In body-centric communications, energy efficiency is the most critical parameter, while the maximum achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. This scheme is a combination of the time-spread On-Off keying (TS-OOK) and the pulse position modulation (PPM). The new modulation scheme presents lower energy consumption levels at a small cost to the achievable data rate. Furthermore, another important aspect is that, due to the nature of noise in THz communications, the proposed modulation is affected in a much smaller way by the noise. Finally, we present analytical and simulation results in order to compare the new scheme with TS-OOK.

In body-centric communications, energy efficiency is a critical performance metric, while the achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. The proposed scheme is a combination of the time-spread On-Off keying (TS–OOK) and the pulse position modulation (PPM) and presents lower energy consumption, compared to other existing methods as TS–OOK, at a minor cost to the data rate. Furthermore, another important aspect is that the proposed modulation scheme can be effectively used to mitigate the impact of the specific kind of noise in THz body-centric communications, thus leading to better error performance. Finally, we present analytical and simulation results in order to compare the new scheme with the existing TS–OOK.

Recent advances in earthquake monitoring have led to the development of methods for the automatic generation of high-resolution catalogues. These catalogues are created at considerably reduced processing times and contain significantly larger volumes of data concerning seismic activity compared to standard catalogues created by human analysts. Disciplinary statistics and physics-based earthquake forecasting models have shown improved performance when rich catalogues are used. The use of high-resolution catalogues paired with machine learning algorithms, which have recently evolved due to the rise in the availability of data and computational power, is therefore a promising approach to uncovering underlying patterns and hidden laws within earthquake sequences. This study focuses on the development of short-term data-driven spatiotemporal seismicity forecasting models with the help of deep learning and tests the hypothesis that deep neural networks can uncover complex patterns within earthquake catalogues. The performance of the forecasting models is assessed using metrics from the data science and earthquake forecasting communities. The results show that deep learning algorithms are a promising solution for generating short-term seismicity forecasts, provided that they are trained on a representative dataset that accurately captures the properties of earthquake sequences. Comparisons of machine learning-based forecasting models with an epidemic-type aftershock sequence benchmark show that both types of models outperform the persistence null hypothesis commonly used as a benchmark in forecasting the behaviour of other types of non-linear systems. Machine learning forecasting models achieve similar performance to that of an epidemic-type aftershock sequence benchmark on the Southern California and Italy test datasets at significantly reduced processing times - a major advantage in applications to short-term operational earthquake forecasting. Graphical Abstract

The catastrophic nature of earthquakes drives the need for understanding seismic events, as well as for providing forecasts of when these are likely to occur. Due to the clustering nature of earthquakes, large magnitude events often trigger aftershocks that occur close to the mainshock in both space and time. In this study, we use a convolutional neural network to develop a data-driven spatiotemporal model to forecast next-day seismicity in an attempt to provide information that can contribute to answering one of the most pressing questions: whether a larger magnitude earthquake is to be expected after an intermediate magnitude event. We design our test to estimate expected seismicity within one day after earthquakes of magnitude four and above. We assemble a comprehensive dataset of earthquake catalogues from diverse tectonic regions to achieve a representative sample of input data and use it to create weekly spatiotemporal sequences of seismicity consisting of daily maps. Leveraging the predictive power of deep learning, our model uncovers complex patterns within this large dataset to produce next-day expected seismicity rate and magnitude forecasts in regions of interest. We use gradient-weighted class activation mapping (Grad-CAM) to provide visual explanations of the produced forecasts. We evaluate the performance of our forecasting model using data science and earthquake forecasting metrics and compare against persistence, which assumes no change between consecutive days, echoing typical experimental setups of forecasting models. Furthermore, we use a time series forecasting foundation model to generate next-day aftershock forecasts on the same dataset and compare these results against those produced by the convolutional neural network. We find that deep learning approaches are a promising solution for producing short-term aftershock forecasts, providing valuable insights that can contribute to better earthquake preparedness and response and be integrated with disciplinary statistics and physics-based forecasts.

The development of novel deep learning-based earthquake monitoring workflows has led to a rapid increase in the availability of earthquake catalogue data. Earthquake catalogues are now being created by deep learning algorithms at significantly reduced processing times compared to catalogues built by human analysts and contain at least a factor of ten more earthquakes. The use of these rich catalogues has been shown to have led to improvements in the predictive power of statistical and physics-based forecasts. Combined with the increasing availability of computational power, which has greatly contributed to the recent breakthrough in the field of artificial intelligence, the use of rich datasets paired with machine learning workflows seems to be a promising approach to uncovering novel insights about earthquake sequences and discovering previously undetected relationships within earthquake catalogues. Our focus is on employing deep learning architectures to produce high-quality earthquake forecasts. Our hypothesis is that deep neural networks are able to uncover underlying patterns within rich earthquake catalogue datasets and produce accurate forecasts of earthquakes, provided that a representative dataset that accurately reflects the properties of earthquake sequences is used for training. We use earthquake catalogue data from different geographical regions to build a time series of spatiotemporal maps of past seismicity. We then split this time series into training, validation, and test datasets in order to explore the ability of deep neural networks to capture patterns within sequences of seismicity maps and produce short-term spatiotemporal earthquake forecasts. We assess the performance of the trained deep learning-based forecasting models by using metrics from the machine learning and time-series forecasting domains. We compare the trained models against a null hypothesis, the persistence model, which assumes no change between consecutive time steps and is commonly used as a baseline in various time series forecasting settings. The persistence null hypothesis has been proven to be a very effective model due to the fact that when only background seismicity is observed, there is very little change between consecutive time steps. We also evaluate the relative performance of different deep learning architectures and assess their suitability for dealing with our specific problem. We conclude that deep learning techniques are a promising alternative to disciplinary statistics and physics-based earthquake forecasting methods as, once trained, deep learning models have the potential of producing high-quality short-term earthquake forecasts within seconds. This realisation can influence the future of operational earthquake forecasting and earthquake predictability.