dataesr/scientific-paragraphs-categorization huggingface dataset:
Learning the time-dependent moment release. In order for PEGSNet to learn M w (t), we randomly perturb the starting time of the input data during training (Extended Data Fig. 2c ). Every time that an example is extracted from the dataset, a value (T 1 ) is drawn at random from a uniform distribution between -315 and 0 (s). T 1 is the time relative to the earthquake origin time (T 0 ) corresponding to the starting time of the selected seismograms for that example. In practice, from the 700-s-long seismograms (centred on T 0 ) in the database, we extract traces from T 1 to T 2 = T 1 + 315 s: for T 1 = -315 s the extracted traces end at T 0 ; for T 1 = 0 s the traces start at T 0 and end 315 s after. Once a value for T 1 is selected, the value of M w (T 2 ) is assigned as the corresponding label for this example (Extended Data Fig.
2d ). This enables the model to learn patterns in the data as the STF evolves with time. Training. The full database (500,000 examples of synthetic earthquakes) is split into training (350,000) validation (100,000) and test (50,000) sets, following a 70/20/10 strategy. The network is trained for 200 epochs (using batches of size 512) on the training set by minimizing the Huber loss between the true values and the predicted earthquake source parameters using the Adam algorithm  49  , with its default parameters (β 1 = 0. 9 and β 2 = 0. 999) and a learning rate of 0. 001. At the end of each epoch, the model is tested on the validation set to assess the learning performance and avoid overfitting (Extended Data Fig. 2b ). After learning, the model that achieved the best performance (lowest loss value) on the validation set is selected as the final model.
The same procedure is also applied to real data (Fig. 3  and Extended Data Fig. 10 ), to simulate a playback of the data as if they were fed to PEGSNet in real-time. Test on noise-free synthetics. We investigate the performance of PEG-SNet by using the same database described above but without including noise in the input data. Training and testing on noise-free synthetic data provides an upper limit of PEGSNet performance. Although this experiment represents a virtually impossible scenario for real-world applications, the results can reveal inherent limitations of our model or in the input data. Extended Data Fig. 6a  shows the accuracy map for the test set. As expected, the model is able to determine the final M w of the events with high accuracy and similar performance regardless of the actual M w of the event, except at early times.
Preprocessing of real data The preprocessing steps for real data closely follow the procedure detailed in previous work  7. For each station and each component: 1. Select 1-h-long raw seismograms ending at the theoretical T P calculated using the source location from the United States Geological Survey (USGS) catalogue (Extended Data Fig. 10a  This procedure is the same as that used to generate the synthetic database, except that here, traces need to be cut at the P-wave arrival first to avoid contamination of PEGS by the P-wave during instrument response removal. To speed up our testing procedure (see Methods subsection 'Testing strategy'), the data are preprocessed once and then sliced into input for PEGSNet at each time step. In an online version of our model, this is unfeasible as all the preprocessing steps need to be applied each time that new packets of data are streamed in.
Data availability Waveforms and metadata used in this study were provided by the Global Seismograph Network 51 , the New China Digital Seismograph Network  52  , GEOSCOPE  53  (IPGP/EOST, 1982), the Japan Meteorological Agency Seismic Network (https://www. jma. go. jp/en/quake) and the National Research Institute for Earth Science and Disaster Resilience/F-net  54  , all publicly available from the IRIS Data Management Center (https://ds. iris. edu/ds/nodes/dmc) and the F-net data centre (https://www. fnet. bosai. go. jp/top. php). Source data are provided with this paper. Extended Data Fig. 9  | PEGSNet noise test. Density plot of the predictions obtained for 1,000 real noise recordings (the same used in Extended Data Fig. 8 ) assuming P-wave arrivals (but no PEGS) as in the real data for the Tohoku-Oki earthquake.
STF database. a, STFs (moment rate) coloured by their final magnitude in the training database from a previous model 5 (see description in Methods). Only STFs for events with final M w between 7. 0 and 9. 5 are shown. b, The complete M w (t) distribution is used for labelling the database. Extended Data Fig. 2 | PEGSNet architecture and training strategy. a, The input data for one example consists of a three-channel image of shape M × N, where M is the number of time samples and N is the number of seismic stations. Only the vertical component of the input data is displayed for simplicity. Each convolutional block is composed of a convolutional layer (yellow) with a ReLu activation (orange), a spatial dropout layer (light blue). Max pooling layers (red) reduce each dimension of the input data by a factor of two.
The number of filters used in each convolutional layer is indicated for clarity. The last convolutional block is connected to dense layers (purple) (using a ReLu activation function), with dropout (light blue). The output layer uses a tanh activation function to predict values of M w , latitude (φ) and longitude (λ). b, The value of the Huber loss is plotted as a function of epochs for the training (Train. ) and validation (Val. ) sets. Each epoch corresponds to a full pass over the training set in batches of size 512. The red star indicates the epoch with the minimum value of the loss on the validation set. The corresponding model is used for predictions on the test set and real data. c, Data from one example from the training database (vertical component). The grey shaded area corresponds to the input data for PEGSNet shown in a.
6 | Results on noise-free test set. a, As in Fig. 2a, but for a test set including only noise-free PEGS waveforms. b, Density plot of the residuals as a function of time for the same test set. The solid red line is the median, the dashed lines are the 5th and 95th percentiles of the distribution. Note that both in a and b, the predictions are obtained for a model that has been trained on a database of noise-free synthetics.
PEGSNet predictions on real data. a, List of earthquakes used for the real data cases. All parameters are taken from the USGS catalogue. Earthquakes were selected according to the following criteria: M w ≥ 7. 0, dip-slip focal mechanism and located within 40 km from the source locations used for training (green lines in Fig. 1). b-f, PEGSNet M w (t) predictions are indicated by the blue line. The integrated STFs (orange lines) are taken from the SCARDEC database with dashed lines representing ±0. 3 magnitude units.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  949221 ). This work has been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR) ,  ANR-15-IDEX-01. This work was granted access to the  HPC resources of IDRIS  under the allocations  2020-AD011012142 ,  2021-AP011012536  and  2021-A0101012314  made by  GENCI. B. R. -L. 's work was supported by  Institutional Support (LDRD) at Los Alamos  ( 20200278ER ). Numerical computations for the synthetic database of PEGS were performed on the S-CAPAD platform, IPGP, France. We thank  M. Böse  for providing the results of FinDer2 on Tohoku-Oki data.
Extended Data Fig. 1 | 1 STF database. a, STFs (moment rate) coloured by their final magnitude in the training database from a previous model 5 (see description in Methods). Only STFs for events with final M w between 7. 0 and 9. 5 are shown. b, The complete M w (t) distribution is used for labelling the database. Extended Data Fig. 2 | PEGSNet architecture and training strategy. a, The input data for one example consists of a three-channel image of shape M × N, where M is the number of time samples and N is the number of seismic stations. Only the vertical component of the input data is displayed for simplicity. Each convolutional block is composed of a convolutional layer (yellow) with a ReLu activation (orange), a spatial dropout layer (light blue). Max pooling layers (red) reduce each dimension of the input data by a factor of two.
LakeatLaS forms part of the larger HydroatLaS data repository and expands the existing datasets of sub-basin and river reach descriptors by adding equivalent information for lakes and reservoirs in a compatible structure. Matching its HydroATLAS counterparts, version 1. 0 of LakeATLAS contains data for 56 variables, partitioned into 281 individual attributes and organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. LakeATLAS derives these attributes by processing and reformatting original data from well-established global digital maps at 15 arc-second (~500 m) grid cell resolution and assigns the information spatially to each lake by aggregating it within the lake, in a 3-km vicinity buffer around the lake, and/or within the entire upstream drainage area of the lake.
Advances in remote sensing and computing over the past two decades have produced increasingly comprehensive and dynamic global maps of inland surface waters  [14] [15] [16] [17]. But these datasets, on their own, are limited in their ability to describe surface water characteristics and distinguish among lakes, rivers, floodplains, and other wetlands. To aid in this distinction, a global lake-only database of more than 1. 4 million individual lake and reservoir shoreline polygons has been created through extensive manual processing of surface water maps, termed HydroLAKES  18. However, although HydroLAKES provides basic information on the distribution and geometry of lakes, users in need of additional lake characteristics that shape their ecological and geomorphological functioning -such as climatic conditions, topography, or surrounding land cover -still have to derive or summarize this information from disparate sources.
For instance, to spatially correlate data that are processed at different resolutions, extents, or projections, the applied alignment procedures can result in potential artefacts and faulty interpolation. Therefore, baseline datasets providing consistent and homogeneous global coverage are needed to enable and support limnological research and water resource management in a timely manner across the world, particularly in remote areas where little monitoring is available. Few initiatives to date have compiled and harmonized baseline data on the hydro-environmental attributes of lakes at large scales. As notable regional exceptions, the Lake-Catchment (LakeCat) dataset  21  provides 135 environmental attributes for all 378,088 lakes in the conterminous U. S. at varying lake catchment scales; LAGOS-NE  22  provides hydro-environmental attributes and in situ measurements of lake water quality for lakes across 17 states in the U.
S. Northeast; and the forthcoming LakePulse database will provide attributes for lakes across Canada (https://lakepulse. ca). At the global scale, several databases provide hydro-environmental attributes for a small subset of lakes, or a limited selection of attributes for a larger number of lakes; examples include the International Lake Environment Committee Foundation World Lake Database (https://wldb. ilec. or. jp), LakeNet's Global Lakes Database (http://www. worldlakes. org/lakes. asp), data compiled by the GloboLakes project (http://www. globolakes. ac. uk), or the Global Lake Database (GLDB)  23. However, to our knowledge the only existing compilation of attributes for a near-complete global set of lakes (≥10 ha) is the Global Lake area, Climate, and Population dataset (GLCP)  24. GLCP combines static lake polygons from HydroLAKES with calculated yearly lake surface area and basin-level temperature and precipitation metrics, as well as human population estimates for each of the 1.
4 million individual lakes. GLCP is a valuable contribution to the global study of lake and reservoir dynamics as its data on lake surface fluctuations not only address important limitations in the component datasets of HydroLAKES (see Technical Validation) but also provide information on seasonal and long-term changes in water quantity which are key to understanding the global interplay between lakes, humans, and hydrology  24. Nonetheless, GLCP shows some technical constraints associated with the spatial units used for the calculation of basin-level statistics (see Technical Validation for more details); and its restricted set of environmental variables limits its applicability when data on a broader range of lake characteristics are required (e. g. , land cover, physiography, soil conditions).
Also, GLCP derives its environmental variables for an encompassing lake surface and basin area. Yet for some environmental conditions it is important to distinguish whether the information is derived from the entire drainage area of the lake, from within the lake proper (i. e. , across the lake surface area), or from within close vicinity around the lake's shoreline, as each of these zones can provide unique contributions to lake ecosystem functioning and integrity  3, 21, 25, 26. Here we introduce the LakeATLAS dataset  27  , which provides hydro-environmental information for all lakes and reservoirs globally with an area of at least 10 ha that are contained in HydroLAKES. LakeATLAS complements an existing global data repository of hydro-environmental sub-basin and river reach characteristics termed HydroATLAS  28  (Fig.
1 ). The goal of the overarching HydroATLAS database is to provide a single, comprehensive, consistently organized and fully global digital data compendium at high spatial resolution, organized in three distinct hydrographic sub-datasets: BasinATLAS (containing ~1. 0 million sub-basin polygons), RiverATLAS (containing ~8. 5 million river reach lines), and LakeATLAS (containing ~1. 4 million lake polygons). The hydro-environmental attributes of all three sub-datasets are compiled from publicly available data sources and are organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences (Table  1 ). To match with the remainder of the HydroATLAS database, version 1. 0 Fig. 1  Conceptual design of HydroATLAS and relationship to underpinning HydroSHEDS database.
HydroATLAS consists of three companion attribute datasets: BasinATLAS and RiverATLAS (fully described in Linke et al. 28  ) as well as LakeATLAS (this paper). BasinATLAS provides sub-basin characteristics for hierarchically nested watersheds at twelve spatial scales. RiverATLAS contains the same attributes yet calculated for river and stream reaches rather than sub-basins. The geospatial units for both databases, i. e. , sub-basin polygons and river reach line segments, respectively, have been derived from the global hydrographic database HydroSHEDS 39 at a spatial resolution of 15 arc-seconds (~500 m at the equator). LakeATLAS is added here following the same overall format and structure. of LakeATLAS provides the same list of attributes as versions 1. 0 of BasinATLAS and RiverATLAS comprising a total of 281 individual attributes that represent 56 different hydro-environmental variables (Table  2  and Fig.
2 ). The attributes of LakeATLAS are processed for three complementary spatial units: within the lake surface polygon, in a 3-km buffer around the lake, and/or within the entire upstream drainage area of the lake. Table  3  provides an overview of the main statistical characteristics of each variable as derived from all lakes included in LakeATLAS. We find that lakes occur in all major climate zones, land cover types, biomes, and countries; their distribution spans elevations between -414 and 5,826 m a. s. l. and mean air temperatures between -25. 9 and 31. 9 °C; in average 29. 0% of their upstream area is forested and 12. 6% of their upstream area is protected; and we estimate that a total of 965 million people (more than 12% of the world's population) live within 3 km of a lake. A unique feature of HydroATLAS and its three companion datasets is the ability to spatially integrate the respective hydrographic features using standard Geographic Information System (GIS) technology.
For example, each lake polygon is associated to the river network of RiverATLAS and the sub-basin network of BasinATLAS (via unique IDs), which allows to geolocate each lake within the drainage network. This facilitates topological assessments such as identifying and analyzing the sub-basins or river reaches that are upstream or downstream of a given lake. Using this functionality, additional attributes or advanced statistics can be calculated. The LakeATLAS dataset is expected to create novel opportunities for a mix of theoretical and applied limnological studies, enabling multi-variable statistical assessments and model-based analyses, and to stimulate large-scale limnological research in otherwise data-poor or remote regions. For example, LakeATLAS can facilitate large-scale assessments of anthropogenic pressures on lakes  29  , support systematic lake classification efforts  30, 31  , and reveal biases in monitoring and conservation networks  32, 33  , tasks previously unachievable at this scale in the absence of consistent global data.
Globally comprehensive data on lake characteristics can also be combined with datasets from in situ monitoring networks like the Global Lake Ecological Observatory Network (GLEON)  34  , long-term ecological research networks  35  , other data compilation efforts (e. g. , ref. 36  ), or information derived from remote sensing imagery (e. g. , refs. 15, 17 ) to yield novel insights and refined estimates of the magnitude and distribution of lake processes worldwide. Finally, by connecting individual lakes to the river and stream network and their associated drainage areas, the overarching HydroATLAS database enables advanced mechanistic macroscale analyses of inland waters whereby the fluxes of materials and organisms among the elements of the waterscape can be explicitly modeled  3. It should be noted that every effort is made throughout this article to distinguish 'natural lakes' from 'human-made reservoirs' , yet the general term 'lake' is used in all instances where this distinction is considered non-critical.
Methods Lake polygons and morphometric attributes. The geospatial foundation of LakeATLAS is formed by the HydroLAKES dataset (https://www. hydrosheds. org/hydrolakes) which provides lake polygons and basic morphometric attributes (e. g. , area, depth, volume) for all lakes globally with a surface area of at least 10 ha, comprising a total of 1,427,688 individual lakes  18. HydroLAKES contains both freshwater and saline lakes, including the Caspian Sea, as well as human-made reservoirs and regulated lakes. HydroLAKES polygons and morphometric attributes were used in their original form for LakeATLAS. This section and the following (Spatial association of lake polygons with drainage network) include a brief description of the original production process implemented by Messager et al. 18  -for more details, see the original publication  18  and the associated Technical Documentation  37.
HydroLAKES was built with the aim to be as comprehensive and consistent as possible at a global scale. It was created by compiling, correcting, and unifying several near-global and regional datasets (see ref. 37  for more details) -foremost the Shuttle Radar Topography Mission (SRTM) Water Body Data (SWBD)  14  for regions from 56°S to 60°N, and CanVec  38  for lakes in Canada (62% of all lakes in the database). To ensure Continued spatial consistency among the different data sources, map generalization methods were applied to the original polygons and some polygon outlines were smoothed. The resulting map scale is estimated to be between 1:100,000 and 1:250,000 for most lakes globally, with some polygons having a coarser scale of 1:1 million (mostly in Russia north of 60°N). Each lake polygon in HydroLAKES is associated with a suite of geometric attributes, including lake area, shoreline length, average depth, and volume.
Spatial association of lake polygons with drainage network. HydroLAKES was spatially associated with hydrographic baseline information from the HydroSHEDS raster database  39  (Fig. 1 ). This allows for linking each lake to the drainage network by its pour point (the most downstream pixel that drains the lake) and delineating its upstream watershed area. HydroSHEDS consists of a hydrologically conditioned digital elevation model and a corresponding drainage direction map from which auxiliary layers were derived, including flow accumulations, flow distances, river orders, watershed boundaries, and stream networks  39. HydroSHEDS was initially derived from elevation data of the Shuttle Radar Topography Mission (SRTM)  14, 40  at a pixel resolution of 3 arc-seconds (~90 m at the equator) and was subsequently upscaled to a resolution of 15 arc-seconds (~500 m at the equator).
After creating the lake pour points, the upstream watershed area from HydroSHEDS (corrected for latitudinal pixel distortions due to the applied geographic projection) as well as the downscaled discharge estimates were extracted at the location of each lake pour point and assigned to the lake polygon. An estimate of the average residence time for each lake was calculated as the ratio between lake volume and discharge  18. acquisition and selection of hydro-environmentally relevant attribute data. LakeATLAS relies on the same sources of hydro-environmental data as RiverATLAS and BasinATLAS 28 (Fig. 1  and Table  2 ). These input data were acquired either from free and publicly available sources, or from collaborators who provided their data for this project. All data sources were originally assessed regarding their suitability for the entire HydroATLAS project using the following selection criteria: a) completeness of global coverage (allowing only for minor spatial gaps, such as small remote islands, or omission of non-critical areas, such as Greenland or deserts); b) consistency in data quality (i.
e. , no regional or local biases); c) sufficiency of the native resolution, precision, and accuracy (i. e. , resolution of the source data should be in the range of 15 arc-seconds, or finer); and d) permission to use and distribute derivatives under a free license. If multiple datasets were available for the same attribute, priority was given to the most widely recognized and/or best resolution and/or most recent dataset on a case-by-case basis  28. It should be noted, however, that the selection of an attribute dataset does not imply any kind of endorsement or warranty of its quality or superiority over other data. In addition, we acknowledge that new and improved sources of data have been published since the creation of RiverATLAS and BasinATLAS (e. g. , an updated WordClim climatological database  46  , an updated SoilGrids pedological database  47  , and new/updated global land cover products  48, 49  ), yet we opted to keep all data sources originally included in HydroATLAS to warrant consistency and the capacity to link and compare attributes across the three datasets.