方误差=0.6235，平均绝对误差=0.6230，决定系数=88.48%。路径缩放验证了既定的地震学行为。加权分层Student-t混合效应分析显示残差无偏，且方差分配具有物理一致性：sigma分量范围分别为0.26-0.38（事件间）、0.12-0.41（区域间）、0.58-0.71（事件内）和0.68-0.89（总方差）。较低的事件间分量和较高的事件内分量对非遍历性危害分析具有重要影响。预测结果与NGA-West2地震动模型在各种条件下表现出高度一致性。这一可解释框架推动了地震动模型的发展，为地震危害和风险评估建立了透明且物理一致的基础。