Machine Learning For DAS Microseismic Event Detection

Emerging acquisition systems based on fiber optic technologies such as Distributed Acoustic Sensing are enabling dense spatial and temporal sampling of strain fields. This has resulted in a large increase in the volume in the volume of data and the rate at which data is generated. These increases can be interpreted as satisfying two of the 3 ‘V's of ‘Big Data’ i.e. Volume and Velocity (the third V refers to data Variety). In this presentation we show how we have used big data technologies such as Apache Hadoop and Apache Spark to tackle these data issues for the specific problem of microseismic event detection. Furthermore, traditional approaches were thought to be unlikely to efficiently scale to these new data so we turned to machine learning approaches based on computer vision. Rather than trying state of the art technologies such as Convolutional Neural Nets we decided to try a mature technology used for face detection known as a Haar Cascade. We have tested this approach on field data and found that this approach can work well and are motivated to try newer machine learning techniques with the expectation of moving beyond microseismic event detection.