dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Building a dense lateral and vertical fault offset database from Lidar data. WP is a strike-slip and reverse NE-trending, NW-dipping fault that extends over 120-140 km from offshore in the Cook Strait  6, 14  to Mauriceville  24, 32  (New Zealand active fault database, https://data. gns. cri. nz/af) (Fig. 1 ). We acquired airborne and terrestrial Lidar data to examine its bare-earth morphology at high resolution (horizontal spatial sampling ≤1 m, vertical precision 5-10 cm) (Methods, and Supplementary Fig. 1 ). The Lidar stretch extends over 70 km from the southern Wairarapa lake to Mauriceville, which does not include the thrust zone at southern WP (Wharekauhau and Turakirae zone  5  ). As recognized before (https://data. gns. cri. nz/af), the fault generally appears as a single strand, and forms a sharp, fairly linear and continuous trace highlighted by a few meters-high, east-facing scarp (Supplementary Fig.
Most of those markers were previously unknown due to dense vegetation masking their trace in classical optical images and in the field. In fact, only 26 of these markers have been reported in the literature, along with the field measurements of their fault offsets  2, 4, 26, 29  (Supplementary Spreadsheet). Unpublished data (reported in  4  ) suggest that up to ~100 more markers were identified in earlier field studies, but their precise location and offset measures are not available. Here, we automatically measured the lateral and vertical offsets of the 643 markers, along both the main fault trace and the secondary faults, using the code "3D_Fault_Offsets" developed in an earlier work  34  (Methods, Fig. 2  and Supplementary Figs. 2 3 ; measurements in Supplementary Spreadsheet). As we generally analyzed different sections of varying lengths per marker, and each series of measures includes 9 measurements of the lateral offset and 9 measurements of the vertical offset, we eventually produced 15 800 total offset measures.
Of these, we only retained the most relevant and robustly constrained. The final database analyzed here includes 3567 lateral and 3567 vertical offset measures equaling more than 7000 offset measures. This is the largest offset database ever produced for any fault worldwide (see for instance  [35] [36] [37]  ). This is also the first time that cumulative vertical offsets are measured systematically along a strike-slip fault, including the WP. For comparison, only 25 lateral and 20 vertical field offset measures have been published so far on WP  2, 4, 26, 29  (Supplementary Spreadsheet). Our automatic offset measures across the 26 markers identified on the field are consistent with the prior field measures (~70% consistency for both lateral and vertical offsets, see Supplementary Fig. 4A ).
The OPT and PREF offsets are similar within uncertainties in 96% (lateral) and 91% (vertical) of cases. The lower uncertainty-PREF offsets are thus used to provide a more accurate description of the offset data. Of the entire dataset, 97% of the PREF and OPT lateral offsets are lower than 160 m, while 99% of the PREF vertical offsets are below 15 m (Supplementary Fig. 5 ). About 60% of the lateral offsets are greater than 18 m (maximum reported 1855 lateral slip  26  ) demonstrating that the offset collection includes cumulative offsets that were accumulated from the addition of multiple past earthquake slips. Up to 8 large historical and pre-historical earthquakes revealed on WP fault. Supplementary Figs. 6  and 7. Comprehensively, the lateral offset data fit into a rough triangle with an apex of ~140 m at 30-40 km along the fault.
8);  (3) all earthquakes have ruptured the same length of the WP fault (as suggested in earlier works  4  ; we come back to this point in Discussion section). Using a Monte Carlo approach, the code thus searches for the triangular functions that best fit the entire lateral offset dataset. The final best models are derived from 50,000 iterations. Figure  3b  presents the best model for the cumulative lateral offsets of Fig. 3a , while Fig. 3c  presents the 8 earthquake slip profiles derived from the model. While we describe the Fig. 3b  model here, similar results were obtained from modeling all quality PREF and OPT lateral offsets (Supplementary Figs. 9-12 and document A). Figure  4a  shows the coseismic and cumulative lateral slip profiles of the 1855 and 4 previous large earthquakes, respectively, inferred from the modeling (all profiles in Supplementary Fig.
Discussion Lidar data are confirmed to be invaluable in recovering the memory of past large earthquake slips on a fault  1, 36, 44. Our study also confirms the robustness of the code 3D-fault-Offsets  34  to automatically characterize the geometric properties of offset geomorphic markers in topographic data and measure their lateral and vertical offsets accurately. When these offset measures are dense, as is the case here, they form discrete clusters indicative of past earthquake slips and rupture lengths. The along-fault continuity of the three "lower", best-constrained offset clusters (Fig. 3a ) suggests that the three most recent earthquakes which produced them broke the entire length of the WP fault, which was indeed observed in the 1855 event. Based on this observation, we have hypothesized that all prior earthquakes identified in our offset data were similar, i.
9 ± 0. 5 km from west Lidar stretch, Supplementary document A), near Carterton, and decrease progressively on either side up to the fault tips. Our dense offset database provides a robust basis to identify the geomorphic markers most appropriate to date the identified earthquakes. Along with the measures of the largest and mean WP earthquake slips we provide here, this dating should help estimating the probability of rupture of the WP fault over the next 100 yrs  7, 28, 55. The WP fault produces earthquakes much stronger than commonly observed on continental faults of similar length. It has been suggested that such large stress drop earthquakes are primarily produced by immature faults  46  , i. e. , faults with short slip history generally less than a few Myrs. The WP fault is indeed immature  6, 48, 49.
Methods Lidar data acquisition and processing. We acquired the LiDAR data in the framework of the project CENTURISK funded by the French Research Agency (ANR, CENTURISK Risknat09-456076, PI: I. Manighetti) (Supplementary Fig. 1 ). Airborne LiDAR and digital imagery was collected between 8-9 April 2012, using NZ Aerial Mapping's Optech ALTM 3100EA LiDAR system and Trimble AIC medium format digital camera. The data was collected flying 900 meters above the ground, and using a field of view of 20 degrees either side of nadir. The system PRF was set at 70 kHz. The LINZ geodetic reference mark KB79 and a mark that NZAM established at Masterton airfield were used for the collection of GPS receiver station data during the aerial data acquisition. The LiDAR sensor positioning and orientation (POS) was determined using the collected GPS/IMU datasets and Applanix POSPac software.
This work was all undertaken in NZGD2000 coordinate system using the data collected at the geodetic reference mark for the DGPS processing. The POS data was combined with the LiDAR range files and used to generate LiDAR point clouds in New Zealand Transverse Mercator (NZTM) map projection but NZGD2000 ellipsoidal heights. This process was undertaken using Optech LMS LiDAR processing software. The data was checked for completeness of coverage. The relative fit of data in the overlap between strips was also checked. The point cloud data was then classified into ground, first and, intermediate returns using automated routines tailored to the project landcover and terrain. These, and subsequent steps were undertaken using TerraSolid LiDAR processing software modules TerraScan, TerraPhoto and TerraModeler.
The Trimble camera images were developed into 8 bit per channel uncompressed TIFF format images. The LiDAR POS data was transformed for use with the camera, and this data was used with the automated classified ground LiDAR point cloud data to produce orthophotos with a ground sample distance of 0. 2 m. Comprehensive manual editing of the LiDAR point cloud data was undertaken to increase the quality of the automatically classified ground point dataset. This editing involved visually checking over the data and changing the classification of points into and out of the ground point dataset. Attention was particularly focused on areas of vegetation. The Trimble orthophotos were used as a backdrop when undertaking the manual editing. The height and positional accuracy of the LiDAR data was checked by comparing it to overlapping LiDAR surveys that NZAM had conducted earlier at small locations within the project areas.
The new data was found to fit well with the existing data. To validate the efficacy of the airborne LiDAR to produce robust offset measurements even for smaller lateral offsets (i. e. < 10 m), we conducted a field campaign in April 2016 to acquire terrestrial LiDAR data (TLS) at three sites along the WP fault. We used a FARO Laser Scanner Focus 3D X 350 belonging to the laboratory Géoscience Montpellier. The scanner is a high-speed three-dimensional laser scanner. It can acquire >500,000 points/s from a distance of 0. 6 m up to 300 m. This large amount of points translates to a very high point density, enabling the generation of very-high resolution DEMs (e. g. , averaged spatial resolution ≤20 cm, measurement accuracy ≤2 mm). We acquired TLS measurements at 3 sites (location in Supplementary Fig.
Automatic offset measurements. We automatically measured the lateral and vertical offsets of the >640 markers using the Matlab code "3D_Fault_Offsets" our group developed recently  34  (code available in  34  ; Fig. 2  and Supplementary Figs. 2 3 ). The code is dedicated to analyze sub-linear geomorphic markers in topographic data. It works on a case-by-case analysis. In a topographic data subset enclosing a given offset marker, the user traces the fault line manually and draws two rough polygons enclosing the two marker sections. The fault dip is taken into account. The code then mathematically and automatically identifies in each polygon up to nine of the possible geometric characteristics per marker: the riverbed, identified as the zone of lowest elevation (Min Z referred to as "river", one point cloud); riser or scarp base and top (referred to as "bot" and "top"), identified through their slope break using the maximum (i.
e. maximum convexity of slope) and minimum Laplacian (i. e. maximum concavity of slope) of the topography, respectively (four point clouds on either side of a riverbed); riser or scarp steepest central part or "free face" (referred to as "mid"), identified through the measurement of the maximum gradient of the topography (two point clouds on either side of a riverbed); and the ridge or crest identified as the zone of maximum elevation (referred to as "Max Z"; two point clouds on either side of a riverbed). The code searches for these 9 specific features systematically along fault parallel topographic profiles that cover the entirety of the polygon. This eventually populates each polygon with nine individual point clouds on either side of the fault. Using the least-squares method, the code then computes a 3-D linear regression through each of the nine point clouds on either side of the fault, in effect creating 18 lines of best-fit.
After this first regression, the code automatically removes the artifact points related to the polygon edges and the obvious outliers of the lines (interquartile method). It then recalculates the best-fit lines from these "cleaned" point clouds. The eventual best-fit lines characterize the marker geometry in 3D. Note that some of the best-fit lines might not be well constrained or geomorphically relevant. If so, the user can remove them at a later stage. Each 3D line of best-fit intersects the dipping fault plane creating a piercing point whose x, y, and z coordinates are recorded. Based upon the common assumption that paired piercing points on either side of the fault were initially the same "pre-faulted" point, the code computes the 3 components of the slip vector that joins the paired piercing points.
It specifically calculates the horizontal and vertical offsets by subtracting the x and z coordinates of the corresponding piercing points on either side of the fault. The 18 offset calculations (nine lateral and nine vertical offset measures per marker) are done systematically regardless of their geomorphic relevance, which can be defined subsequently. Through a Monte Carlo approach, the code calculates the total uncertainty on each offset. Eventually, 3D_Fault_Offsets reconstructs the pre-faulted marker geometry in the horizontal and vertical planes (Fig. 2  and Supplementary Figs. 2 3 ). The code is entirely automatic and we used it with no manual intervention (but fault and polygon tracing). The 9 lateral and the 9 vertical offset measures obtained per marker provide a unique opportunity to examine the variability of the offsets across the entirety of the marker.
The code uses probability density functions (PDF) to extract the most robust offset values and their uncertainties. These uncertainties represent the largest possible errors on the offsets for they integrate the full range of offset variability. We used a constant 80 ± 10° NW dip for the analyzed faults. We generally used different polygon sizes per marker to best capture their geometry, and therefore performed several series of measures per marker (Supplementary Spreadsheet). To calculate the mean offsets per marker (OPT), we only retained the geomorphically meaningful offset values (explanation in  34  ). Because they integrate the full range of offset variability, they generally have fairly large, conservative uncertainties (generally 0-30% of the offset for lateral offsets, Supplementary Fig.
2 3 ). Offset modeling. We wrote a Matlab code, "Paleo_Slip-Length", to model the cumulative lateral offset data and recover the individual earthquake slips that accumulated over time to build them (Matlab 2019, version 9. 7. 0 (R2019b), Natick, Massachusetts: the MathWorks Inc. ). The code is based upon 3 hypotheses: (1) the measured offsets result from the addition of a variable number of discrete earthquake slips (hypothesis commonly made in paleoseismology  36  ); (2) the most recent earthquake slip profile (1855) and the earlier cumulative slip profiles have a triangular envelope shape, consistent with the generic form of both fault  41, 42  and earthquake  [38] [39] [40]  slip profiles worldwide; (3) all earthquakes have ruptured the same length of the WP fault (as suggested in earlier works  4  ).
The code is made of three connected routines: innermost, intermediate, and outermost. The innermost routine reads the data (L, D, W) where D and L are the measured displacement and its along-fault position, respectively, and W the weight on the offset. This weight is calculated with a separate routine; the user can define it as depending on the offset uncertainty (W = 1/sigma^2), on the quality weight (weights assigned to 1, 0. 75, and 0. 5 for Q1, Q2 and Q3, respectively), or on both (W = W_sigma x W_qual). We chose the latter case. The user provides the number, Ns, of functions supposed to be needed to model the data (i. e. , number of earthquakes), and the ranges for earthquake slip, along-fault position of largest earthquake slip, and rupture length. The inner routine then generates Ns triangular functions whose base lengths and apexes are randomly distributed within the ranges above.
Data availability The offset data generated and analyzed during the current study are tabulated and provided in Supplementary Spreadsheet. The code Paleo_Slip-Length is available at https://github. com/ClementPerrin/Paleo_Slip-Length. git. The Lidar data will be placed in early 2020 onto the OpenTopography repository (https://opentopography. org/). All intermediate data processing and calculations generated and analyzed during the current study are available from the corresponding author on reasonable request.
U-Pb dating was carried out by laser ablationsector fieldinductively coupled mass spectrometry (LA-ICP-MS) also at the Goethe University (Frankfurt, Germany) following the method described by  Zeh and Gerdes (2012). Laser spots were 30 mm in diameter and approximately 20 mm in depth. U and Pb content and Th/U ratio were calculated relative to GJ-1 reference zircon. Isotopic data were corrected for background, intra-run elemental fractionation, and common Pb using an inhouse EXCEL ® spreadsheet (for details see  Gerdes and Zeh, 2009)  that incorporates the Pb evolutionary model of  Stacey and Kramers (1975). All data U-Pb age data were corrected by standard bracketing relative to the zircon standard GJ-1 (primary standard;  Jackson et al. , 2004) , which yielded after background, daily drift, and common Pb correction a Concordia age of 603.
Acknowledgements. This work is part of Félix Djerossem PhD thesis conducted at the  Université Paul Sabatier , Toulouse 3 with a fellowship from the  French Embassy in Chad. This work is part of the  URCO  project funded by the  CNRS-NEEDS Program  for field and analytical expenses. Field work benefited from the assistance of  Issaka Ousman Al-Gadam  and  Ahmat Aboulfathi Haroun Djarma. Fabienne de Parseval  and  Jean-François Ména  are thanked for the confection of thin sections and  Thierry Aigouy  for assistance with the SEM. J. -P. Liégeois  and  R. Schmitt  provided constructive reviews;  R. Augier  and  L. Jolivet  are acknowledged for their editorial handling.
Alpine glaciers are shrinking and rapidly loosing mass in a warming climate. Glacier modeling is required to assess the future consequences of these retreats on water resources, the hydropower industry and risk management. However, the performance of such ice flow modeling is generally difficult to evaluate because of the lack of long-term glaciological observations. Here, we assess the performance of the Elmer/Ice full Stokes ice flow model using the long dataset of mass balance, thickness change, ice flow velocity and snout fluctuation measurements obtained between 1979 and 2015 on the Mer de Glace glacier, France. Ice flow modeling results are compared in detail to comprehensive glaciological observations over 4 decades including both a period of glacier expansion preceding a long period of decay.
Indeed, with the improvement of computational resource performance, running a model describing the Stokes ice flow solution for the complex three-dimensional geometry of a whole glacier has become much more affordable (e. g. ,  Réveillet et al. , 2015; Jouvet and Huss, 2019; Gilbert et al. , 2020). Among such models, Elmer/Ice  (Gagliardini et al. , 2013)  has already been used for a number of glacier applications (e. g. ,  Gagliardini et al. , 2011; Réveillet et al. , 2015; Gilbert et al. , 2020)  and will be used for this study. However, very few glacier datasets are available to make a detailed comparison between observed and modeled fluctuations at the multi-decadal scale. Mer de Glace offers a rare opportunity to compare state-of-the-art model results with a large dataset containing observed thickness changes, ice flow velocities and snout fluctuations over a nearly continuous 40year period thanks to the GLACIOCLIM observatory monitoring program  (Vincent, 2002; Vincent et al.
The large dataset of observations of thickness and centerline velocity collected at the Tacul cross section allows a good constraint on the flux passing through the gate. Observations at Leschaux cross section are limited, but, as presented in Sect. 3. 2. 3, the ice flow is much lower and has therefore a lower influence on the glacier evolution. Thus, in this study, the evolution of Mer de Glace is driven by two flux boundary conditions applied at the Tacul and Leschaux gates that prescribe the ice flux from the upstream accumulation areas of the glacier. The flux coming from the upper part of the glacier through the Tacul gate boundary condition is imposed from observations (thickness and central horizontal velocity at the Tacul gate) from the past and the estimated flux in the future. A similar method based on a flux is applied at the junction with Leschaux glacier.
The implementation of an ice flux at these two gates for hindcast and forecast simulations differs slightly and is described in detail in Sect. 3. 2. 3. Our simulations cover the period 1979-2050. The hindcast simulation covers the period 1979-2015 for which surface mass balances, surface velocities and elevation changes are available yearly at the four cross sections of Tacul, Trélaporte, Echelets and Montenvers (Fig. 1 ). The dataset at the Tacul cross section is used to specify the flux on this artificial boundary of the glacier domain, while the three others are used to evaluate the model over the hindcast period. For the forecast simulations from 2015 until 2050, results from climate simulations are used to simulate the future flux evolution at the boundary of the glacier domain. Sections 3.
2 m yr -1 (100 m) -1. Despite a strong variability from year to year (Fig. S1  in the Supplement and  Rabatel et al. , 2005) , a constant surface mass balance gradient of k b = 0. 9 m yr -1 (100 m) -1 is adopted for hindcast and forecast simulations. For future simulations, the surface mass balance at the Tacul gate in Eq. (  3 ) is inferred from a series of 26 downscaled and adjusted regional climate projections of the EURO-CORDEX program  (Jacob et al. , 2014). The adjustment was performed using the ADAMONT method (Verfaillie et al. , 2017) using the SAFRAN (Système d'analyse fournissant des renseignements atmosphériques à la neige) reanalysis  (Durand et al. , 2009)  as an observation reference, as described in  Verfaillie et al. (2018). The 26 climate projections used here span the three representative concentration pathways (RCPs) (see Table  S1  in the Supplement).
ice thickness equivalent, are 0. 0048 m • C -1 d -1 for snow and 0. 0053 m • C -1 d -1 for ice, as calibrated by  Réveillet et al. (2017)  for Mer de Glace. The surface accumulation is the sum of the solid precipitation (snow) and winter liquid precipitation (rain); it is assumed that during winter any rain that falls freezes and remains in the snow pack. Previous works (e. g. ,  Gerbaux et al. , 2005; Réveillet et al. , 2017; Vionnet et al. , 2019)  show that precipitation is underestimated in some reanalysis datasets. A comparison of precipitation simulated by SAFRAN reanalysis  (Durand et al. , 2009)  with the annual surface mass balance at Tacul between 1979 and 2015 and with the observed winter accumulation data available after 1994 in the accumulation area (see Supplement) indicates that the SAFRAN precipitation must be increased by 63 % to best fit the observations, which is in good agreement with  Réveillet et al.
(2017). The same method is then repeated for the climate scenarios adopted for this study. For each scenario, the correction factor for precipitation is evaluated over the past period 1979-2015. On average, simulated precipitation must be increased by 70 % to fit observations with only slight differences from one scenario to another. The value of 70 % is therefore applied to all scenarios. The surface mass balance at the Tacul gate obtained after 2015 with the PDD model and the corrected precipitation from the 26 different climate scenarios constitute the forcing data for the 26 forecast simulations. The same relation as for the hindcast simulation (Eq. 3) is then used to infer the spatial distribution of the surface mass balance.