用迭代变分自编码器修复深海海啸监测数据中的异常，提升地球重力场研究精度。
Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation
- 通过迭代编码解码结构逐步清除数据异常，保留原始信号特征。
- 相比传统方法，在去除尖峰和缓慢漂移时更有效，保持关键海洋特性。
- 适合需要高精度时间序列的气候与地震监测研究者使用。
美国国家海洋和大气管理局（NOAA）的深海海啸评估与报告系统（DART）数据对NASA-JPL的海啸探测、实时运行及海洋学研究至关重要。然而，这些时间序列常含尖峰、阶跃和漂移等异常，降低数据质量并掩盖关键海洋特征。本文提出一种迭代编码-解码变分自编码器（Iterative Encoding-Decoding VAEs）模型，用于提升DART时间序列质量。该模型通过逐轮重构逐步剔除异常，同时保留数据的潜在结构，避免传统滤波和阈值法对信号特征的破坏。结合混合阈值策略，可有效保护边界区域的真实海洋学特征。在复杂DART数据集上的实验表明，该方法生成的重构结果比经典统计技术更准确地维持关键海洋属性，对尖峰去除和微小阶跃变化具有更强鲁棒性。高质量数据为NASA-JPL的GRACE-FO任务提供有力支持，确保表面重力测量精度，助力地球重力场与全球水循环建模。最终，该方法显著增强海啸预警能力，并为未来气候模拟提供更高可解释性与可靠性基础。
原文摘要 · Abstract (English)
NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) data are critical for NASA-JPL's tsunami detection, real-time operations, and oceanographic research. However, these time-series data often contain spikes, steps, and drifts that degrade data quality and obscure essential oceanographic features. To address these anomalies, the work introduces an Iterative Encoding-Decoding Variational Autoencoders (Iterative Encoding-Decoding VAEs) model to improve the quality of DART time series. Unlike traditional filtering and thresholding methods that risk distorting inherent signal characteristics, Iterative Encoding-Decoding VAEs progressively remove anomalies while preserving the data's latent structure. A hybrid thresholding approach further retains genuine oceanographic features near boundaries. Applied to complex DART datasets, this approach yields reconstructions that better maintain key oceanic properties compared to classical statistical techniques, offering improved robustness against spike removal and subtle step changes. The resulting high-quality data supports critical verification and validation efforts for the GRACE-FO mission at NASA-JPL, where accurate surface measurements are essential to modeling Earth's gravitational field and global water dynamics. Ultimately, this data processing method enhances tsunami detection and underpins future climate modeling with improved interpretability and reliability.
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