论文标题
深度与浅学习：低级地震检测中的基准研究
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection
论文作者
论文摘要
虽然深度学习模型最近看到了地球科学的高度吸收，并且具有从最低限度处理的输入数据中学习的能力吸引人，因为黑匣子模型没有简单的方法来了解如何达到决策，而在安全至关重要的任务中，尤其是哪些尤其是有问题的。另一种途径是使用更简单，更透明的白色框模型，在该模型中，特定于任务的功能构建取代了在深度学习模型中自动执行的更不透明的功能发现过程。使用来自荷兰的Groningen气场的数据，我们通过在CATCH22时间序列分析软件包中使用弹性网驱动数据挖掘发现的另外四个功能添加了现有的逻辑回归模型。然后，我们评估了在Groningen数据上预先训练的深度（CNN）模型的增强逻辑回归模型的性能，该模型逐渐增加了噪声与信号比率。我们发现，对于每个比率，我们的逻辑回归模型正确地检测到了每场地震，而深层模型未能检测到近20％的地震事件，因此在应用深度模型的应用中至少谨慎地谨慎，尤其是对具有较高噪声比率比率的数据。
While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent white box models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, we build on an existing logistic regression model by the addition of four further features discovered using elastic net driven data mining within the catch22 time series analysis package. We then evaluate the performance of the augmented logistic regression model relative to a deep (CNN) model, pre-trained on the Groningen data, on progressively increasing noise-to-signal ratios. We discover that, for each ratio, our logistic regression model correctly detects every earthquake, while the deep model fails to detect nearly 20 % of seismic events, thus justifying at least a degree of caution in the application of deep models, especially to data with higher noise-to-signal ratios.
