A tool for Swarm satellite data analysis and anomaly detection

In this work we introduce a system pipeline for the analysis of earth's electromagnetic field that is used to analyse precursors to earthquakes. Data gathered by the Swarm satellites are used to present the utility of our system. Our objective is to provide a streamlined method to analyze electromagnetic data over a region and investigate the relationship of precursory signals to seismic events. The process follows three distinct stages: data extraction, data pre-processing and anomaly detection. The first stage consists of the region selection and data extraction. The second stage consists of four different pre-processing methods that address the data sparsity problem and the cause of artificial anomalies. The last stage is the Anomaly Detection (AD) of the Swarm satellite data, over the investigated region. The different methods that are implemented are known to perform well in the field of AD. Following the presentation of our system, a case study is described where the seismic event of 6.2 Mw is in Ludian, China and occurred on 3rd August 2014. The event is used to present the usefulness of our approach and pinpoint some critical problems regarding satellite data that were identified

Machine learning applied to pore-space geometry in sandstones: a tool for evaluating grain-scale similarity?

The ability to identify similar sandstones to a given sample is important where the provenance of the sample is unknown or the quarry of origin is no longer in operation. In the case of building stones from heritage buildings in protected areas, it may be mandatory. Here, a proof of concept for an automated similarity measure is presented by means of a convolutional autoencoder that is able to extract features from a sample thin section and use these features to identify the most similar sample in an existing image library. The approach considers only the shape of the pore space between grains, as, if the pore space alone contains enough information to distinguish between samples, the required image pre-processing and training of a model is greatly simplified. The trained model is able to predict correctly the progenitor quarry of a thin section, from an eight-class dataset of Scottish sandstones, with an accuracy of 47.9%. This prototype, although insufficient for commercial purposes, forms a benchmark for future models against which improvements can be assessed and some of which are suggested

Derivation of lithofacies from geophysical logs : a review of methods from manual picking to machine learning

The aims of this report are to document: 1. A range of methods that are currently used by the BGS stratigraphers to extract lithological information from geophysical logs (includes manual classification, cut-off analysis, mineral composition by linear inversion). 2. Alternative methods which, at present, are not routinely applied but are sufficiently practical and accessible that they could become important, including unsupervised (k-mean clustering) and supervised machine learning approaches. The report does not aim or claim to be a complete inventory of all possible methods to derive lithological information from geophysical logs. The authors welcome correspondence and information on any additional methods that are available or emerging

Automatic detection of Ionospheric Alfvén Resonances in magnetic spectrograms using U-net

Ionospheric Alfven Resonances (IARs) are weak discrete non-stationary Alfven waves along magnetic field lines, at periods of ~0.5-20 Hz, that occur during local night-time, particularly during low geomagnetic activity. They are detectable through time-frequency analysis (spectrograms) of measurements made by sensitive search coil magnetometers. The IARs are generated by the interaction of electromagnetic energy partially trapped in the Earth-ionosphere cavity with the main geomagnetic field and their behavior provides proxy information about atmospheric ion density between 100-1000 km altitude. Limited methods exist to automatically detect and analyse their properties and behavior as they are difficult to extract using standard image and signal processing techniques. We present a new method for the detection of IARs based on the fully convolutional neural network U-net. U-net was chosen as it is able to perform accurate image segmentation and it can be trained in a supervised fashion on a relatively small labeled dataset utilizing data augmentation. We show that the resulting predictive model generated by training the U-net is able to detect IAR signals while mislabelling considerably less noise than other data analysis methods. We achieved our best results by using a training set of 178 hand-digitized examples from high-quality spectrograms measured at the Eskdalemuir Geophysical Observatory (UK). We find that the network converges in ten iterations with a final intersection over union (IoU) metric of 0.9 and a training loss of below 0.2. We use the trained network to extract IARs from over 2300 images, covering six years of search coil magnetometer data measured at the Eskdalemuir Observatory. U-net can also automatically handle missing data or days without IARs, giving a null result as expected. This constitutes the first use of a neural network for pattern recognition of unstructured image data such as spectrograms containing IAR signals, though the method is applicable to other types of resonances or geophysical features in the time-frequency domain