Anomalies Detection in Metocean Simulation Results Using Convolutional Neural Networks

Abstract

Metocean modeling with short output fields timestep, for example, hourly average fields output, generates a large number of pictures and requires extended computational power. Often, during the simulation different types of artifacts can appear due to the inappropriate model tuning or errors in boundary and initial data and, therefore, expert’s supervision and validation are required. When the number of images is increasing it becomes difficult or even impossible to check all output images manually. Therefore, it is required to use machine learning algorithms to reduce a time for expert’s validation. Thereby, it would be useful to develop a system that allows detecting anomalies in generated data automatically during the experiment. In the paper, we provide a method of anomalies detection for the geospatial data. Data in climatographic archives is available in restricted amount and therefore, full Arctic images are divided into sub-zones, which allows one to increase training set. Moreover, this division can be used to account for the spatial dependency, which is required for ice images. An advantage of the approach is the ability to detect anomalies completely in automatic mode without involving a domain expert and manual labeling.