异常检测是识别数据集中异常实例或事件的过程，这些情况偏离了规范。在本研究中，我们提出了一种基于机器学习算法的签名，以检测给定数据集的稀有或意外项目。我们将签名或随机签名的应用作为异常检测算法的特征提取器;此外，我们为随机签名构建提供了简单的，表示的理论理由。我们的第一个申请基于合成数据，旨在区分股票价格的实际和假轨迹，这是通过目视检查无法区分的。我们还通过使用加密货币市场的交易数据来显示实际应用程序。在这种情况下，我们能够通过无监督的学习算法识别在社交网络上组织的泵和转储尝试，该算法高达88％，从而实现了靠近现场最先进的结果基于监督学习。

With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO2 plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO2 plumes by leveraging Class Activation Mapping methods.