Abstract: The East Antarctic Ice Sheet is the largest, highest, coldest, driest, and windiest ice sheet on Earth. Understanding of the surface mass balance (SMB) of Antarctica is necessary to determine the present state of the ice sheet, to make predictions of its potential contribution to sea level rise, and to determine its past history for paleoclimatic reconstructions. However, SMB values are poorly known because of logistic constraints in extreme polar environments, and they represent one of the biggest challenges of Antarctic science. Snow accumulation is the most important parameter for the SMB of ice sheets. SMB varies on a number of scales, from small-scale features (sastrugi) to ice-sheet-scale SMB patterns determined mainly by temperature, elevation, distance from the coast, and wind-driven processes. In situ measurements of SMB are performed at single points by stakes, ultrasonic sounders, snow pits, and firn and ice cores and laterally by continuous measurements using ground-penetrating radar. SMB for large regions can only be achieved practically by using remote sensing and/or numerical climate modeling. However, these techniques rely on ground truthing to improve the resolution and accuracy. The separation of spatial and temporal variations of SMB in transient regimes is necessary for accurate interpretation of ice core records. In this review we provide an overview of the various measurement techniques, related difficulties, and limitations of data interpretation; describe spatial characteristics of East Antarctic SMB and issues related to the spatial and temporal representativity of measurements; and provide recommendations on how to perform in situ measurements.

Abstract: Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publicly-available seismic data nowadays requires automated, fast, and reliable tools to carry out a multitude of tasks, such as the detection of small, local earthquakes in areas characterized by sparsity of receivers. A similar application of machine learning, however, should be built on a large amount of labeled seismograms, which is neither immediate to obtain nor to compile. In this study we present a large dataset of seismograms recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide; this includes 629,095 3-component seismograms generated by 304,878 local earthquakes and labeled as EQ, and 615,847 ones labeled as noise (AN). Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings, even if applied in regions not represented in the training set. Achieving an accuracy of 96.7, 95.3, and 93.2% on training, validation, and test set, respectively, we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm, and makes it applicable to real-time detection of local events. We make the database publicly available, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.