Anomaly detection algorithm of single variable time-series data based on dynamic parametrization for subsurface fluid data anomaly detection Article Swipe

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Anomaly detection Anomaly (physics) Series (stratigraphy) Geology Time series Variable (mathematics) Geophysics Algorithm Data mining Seismology Computer science Mathematics Machine learning Physics Paleontology Condensed matter physics Mathematical analysis

Zhi Yang , Cheng Wang , Quancheng Huang , Liyun Fan , Xuehui Yang , Gaochuan Liu , Chunyan Shuai , Fang Yang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1093/gji/ggaf328 · OA: W4413744279

SUMMARY Due to the extremely destructive characteristics of seismic hazards, and as one of the effective pre-seismic physical signals, abnormal changes in subsurface fluids can provide key precursor information for earthquake prediction. Furthermore, an efficient method for labelling anomalies in seismic monitoring data is urgently needed. Therefore, this paper analyses the change characteristics of subsurface fluid-water level data and proposes an Anomaly Detection Algorithm of Single Variable Time-Series Data Based on Dynamic Parameter Tuning (ADSV-DPT) based on three important characteristics (jump, step and steep), which first determines the central tendency of the data by calculating the median of the water level data within the initial window, and then utilizes the Median Absolute Deviation (MAD) as a robust dispersion metric to reduce the impact of extreme values on the anomaly detection. The sliding window mechanism is employed to update the median and MAD step-by-step, thereby ensuring the efficiency and adaptability of the algorithm in processing time-series data. Finally, the anomalies in the data are detected by setting dynamic thresholds. A comparison of the anomaly detection efficacy of the proposed ADSV-DPT algorithm with that of three alternative models (namely, K-Nearest Neighbour, KNN; Pruned Exact Linear Time, PELT; and One-Class Support Vector Machine, OC-SVM) was conducted. The experimental results demonstrate that the ADSV-DPT algorithm outperforms the other models in accurately identifying anomalous features. The average precision, recall and F1-score of the ADSV-DPT algorithm all exceed 85 per cent. The algorithm's capacity for adapting to variations in the data is noteworthy, as is its ability to accurately identify abnormal values that deviate from the established normal range.