dataesr/scientific-paragraphs-categorization huggingface dataset:
As an example, Fig. 2. 2 shows the time series of CO concentration detected by an infrared gas filter correlation CO monitor at various sampling points after a point injection in the CESAM chamber  (Wang et al. 2011). Numerical modelling can further help understanding the mixing in the chamber. Wang et al. (2011)  divided the volume of the CESAM chamber into 4137 cubic cells each of which has a volume of 1 L and set up a multi-box-model. Time series of trace concentrations were modelled for each box using a kinetic solver (Facsimile TM software package-Curtis 1979) with a non-zero initial concentration in the box in which the injection was located. Assuming isotropic mixing, the exchange rate was adjusted to match the measured concentration time series (Fig. 2. 2). For experiments, in which the fan was operated at full speed, these calculations gave a first-order exchange rate of (3 ± 0.
Total number concentration was held below 10 4 cm -3 to minimize the collision probability and so as not to require a correction for coagulation. The number size distribution was measured as a function of time (with a SMPS for sub-micron particles and OPC for super-micron particles). A first order decay fit following Eq. 2. 7. 1 was fitted to the time evolution of each size-bin. Wang et al. (2011)  reported that particle lifetime in the stainless steel CESAM chamber ranges from 10 h to 4 days depending on particle size distribution, enabling the chamber to provide satisfactorily high-quality data on aerosol aging processes and their effects. More recently  Lamkaddam (2017)  has studied the physical wall loss rate as a function of particle size. Submicron ammonium sulphate particles were generated in small number to minimize coagulation and mineral dust were used for supermicron particles.
These tremors dominate the noise field above ∼0. 5 Hz  (Gómez-García et al. , 2018). As we will discuss later, this observation has important implications for noise interferometry studies. Other studies have investigated the time-dependent variations of the subsurface properties. Koulakov et al. (2013)  found velocity changes in the upper crust below the KVG using repeated 3D seismic tomography. They interpreted these changes as signs of magma migration preceding eruptive periods. However, due to its coarse temporal resolution, 4D earthquake tomography is only of limited use for eruption early warning. Gómez-García et al. (2018)  applied PII to data collected at the KVG. While they recovered long-term velocity changes from frequencies The sites of permanent and temporary stations are represented by squares and triangles, respectively.
e. , the Green's function estimate) changes significantly during the study. The average CF is plotted in black on top of the heatmap. We muted the daily correlation functions for -0. 5 s ≤ τ ≤ 0. 5 s to emphasize the shape of the coda. The color scale is truncated to -0. 25 and 0. 25, respectively. (c) The velocity change was estimated using the trace stretching method with the data from (b) and smoothing of 2 days. We indicate the location of X9. IR1 in Figure  1.
Complementary Data To set our results in context to potential environmental and volcanological forcing, we retrieve snow load, precipitation, and temperature data from ERA5, a global climate reanalysis dataset. ERA5 is sampled every hour and is resolved on a 9 × 9 km grid  (Hersbach et al. , 2020). While the precipitation data shows degraded accuracy on an hourly time scale  (Tang et al. , 2020) , its performance is still sufficient for our purposes. For Kamchatka, ERA5 is exclusively based on satellite data. We rely on infrared satellite measurements  (Coppola et al. , 2021)  to compare the determined velocity changes to the lava output of the volcanoes. The magnitudes and origin times for regional earthquakes were obtained from the USGS FDSN web service.
Similarly, Steinmann, Seydoux, Beaucé, and Campillo (2022)  combined independent component analysis (ICA), a dimensionality reduction algorithm that separates statistically independent features, with hierarchical clustering. Thereby, they succeeded to discriminate time windows with different dominant sources. In another approach, they analyzed the output features of the ICA and found a feature that closely resembles the velocity change of the medium  (Steinmann, Seydoux, & Campillo, 2022). While this approach seems to retrieve very accurate and highly resolving dv/v estimates for their dataset, one must have successfully retrieved a low-resolution baseline estimate of dv/v using a conventional algorithm. Only then it is possible to evaluate that a feature of the decomposition corresponds to dv/v and provide quantitative estimates of the velocity change, as the amplitude of the ICA coefficients is physically meaningless.
These limitations reduce the applicability of the ICA-based method. In addition, in more complex datasets, like our datasets, dv/v does not seem to be confined to a single feature of the ICA decomposition rendering the output practically uninterpretable. A more general issue with approaches that rely entirely on machine learning is that the related physical processes remain elusive, often making a correct interpretation of the results challenging. In the following section, we will describe an approach that combines the advantages of machine learning and conventional noise interferometry to retrieve velocity change estimates for the Kamchatka dataset.
Also, using separate clusters would unnecessarily complicate the comparability of results between different stations. We show the hierarchical dendrogram for data between 2 and 4 Hz in Figure  3a. As a compromise between stability and length of the dv/v time series, we split the dataset into five clusters. For 2-4 Hz, all clusters are highly dissimilar with a minimum cluster merging cost of about 9 (see Table  S1  in Supporting Information S1 for a merging cost-frequency overview). We can assess the nature of each cluster by examining its temporal occurrence (see Figure  3b ) and the shape of its average CF (Figure  3c ). Most average CFs share some common features at early lag times, but their peaks differ later in the coda, confirming that they are ill-suited for an interferometry study based on a single reference trace and time window.
Below 0. 25 Hz, we find no temporal patterns attributed to volcanic activity. We provide additional clustering results in Figures S1-S7 in Supporting Information S1. To retrieve stable dv/v estimates for shorter periods, we apply the trace stretching algorithm to all CFs in time periods dominated by one (e. g. , August 2015 to November 2015, April 2016 to July 2016, see Figure  3b ) or, in some cases, two clusters (time windows at the turn of the year, see Figure  3(b) ). Thus, instead of using CFs from only one cluster per dv/v estimation, we use all CFs for a given period. Applying interferometry to only one cluster could introduce a bias if the clustering algorithm splits the dataset due to changes in the medium (i. e. , stretching of the CF), which we want to retain and analyze in PII, rather than changes in noise characteristics.
For a much longer continuous dataset at Piton de la Fournaise,  Yates et al. (2022)  show that changes in the medium alter the dominant cluster permanently, whereas changes in the noise wavefield only lead to temporary variations. However, for the times available to us at KVG, making such an evaluation is challenging since varying tremors, which commence in late November 2015, last throughout the whole study period  (Journeau et al. , 2022). For each period, we use the average CF as a reference correlation function to measure changes in wave velocity. The same segments are chosen for all stations and station groups. In the following, we will refer to our described method as time-segmented passive image interferometry (TSPII). We provide a complete overview of the chosen time segments in Table  S2  in Supporting Information S1.
Velocity Changes at the KVG Using the method outlined above, we compute dv/v time series stacks for frequencies above 0. 5 Hz, where volcanic tremor energy is generally very high (see Figure  2a ). For frequencies below 0. 5 Hz, the statistical variation exceeds the amplitude of physical velocity changes over 1 year, even after introducing an aggressive temporal smoothing with a window length of 30 days. To remove these statistical variations and obtain interpretable results from these frequencies, we would require a longer continuous dataset. The most stable results were obtained from the averages of auto-and self-correlations of all components from the stations of each of the groups shown in Figure  4. Figure  5  shows the estimated velocity variation for the station group CKD from July 2015 to December 2015 for 4-8 Hz.
e. , the time that has passed since the precipitation event) and λ is the decay. To invert for a, b, C, and λ, we employ a least-squares inversion that optimizes a cost function to minimize the misfit between dv/v syn and dv/v. For a and b, we jointly invert for all time windows using data from one station group and each octave frequency band. For the offset C, we perform a separate inversion for each time window. We determine λ to be optimal for the whole dataset at λ = 10 days. As expected, the magnitudes of a and b are higher for 2-8 Hz than for lower frequencies indicating that precipitation and snow load have a higher impact on the velocity of shallower layers. We do not identify clear trends in a and b for the different station groups (see Figure  S8  in Supporting Information S1). We show dv/v syn as a black dashed line in Figures  5  and 6.
Additionally, we provide the coefficient of determination R 2 for each time window as an objective measure to evaluate the fit of our model. R 2 reaches a value of 1 for a perfect fit, R 2 = 0 implies that the model has the same fit as the mean of the data points, and values below 0 have a fit worse than the mean. We achieve the highest R 2 values for times dominated by strong precipitation and changes in snow load (e. g. , Figures  6(a ) and 6(b), and (c), whereas, for dv/v curves that are strongly affected by mechanisms that our model does not take into account (e. g. , earthquake damage, Figure  6 ), we obtain lower R 2 values. Despite its simplicity, our model reproduces first-order trends of the dataset reliably. For lower frequencies between 0. 5 and 2 Hz, it generally performs worse with R 2 values around 0 than for the higher frequencies, prob-ably due to higher scatter in the dv/v datapoints in those low-frequency velocity change estimates (see Figures S10-S45 in Supporting Information S1).
Data Availability Statement The KISS dataset  (Shapiro et al. , 2015 , https://doi. org/10. 14470/K47560642124) is openly available via the GFZ Data Services. Access to the data recorded by the D0 seismic network is restricted but can be obtained via the GFZ Data Services. The ERA5 climate data (Muñoz Sabater, 2019, doi: https://doi. org/10. 24381/cds. e2161bac) are available upon registration at https://cds. climate. copernicus. eu/cdsapp#!/dataset/10. 24381/ cds. e2161bac?tab=overview. The TADR datasets are provided as supplementary material to  Coppola et al. (2021). All TerraSAR-X data are available through the German Aerospace Centre (DLR). The origin times of the seismic events plotted in Sections 4 and 5 can be queried via the USGS FDSN web service. The seismic data download and the computation of correlations and dv/v were executed using SeisMIC 0.
1. 28  (Makus & Sens-Schönfelder, 2022 , doi: https://doi. org/10. 5880/GFZ. 2. 4. 2022. 002), available under the EUPL license agreement at https://github. com/PeterMakus/SeisMIC. TheSequencer algorithm  (Baron & Ménard, 2021)  that we used to sort the similarity matrices of the stations can be executed online at http:// sequencer. org, its source code can be downloaded at https://github. com/dalya/Sequencer. TheSequencer is licensed under the MIT license agreement. All maps were plotted using PyGMT (Uieda et al. , 2022, doi:   https://doi. org/10. 5281/zenodo. 7481934). For Figures  1a, 4 , and 7a, we used the SRTM15+ digital elevation model (DEM) by  Tozer et al. (2019). For Figures  1b  and 8a , we obtained the SRTMGL1 DEM from NASAJPL (2013).
Acknowledgements. We are particularly indebted to  Jean-Pierre Bouillin  for friendly discussions during the elaboration of this work, for making available some invaluable documents, and for his last minute pertinent comments on our revised manuscript. We are indebted to  Stefan M. Schmid  for his accurate comments on a first draft of this paper. Our referees  Rémi Leprêtre  and  Giancarlo Molli  are warmly thanked for their very constructive and helpful reviews. Sincere thanks are also due to  Laurent Jolivet  who encouraged us to work out our manuscript, and to  Romain Augier  who supervised the reviewing process.
, 2021)  or machine learning to obtain data from a large database of hypothetical deposits  (Mitra et al. , 2020). However, linking both forward and inverse models can give greater insights into the resulting sedimentary deposition than a single approach only  (Bosnic et al. , 2021). Regardless of the type of approach taken, a key part of any interpretation is a thorough understanding of the depositional mechanisms that occur during tsunami inundation. To date, this is poorly known  (Costa & Andrade, 2020) , which remains a challenge when interpreting palaeotsunami deposits  (Yamamoto et al. , 2021). The Holocene Storegga tsunami, which occurred around 8150 years ago  (Bondevik et al. , 2003) , was generated by a large submarine slide  (Bondevik et al. , 1997). The wave spread across the Norwegian-Greenland sea  (Bondevik et al.
A Brivo CT385 medical CT scanner (GE Healthcare) was operated at an X-ray voltage of 120 kV, tube current of 80 mA and focal spot size of 1. 2 mm for an exposure time of 1 s. This enabled the reconstruction of 96 mm diameter, 110 mm deep sub-samples of the core with a pixel resolution of 0. 188 mm and a slice separation (or voxel depth) of 0. 625 mm. This resolution is high enough to see sedimentary features in detail, but not sufficient to see individual grains. Image processing was conducted using Matlab v2020a. Sampling of cores A004 and B003 began at ca 30 mm beneath the observable base of the deposit and ceased approximately 30 mm above the observable upper contact of the deposit. Samples were taken every 2. 5 mm. The total core sections analysed were around 100 mm in length. Once removed, core samples were isolated in glassware and exposed to hydrogen peroxide (H 2 O 2 ) in order to remove any organic matter via decomposition.
Once decomposition had subsided, as indicated by a lack of an effervescence reaction, samples were transferred into centrifuge tubes and washed using deionised water in a Fisherbrand vortex mixer. In order to prevent coagulation of particles within the clay fraction, three drops of sodium hexametaphosphate ((NaPO 3 ) 6 ) were added to each centrifuge tube. Sections were then analysed for the 0. 5-2000 μm fractions in a Malvern Mastersizer with a Hydro 2000MU dispersion unit. Grain-size statistics were calculated using the G2Sd R package  (Fournier et al. , 2014). In order to assess if changes in grain size were statistically significant, changepoint analysis was employed using the R (R Core Team, 2020) package changepoint  (Killick & Eckley, 2014). Changepoint analysis estimates where the data are y, which contains an unknown number, m, of changepoints,.
Resolution varied from 20 m in the area of interest around the Ythan Valley to 1 km away from the coastline in deeper water. Using the HRDS Python package  (Hill, 2019) , the full range of topographic and bathymetric data (see below for full description of these data) were dynamical loaded into Thetis flow solver during the simulation so that to ensure the finest resolution data is selected at each point in the mesh. The seaward boundary is forced using water elevation data from the regional Storegga simulation of  Hill et al. (2014)  via oneway coupling. The data from  Hill et al. (2014)  were interpolated onto the node points of the mesh via linear 2D interpolation in space. The wave height data were then linearly interpolated in time to the 3 s timestep of the Thetis flow solver. This is the same methodology as  Bateman et al.
(2021). The initial bathymetric/topographic data are a mix of spatial resolutions. The topography data were derived from the 50 m Ordnance Survey DEMs and the Scottish Government's LIDAR data at 1 m resolution. Bathymetric data were derived from OceanWise data, which have a ca 30 m resolution. These data were adjusted to the same datum (Ordnance Datum). To adjust these data further to account for RSL and geomorphological changes five different scenarios were created to represent different levels of recreation of the palaeolandscape (Table  1 ). The same strategy as  Hill et al. (2014)  was employed to ensure consistency in the boundary conditions and the simulated scenarios presented here, which was to apply the GIA reconstruction of  Bradley et al. (2011)  to recreate the palaeobathymetry/ (1) m+1 ∑ i=1 C y ( i=1 +1): i + f(m) (2) t +∇⋅ H d u = 0, (3) u t + u ⋅ ∇u -𝜈∇ 2 u + f u ⊥ + g ∇ 𝜂 =- 𝜏 b 𝜌H d , (4) b = gn 2 | u | u H d 1 3 20554877, 0, Downloaded from https://onlinelibrary.
This only occurs in the model when the spit is removed entirely (see Video S7). It is clear from Figure  10  that the reconstruction still contains some modern elements of topography. The curve just north of Waterside is a bridge, which has not been removed fully during processing of LIDAR data to create a DEM. This will have some impact on the wave FIGURE 10 Three dimensional representation of the maximum wave height from model palaeo-full. The view is looking north-east. All data are exaggerated by a factor of 10 in the vertical. The maximum wave height shows a general decrease as it travels up the Ythan Valley. The sites described here are labelled, with the previous locations of where a Storegga deposit was found (Tarty Burn and Waterside) shown using a white cone. The site described here is shown with the blue cone.
To generate the forecast, the initial conditions at the low resolution are integrated to the target lead time with the forecast model. As we want to reinitialise the forecast model with the corrected model fields later, the model error correction has to be estimated at the low resolution. To consequently match the resolution of the forecast with the truth, we project the truth at the target lead time to the low resolution with our previously defined projection operator. The neural network targets the difference between truth and forecast at the low resolution (see also Sect. 2. 1). Using this strategy and an ensemble of initial conditions and forcing parameters, we generate our training dataset, which is then used to learn the model error correction. Additionally, we evaluate the performance of the learned model error correction on a similar but independent test dataset.
A convolutional U-Net baseline The neural network (NN) should learn to relate the input predictors to the output targets. The inputs and targets are spatially discretised as finite elements, and the NN should directly act on this triangular model grid. Moreover, the NN architecture should be scalable from regional models, as used in this study, to Arctic-wide models, like neXtSIM. As we expect that the model errors from the sea-ice dynamics have an anisotropic behaviour, we additionally want to directly encode the extraction of localised features with a directionaldependent weighting into the NN. Therefore, as depicted in Fig. 4 , we use an NN based on a convolutional U-Net architecture  (Ronneberger et al. , 2015). For a more technical description of this NN architecture, we refer to Sect.
Data generation and training We train and test different NNs with twin experiments using the regional sea-ice model, as described in Sect. 2. We simulate high-resolution truth trajectories with a resolution of 4 km and an integration step of 8 s and low-resolution forecasts with an 8 km resolution and a 16 s step. The NNs are trained to correct these low-resolution forecasts for a lead time of 10 min and 8 s. We train the NNs on an ensemble of 100 trajectories. The NN hyperparameters, like the depth of the network or the number of channels, are tuned against a distinct validation dataset with 20 trajectories. Finally, the scores are estimated using an independent test dataset with 50 trajectories. All high-resolution trajectories are initialised with a randomly chosen cohesion field and randomly drawn forcing parameters, as specified in Table  1.
Description Value Cohesion C U (5 × 10 3 Pa, 1 × 10 4 Pa) Amplitude A U (8 m s -1 , 20 m s -1 ) Wave length λ U (50 km, 200 km) Phase φ U (-100 km, 100 km) Advection ν U (-0. 5 m s -1 , 0. 5 m s -1 ) Base velocity u 0 max(20 m s -1 -A, U (0 m s -1 , 10 m s -1 )) model until the forecast lead time of 10 min and 8 s. To generate the datasets for the training of the NNs, these forecasts are compared to the projected truth fields at the same lead time. These datasets contain input-target pairs. The inputs for the NNs consist of 20 fields: nine forecast model fields and one forcing field for the initial conditions and the forecast lead time. The targets are the difference between the projected truth and the forecasted state at the forecast lead time and consist of nine fields. The inputs and targets are normalised by a global per-variable mean and standard deviation, both estimated from the training dataset.
The hourly slicing gives us 48 samples per trajectory, resulting in 4800, 960, and 2400 samples for the training, validation, and test dataset, respectively. In total, the training dataset has 12. 3 × 10 6 degrees of freedom (number of samples × number of variables × number of grid points). The NN configuration used in our experiments (see also Table  B1  in Sect. B) has 1. 2 × 10 6 parameters, an order of magnitude smaller than the degrees of freedom in the training dataset. During training, the NNs experience no overfitting, even if only 10 % of the training data are used, as shown in Sect. D1. We train the NNs by minimising a loss function proportional to a weighted mean absolute error (MAE); a more rigorous treatment of the loss function can be found in Sect. B3. The MAE is estimated for each variable independently.
To average these MAEs across all variables, the individual MAEs are weighted by a per-variable weight. The weights are learned alongside the NN and can be seen as an uncertainty estimate from the training dataset. In our case, the weighted MAE empirically performs better than a weighted mean-squared error loss function and than if the weighting is automatically learned from data (Sect. D3). If not otherwise specified, all NNs are trained for 1000 epochs, with a batch size of 64. To optimise the NNs, we use Adam  (Kingma and Ba, 2017)  with a learning rate of γ = 3× 10 -4 , β 1 = 0. 9, and β 2 = 0. 999. We refrain from learningrate decay or early stopping, as such methods would make the experiments harder to compare. All experiments are performed on the CNRS/IDRIS (French National Centre for Scientific Research) Jean Zay supercomputer, using a single NVIDIA Tesla V100 GPU or NVIDIA Tesla A100 GPU per experiment.
Results We propose a baseline architecture based on the U-Net, as described in Sect. 3, in the following simply called U-NeXt. We have selected the parameters of the U-NeXt architecture (see also Table  B1  in Sect. B) after a randomised hyperparameter screening in the validation dataset with 200 different network configurations. We evaluate our trained NNs on the test dataset, with the mean absolute error (MAE) in the low resolution. To get comparable performances across the nine model variables, we normalise their errors by their expected MAE in the training dataset. Note that this normalisation results in a constant weighting, differing from the adaptive weighting used during the training process, which depends on the training trajectory. Furthermore, this normalisation allows us to estimate the performance of the NNs with a single metric, averaged over all model variables.