Prediction of Large Events on a Dynamical Model of a Fault

We present results for long term and intermediate term prediction algorithms applied to a simple mechanical model of a fault. We use long term prediction methods based, for example, on the distribution of repeat times between large events to establish a benchmark for predictability in the model. In comparison, intermediate term prediction techniques, analogous to the pattern recognition algorithms CN and M8 introduced and studied by Keilis-Borok et al., are more effective at predicting coming large events. We consider the implications of several different quality functions Q which can be used to optimize the algorithms with respect to features such as space, time, and magnitude windows, and find that our results are not overly sensitive to variations in these algorithm parameters. We also study the intrinsic uncertainties associated with seismicity catalogs of restricted lengths.Comment: 33 pages, plain.tex with special macros include

Hierarchical Attention Network for Action Segmentation

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.Comment: Published in Pattern Recognition Letter

Statistics and Pattern Recognition Applied to the Spatio-Temporal Properties of Seismicity

Due to the significant increase in the availability of new data in recent years, as a result of the expansion of available seismic stations, laboratory experiments, and the availability of increasingly reliable synthetic catalogs, considerable progress has been made in understanding the spatiotemporal properties of earthquakes. The study of the preparatory phase of earthquakes and the analysis of past seismicity has led to the formulation of seismicity models for the forecasting of future earthquakes or to the development of seismic hazard maps. The results are tested and validated by increasingly accurate statistical methods. A relevant part of the development of many models is the correct identification of seismicity clusters and scaling laws of background seismicity. In this collection, we present eight innovative papers that address all the above topics. The occurrence of strong earthquakes (mainshocks) is analyzed from different perspectives in this Special Issue

From Hector Mine M7.1 to Ridgecrest M7.1 Earthquake. A Look from a 20-Year Perspective

The paper provides a comparative analysis of precursory phenomena in the ionosphere and atmosphere for two strong earthquakes of the same magnitude M7.1 that happened in the same region (North-East from Los Angeles) within a time span of 20 years, the Hector Mine and Ridgecrest earthquakes. Regardless of the similarity of their location (South-Eastern California, near 160 km one from another), there was one essential difference: the Hector Mine earthquake happened during geomagnetically disturbed conditions (essential in the sense of ionospheric precursors identification). In contrast, the quiet geomagnetic conditions characterized the period around the time of the Ridgecrest earthquake. The Hector mine earthquake happened in the middle of the rising phase of the 23-rd solar cycle characterized by high solar activity, while the Ridgecrest earthquake happened by the very end of the 24th cycle under very low solar activity conditions. We provide a comprehensive multi-factor analysis, determine the precursory period for both earthquakes and demonstrate the close similarity of ionospheric precursors. Unlike the majority of papers dealing with earthquake precursor identification based on the “abnormality” of observed time-series mainly determined by amplitude difference between “normal” (usually climatic) behavior and “abnormal” behavior with amplitudes exceeding some pre-established threshold, we used the technique of cognitive recognition of the precursors based on the physical mechanisms of their generation and the morphology of their behavior during the precursory period. These permits to uniquely identify precursors even in conditions of disturbed environment as it was around the time of the Hector Mine earthquake. We demonstrate the close similarity of precursors’ development for both events. The leading time of precursor appearance for the same region and similar magnitude was identical. For the Hector Mine it was 11 October 1999—5 days in advance—and for 2019 Ridgecrest it was 28 June—7 days before the mainshock and five days before the strongest foreshock