利用本福特定律进行地震检测
Earthquake Detection Using Benford's Law
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中文总结 AI 辅助
本研究将本福特定律作为统计框架，分析印度次大陆两类构造环境的地震数据，提出滑动窗口结合自适应归一化的方法，实现无需训练的高效本地地震检测，性能受窗口长度影响。
中文摘要 AI 辅助
可靠检测本地地震并准确识别P波初至是地震学的基础任务，但许多现有方法需要大量参数调整或大型训练数据集。本研究探讨本福特定律——一种控制自然数据中首位数字出现的对数分布——作为本地地震检测统计框架的适用性。我们分析印度次大陆两个不同构造环境的连续地震波形数据：板内的德干火山省和活跃变形的喜马拉雅地区。采用滑动窗口方法，我们量化地震振幅首位数字分布与本福特定律的时间一致性，并应用自适应归一化方案以考虑台站特定噪声。结果显示，本地地震波形在地震能量初至期间始终符合本福特定律，而事件前噪声则不符合。本福特衍生的统计异常与理论P波到达时间高度吻合，检测性能主要受窗口长度控制。这些结果确立本福特定律为一种计算成本低、参数需求少且无需训练的本地地震检测工具。
英文摘要
Reliable detection of local earthquakes and accurate identification of P-wave onsets are fundamental tasks in seismology, yet many existing methods to accomplish them require extensive parameter tuning or large training datasets. In this study, we investigate the applicability of Benford's Law - a logarithmic distribution governing the occurrence of leading digits in naturally occurring data - as a statistical framework for local earthquake detection. We analyze continuous seismic waveform data from two contrasting tectonic environments in the Indian subcontinent: the intraplate Deccan Volcanic Province and the actively deforming Himalayan region. Using a sliding-window approach, we quantify the temporal conformity of first-digit distributions of seismic amplitudes to Benford's Law and apply an adaptive normalization scheme to account for station-specific noise. Our results show that local earthquake waveforms consistently conform to Benford's Law during the onset of seismic energy, while pre-event noise does not. Benford-derived statistical anomalies align closely with theoretical P-wave arrival times, with detection performance primarily controlled by window length. These results establish Benford's Law as a computationally inexpensive, parameter-light, and training-free tool for local earthquake detection.
