地震预测领域终于有了能捕捉空间异质性的神经网络方法，做灾害风险评估的团队可以直接用分位数构建警报，比传统全局假设模型更准。
传统地震预测模型假设泊松分布且全局离散度一致，但中亚地震数据（2010-2024）强烈拒绝该假设（p<10^{-179}）。本研究提出 EarthquakeNet 架构，通过神经网络（空间嵌入+MLP）内生估计每个网格的过离散参数 alpha，无需显式空间协方差设定。相比传统负二项回归假设全局 alpha，该模型能识别地震聚集的空间异质性，并通过预测分布分位数构建概率风险警报。2018-2023 年滚动评估显示，平均引脚偏差（MPD）比负二项 GLM 基线降低 8.6%，在极端事件（Y>=5）的连续排名概率分数（CRPS）降低 12.5%。
Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment
Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010-2024), where a likelihood-ratio test with boundary correction strongly rejects the Poisson hypothesis (p < 10^{-179}). The main contribution of this work is the EarthquakeNet architecture, which provides an endogenous per-cell estimate of the overdispersion parameter alpha via a neural network (spatial embeddings + MLP), without explicit spatial covariance specification. In contrast to existing negative binomial regression approaches in seismological forecasting, which typically assume a single global alpha, the proposed per-cell formulation allows the model to identify spatial heterogeneity in seismic clustering and to construct probabilistic risk-aware alerts via quantiles of the predicted distribution. A walk-forward evaluation (2018-2023) over four systems shows an 8.6 percent reduction in mean pinball deviation (MPD) relative to a negative binomial GLM baseline. The strongest improvements are observed in the tail regime (Y >= 5), where the continuous ranked probability score (CRPS) of the proposed model is 12.5 percent lower than that of the baseline, indicating improved calibration in extreme-event forecasting.