the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ground motion prediction maps using seismicmicrozonation data and machine learning
Federico Mori
Amerigo Mendicelli
Gaetano Falcone
Gianluca Acunzo
Rose Line Spacagna
Giuseppe Naso
Massimiliano Moscatelli
Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by the local stratigraphic and/or topographic setting and buried morphologies (e.g., irregular subinterface between soft and stiff soils) that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the nearrealtime prediction of ground motion variation over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16 000 accelerometric data points and about 46 000 geological and geophysical data points was retrieved from Italian and European databases. The intensity measures of interest were estimated based on nine input proxies. The adopted machine learning regression model (i.e., Gaussian process regression) allows for improving both the precision and the accuracy in the estimation of the intensity measures with respect to the available nearrealtime prediction methods (i.e., ground motion prediction equation and ShakeMaps). In addition, maps with a 50 m × 50 m resolution were generated, providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed subsoil models.
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Spatial distributions of ground motion induced by seismic events should be properly estimated to support risk mitigation policies over large areas. Moreover, seismicrisk analysis, extended to spatially distributed anthropic systems, presents new challenges in characterizing the seismicrisk input, regarding the spatial correlation of the ground motion values where the spatial correlation is the spatial characteristics of the ground motion arising from similarities in the seismicwave paths and local site effects. The ShakeMaps (Wald et al., 2021), provided by the US Geological Survey, are used globally for postearthquake emergency management and response, engineering analyses, financial instruments, and other decisionmaking activities. Moreover, in Italy postevent ShakeMaps are delivered by the National Institute of Geophysics and Volcanology (Michelini et al., 2019; ShakeMap, 2021). Such ShakeMaps are based on ground motion prediction equation (GMPE; among others Bindi et al., 2011) and data recorded from accelerometric stations when available.
Recently, artificialintelligencebased procedures were proposed to produce nearrealtime ground motion in terms of acceleration time histories (Jozinović et al., 2021; Tamhidi et al., 2021) and intensity measure (briefly, IM; among others Kubo et al., 2020). In general, ground motion maps were generated using earthquake source parameters (location, magnitude, and the finite fault if available); IM (peak ground acceleration, peak ground velocity, and spectral acceleration, briefly named PGA, PGV, and Sa, respectively) at the recording accelerometric stations; and the mean shearwave velocity in the upper 30 m, V_{S30}, as a proxy to account for site lithostratigraphic amplifications. Having ShakeMaps only when the first location and magnitude estimation are available, Jozinović et al. (2021) propose to use waveforms to predict the ground motion intensity by means of a machine learning (briefly, ML) approach (i.e., it utilizes only a training set of earthquake waveforms recorded at a preconfigured network of recording stations). Moreover, ML has been adopted to produce seismicamplification factors maps, as in the Japan case study proposed by Kim et al. (2020), rather than to provide ground motion maps. Finally, Zhou et al. (2020) propose a seismic topographiceffect prediction model.
Overall, the abovementioned works have pointed out the following.

Hypocentral depth (H), epicentral distance (R), and magnitude (M) are widely used to estimate ground motion over large areas considering the source effect. Moreover, H, R, and M are provided a few minutes after an earthquake.

V_{S30}, the fundamental frequency of the deposit (f_{0}), and the depth to the engineering bedrock (H_{800}) are the key parameters which gauge well the effect of local subsoil conditions on the seismicwave propagation (i.e., lithostratigraphic effect). The only V_{S30} was used in the adopted ML approach, since the Italian V_{S30} map was provided by Mori et al. (2020a), while national f_{0} and H_{800} maps are not currently available.

Elevation (h), topographic gradients (h_{x} and h_{y}, where x and y are two orthogonal directions), and secondorder topographic gradients (h_{xx} and h_{yy}) are proxies which allow for describing the morphological effects on the seismicamplification phenomena.
In this view, this work focuses on the improvement of ground motion prediction over large areas by using the ML technique. The main task of this work is to suggest a procedure including all the main key parameters together (i.e., H, R, M, V_{S30}, h, h_{x}, h_{y}, h_{xx}, and h_{yy}).
The damage pattern induced by seismic events is related to both geological/geomorphological conditions and the vulnerability of structures and infrastructures (Brando et al., 2020; Fayjaloun et al., 2021; Mori et al., 2020b, 2019). The ground motion prediction (i.e., seismic site response) is generally evaluated by means of numerical simulations which are time consuming and require welldetailed models capable of properly representing subsoil and topographic conditions (see, for example, Bouckovalas and Papadimitriou, 2005; Falcone et al., 2020a, b, 2018; Gatmiri and Arson, 2008; Gazetas, 1982; Luo et al., 2020; Moscatelli et al., 2020b; Pagliaroli et al., 2014; Pitilakis et al., 1999; Régnier et al., 2016, 2018).
Hence, the ML approach was adopted to
 i
implement the H, R, and M parameters available a few minutes after a seismic event;
 ii
include both lithostratigraphic (V_{S30}) and morphological effects (h, h_{x}, h_{y}, h_{xx}, and h_{yy});
 iii
capture the spatial correlation at short distances (hundreds of meters) due to local site effects, which is essential for reliable hazard assessments.
The main results of these elaborations are ground motion prediction maps (i.e., PGA, PGV, and Sa) with the resolution of 50 m × 50 m, which can reproduce the variability captured by advanced numerical modeling.
Seismological data (i.e., H, R, M, PGA, PGV, and Sa) retrieved from European and Italian networks (Luzi et al., 2016, 2019, 2020); geological, geophysical, and geotechnical data from seismicmicrozonation (hereafter SM) studies (DPC, 2021); and morphological data (ALOS, 2021) are presented in Sect. 2. The ML approach is discussed in Sect. 3. In detail, the Sect. 3.1 is focused on the adopted ML approach in terms of training and validation phase. Performances, presented in terms of root mean square error (RMSE) and residuals (i.e., difference between the base10 logarithms of observed and predicted values of PGA, PGV, and Sa), are compared to the results proposed by other studies (Jozinović et al., 2021; Michelini et al., 2019; Bindi et al., 2011).
For the seismic sequence that hit central Italy in 2016–2017, ML results and maps are shown in Sects. 3.2 and 4, respectively. Referring to the seismic event that occurred in central Italy on 30 October 2016, a test is proposed in Sect. 3.2 in terms of residuals of the ground motion IMs (i.e., PGA, PGV, and Sa). Ground motion prediction maps for the central Italy event that occurred on 24 August 2016 (i.e., the first destructive event of the central Italy seismic sequence for which a large number of studies have been published) are shown in Sect. 4 to demonstrate the capability of the proposed ML approach to gauge the ground motion variability at the urban scale. Moreover, with reference to Sect. 4, the ground motion profiles, based on the proposed ML approach, are compared with results obtained by means of two completely different methodologies: 2D numerical modeling of seismic site response (Gaudiosi et al., 2021; Giallini et al., 2020; Grelle et al., 2020) and the mean values predicted by the Italian ShakeMap (2021).
The input and output data for the training of the ML approach were classified into three categories: seismological, geophysical, and morphological data. The ML approach was based on 15 779 seismological data points regarding the log_{10} geometric mean of the horizontal component (geoH) for each IM (i.e., PGA, PGV, and Sa at 0.3, 1.0, and 3.0 s). Each value recorded by the accelerometric station, named output data in Table 1 (i.e., data to be reproduced by means of ML), represents an observed datum. In addition, Table 1 lists the nine used predictors, named input data. Figure 1 shows the location of the selected accelerometric stations. Figures 2 and 3 show the input and output data, respectively, adopted for the training phase of the selected ML approach and presented in this section. Furthermore, some data distributions seem to be imbalanced (e.g., magnitude, M, and elevation, h; Fig. 2). An imbalanced training input dataset is characterized by an unequal distribution of values. For instance, focusing on Fig. 2 and the M distribution, the first and third quartiles are 4.1 and 5.1, respectively. Moreover, focusing on elevation distribution, the first and third quartiles are 136 and 761 m, respectively. Consequently, when the ML algorithm learns the imbalanced data (see, for example, Kubo et al., 2020), the learning focus is mainly on the fit of ground motion with a magnitude lower than 6 or on the fit of the site characterized by elevation lower than 1200 m. The imbalance of the selected training input dataset seems to be caused by a sampling bias, since no highmagnitude ground motions were registered by the available accelerometric stations and since few accelerometric stations have been installed at high elevation, where the exposition at a seismic event is very low. Hence, the training dataset cannot actually be improved. In addition, distributions of topographic gradients and V_{S30} are characterized by few data points with respect to steep slopes and high V_{S30} values. How to handle the imbalanced dataset in the regression problem was out of the scope of this work. Consequently, referring to a range of an input datum, it is expected that the lower the number of training data is, the higher the uncertainty is. To this end, referring to the output data, maps of the standard deviation are reported in Sect. 4.
Seismological parameters are retrieved from Italian and European databases. Regarding 1435 recording accelerometric stations, PGA, PGV, spectral accelerations (i.e., Sa at 0.3, 1, and 3 s), H, R, and M were retrieved from the Engineering StrongMotion Database, briefly ESM (Luzi et al., 2016; ESM, 2021), and ITalian ACcelerometric Archive, herein ITACA (Luzi et al., 2019). In detail, data regarding the central Italy earthquake occurred in 2016 and recorded by a temporary network named 3A have been archived only in the ITACA database (ITACA, 2021). It is worth noting that Greek and Turkish seismicevent data were collected to consider an earthquake characterized by an M value greater than 6.5 and up to 7.6. Moreover, an earthquake characterized by H, R, and a log_{10} PGA value greater than 30, 400 km, and 2 cm s^{−2}, respectively, were selected. It should be noted that the ITACA and ESM selected data consider the shallow active crustal region (i.e., SACR zone characterized by shallow events, H<35 km, in agreement with Michelini et al., 2019). The distributions of seismological data of the chosen events are shown in Figs. 2 and 3. The same figures also show the distribution of data described in the next part of this section.
The dynamic site condition was described by means of the timeaveraged shearwave velocity (V_{S}) to a depth of 30 m, the V_{S30} parameter. It is worth noting that the V_{S30} parameter has been successfully adopted to gauge lithostratigraphic effect on seismicwave propagation by Falcone et al. (2021). V_{S30} data (i.e., input data in the ML approach), determined by means of in situ investigations, are also archived in the ESM and ITACA databases. V_{S30} values were retrieved from Mori et al. (2020a) for ESM and ITACA sites not characterized by in situ surveys. Figure 2 shows the distribution of the V_{S30} data.
The V_{S30} map proposed by Mori et al. (2020a), based on SM studies, was adopted here. The SM studies have been carried out for the Italian municipalities through the funds allocated after the 2009 L'Aquila earthquake, in the framework of the Italian program for seismicrisk prevention and mitigation (Moscatelli et al., 2020a). Approximately 4000 SM studies have been already planned, representing about 99.8 % of the municipalities eligible for funding (i.e., having a 475year return period PGA ≥ 0.125 g). Out of the 4000 planned SM studies, about 75 % have been completed and approved (DPC, 2021). The SM studies permitted the collection, classification, and archival of geological, geophysical, and geotechnical data with a uniform approach following national standard criteria (SM Working Group, 2008; TCSM, 2018). The data from in situ tests are organized into a database and georeferenced through an appropriate geographic information system (DPC, 2021). About 35 000 borehole logs and 11 300 V_{S} profiles, related to about 1700 downhole and 9600 MASW (multichannel analysis of surface waves) tests, were extracted from the SM dataset. Starting from the 11 300 V_{S} profiles, V_{S30} values were calculated. Mori et al. (2020b) derive a largescale V_{S30} map for Italy, starting from the global morphological classes after Iwahashi et al. (2018), by integrating the large amount of data from the Italian SM dataset. The V_{S30} map by Mori et al. (2020a) was used here to integrate data where sitespecific information was not available.
The morphological elevation, h (i.e., an input morphological datum), was retrieved by the Advanced Land Observing Satellite (ALOS) World 3D30m (herein AW3D30) digital elevation model (DEM). The free version of the DEM (ALOS, 2021) adopted here has 1 arcsec resolution, which is equivalent to approximately 30 m at the Equator. AW3D30 global DEM data were produced using the data acquired by the Panchromatic Remotesensing Instrument for Stereo Mapping operated on the ALOS from 2006 to 2011. The Japan Aerospace Exploration Agency, which is the operator of the satellite, produced the global DEM using approximately 3 million images. Considering that the AW3D30 model is the digital surface model which represents the canopy top and building roofs' elevations, Caglar et al. (2018) found that AW3D30 is the most accurate DEM among other similar data elevation products freely available. In detail, it was shown that the AW3D30 root mean square error is equal to 1.78 m.
Finally, the GRASS GIS (Geographic Resources Analysis Support System geographic information system) command r.slope.aspect
(https://grass.osgeo.org, last access: 1 September 2021) was used to generate
the other morphological proxies (i.e., h_{x}, h_{y}, h_{xx}, and
h_{yy}). Such a command generates raster maps of first and secondorder
partial derivatives from a raster map of true elevation values (i.e., AW3D30
data in this study). Figure 2 shows the distribution of the selected
morphological data.
The “MATLAB Regression Learner App” tool (https://it.mathworks.com/help/stats/regressionlearnerapp.html, last access: 1 September 2021) was employed to produce ground motion prediction maps using a supervised ML approach. With this application, users can choose the desired models among many different methods to automatically train and validate regression models. After training multiple models, they can be compared to choose the best one. The application includes commonly used regression methods such as linear regression models, decision trees, support vector machines, ensembles of tree models, and a Gaussian process regression (GPR).
Figure 4 shows the adopted ML workflow. After having imported and selected the data (input variables and output variables), the training and validation phases begin. In these phases the ML model that will be used is “adapted”, or rather the algorithm is adapted to the training dataset. One of the objectives of this phase is the tuning of the model, acting on the hyperparameters (parameters whose value is used to control the learning process) of the algorithm to minimize errors. The kfold crossvalidation technique was used in this work. The models included in MATLAB Regression Learner App tool have all been tested. The fitting performance (in terms of RMSE) on the validation set was considered an indicator of the generalization ability of models. Among the available models the bestfitting performance in terms of RMSE was provided by the GPR model with an exponential kernel (Table 2). GPR is a nonparametric, Bayesian approach to regression, which provides uncertainty measurements on the predictions. Moreover, a detailed description of the GPR method is outside the scope of this work. Suggested references for comprehensive descriptions of the GPR method are Rasmussen and Williams (2006) and chapter 6 of MathWorks (2019). The abovementioned kfold crossvalidation (k=5) method is described in chapter 24 of MathWorks (2019).
The second step is to test the model with the best performance (GPR with an exponential kernel in this research), adopting a dataset not included in the training and validation phases. The dataset for the 30 October 2016 seismic event was used, since the accelerometric data of many accelerometric stations are available. The test is used to evaluate the accuracy of the model in terms of residuals (Eq. 1). In the workflow of Fig. 4 there is also a phase (comparison) that is not part of the standard ML methodology. The comparison with the ground shaking obtained by completely different methodologies was used to further analyze the ML model in terms of ground motion resolution and variability.
Training and crossvalidation phases are described in Sect. 3.1. Comparison in terms of residuals with the performance of the existing methods (i.e., an external test) is presented in Sect. 3.2. The comparison with the ground shaking obtained by completely different methodologies is presented in Sect. 4.
3.1 Training and validation phases
The mean RMSE values of the five crossvalidation datasets were adopted to select the best ML approach. With reference to the tested ML approaches, Table 2 lists the RMSE values for each predicted IM.
Referring to the best prediction model (i.e., GPR with an exponential kernel) and to the training dataset, Fig. 5 shows the comparison between predicted and observed values.
The performance of the GPR model is also presented in terms of the mean value and standard deviation of the residuals' distributions (Table 3), where the residual is defined according to Eq. (1) in agreement with what was presented by other researchers (Bindi et al., 2011; Jozinović et al., 2021; Michelini et al., 2019). It should be noted that the mean and standard deviation of the residuals' distributions referred to the ShakeMap and GMPE that were retrieved from the work of Jozinović et al. (2021) to evaluate the performance of the ML approach suggested in this study. It is worth noting that the suggested ML approach provides the best performance with respect to the approaches proposed by the other studies in terms of both accuracy (mean value) and precision (standard deviation). In detail, the standard deviation values are reduced by 45 %–60 %.
Figure A1 in Appendix A1 shows the contribution of each predictor variable to the reduction of the standard deviation of the residuals' distribution.
3.2 Testing phase
Input and output data for the 30 October 2016 seismic events were selected as an external test dataset not included in the training data. The seismic events in central Italy of 2016 and 2017 began in August 2016 with epicenters located between the regions of Latium, Marche, and Umbria. The first strong shock occurred on 24 August 2016, at 03:36 and had a magnitude of 6.0, with its epicenter located along the Tronto River valley, between the small municipalities of Accumoli and Arquata del Tronto. Two powerful replicas took place on 26 October 2016, with epicenters on the Umbria–Marche border, the first shock with a magnitude of 5.4 and the second with a magnitude of 5.9. On 30 October 2016, the strongest quake was recorded, with a moment magnitude of 6.5 with its epicenter in the region of Umbria. On 18 January 2017, a new sequence of four strong tremors with a magnitude greater than 5 (with a maximum of 5.5) and epicenters located in the region of Abruzzi took place. This set of events displaced a total of about 41 000 persons and caused 388 injuries and 303 deaths.
In detail, the paper refers to the 30 October 2016 main shocks, since according to the available data much more accelerometric data are available, and it is therefore possible to make more detailed and reliable analyses.
Mean and standard deviation values of the residuals' distributions are presented in this section for the seismic event that occurred on 30 October 2016 (briefly named the test event), because it is the event with the most recordings of the whole dataset (241 accelerometric stations). It is worth noting that this event was not included in the dataset adopted for the training phase of the ML approach. Noting that 943 seismic events were characterized by M≤6 and 25 earthquakes by M>6 (see Fig. 3 for the training dataset), the central Italy earthquake that occurred on 30 October 2016 (M=6.5) provides a robust test of the adopted ML approach. The GMPE proposed by Bindi et al. (2014) (hereafter also the Bindi GMPE) was selected to estimate the IMs at the 241 sites of interest aiming to compare the performances of the GMPE and this ML approach. It should be noted that the Bindi GMPE provides IMs depending on the V_{S30} as in this study. Furthermore, the OpenQuake software (Pagani et al., 2014) was used to determine the IMs values based on the selected GMPE.
Mean and standard deviation values regarding the test event (Table 4) are higher than those referred to in the training and validation phase (Table 3), as expected, because the GPR model is trained on a few events with high magnitudes as discussed in Sect. 2.
Moreover, mean and standard deviation values obtained in this example are lower than those obtained by means of the GMPE as shown in Table 4. In detail, the standard deviation values are reduced by 20 %–30 %. Therefore, the overall performance of the proposed ML approach is satisfactory also at the highest magnitude.
After having demonstrated the goodness of the proposed method to reproduce IM values, this chapter presents examples of predictive maps produced by means of the exponential GPR model with a 50 m × 50 m resolution. In this section the map for the 24 August 2016 central Italy seismic event is produced to compare some significant IM profiles produced with independent advanced numerical simulations and data retrieved from ShakeMaps (2021).
The ground motion prediction map of the Sa_{0.3} reported in Fig. 6 is one of the cartographic results of this study; maps of PGA, PGV, and other spectral ordinates are in the Supplement. Macroseismic intensities, I_MCS, retrieved by Galli et al. (2017) are also reported next to the name of the villages in Fig. 6. These maps were chosen because the 0.3 s period is the fundamental vibration period of most buildings in the area (i.e., twotothree story buildings). Moreover, 0.3 s is compatible with the results of modeling provided by Gaudiosi et al. (2021), Giallini et al. (2020), and Grelle et al. (2020) for the same areas.
The map of Fig. 6 shows an output that is in good agreement with the geophysical data (i.e., V_{S30} in Fig. 7) and geomorphological data (i.e., elevation and slope in Figs. A2 and A3 in Appendix A2 and A3, respectively) and, therefore, highlights local site effects. In fact, referring to Fig. 6, it should be noted that the highest Sa_{0.3} values describe the valleys' trend (i.e., the largest and continuous Tronto River valley) and the two extended areas in the southern part of the map (i.e., near the villages of Petrana and Torrita) well, which are characterized by the lowest values of V_{S30} (Mori et al., 2020a). Figure 8 shows the ShakeMap of Sa_{0.3} regarding the central Italy earthquake that occurred on 24 August 2016 for the same area sketched in Fig. 6. As a general issue referring to the ShakeMaps, the higher the distance from the epicenter is (the star in Fig. 8), the lower the predicted Sa_{0.3} is. Hence, the ShakeMaps does not provide ground motion variability induced by the local site condition (i.e., subsoil setting and topography). In detail, the ShakeMap provides an Sa_{0.3} value equal to 0.36 g for the entire area of Arquata del Tronto (square A in Fig. 8) and equal to 0.99 and 1.08 g for Amatrice (square B in Fig. 8).
Referring to A and B closeups in Fig. 6, Fig. 9 shows the mean values of Sa_{0.3} on the left side and the standard deviation values on the right side. It should be noted that the uncertainty is provided by a combination of the input data values. The uncertainty increases referring to input data values for which the ML is not well trained (Figs. 2 and 3 and discussion in Sect. 2). For instance, standard deviation values around 0.3–0.4 are in the areas of inhabited villages, characterized by input data values widely represented in the training dataset, while values in the range of 0.6–0.8 are observed in correspondence with the combination of high slope values and high V_{S30} values, which are underrepresented in the training dataset.
In addition to the maps, Fig. 10 shows the profiles (two at Amatrice and one at Arquata del Tronto) of Sa at 0.3 s and the comparison with the values of the same shaking parameter, calculated with different methodological approaches: ground motion prediction with an ML approach (this study), 2D numerical simulations (modified after Gaudiosi et al., 2021; Giallini et al., 2020; Grelle et al., 2020), and ShakeMap (2021). All the models are defined for the geometric mean (geoH) of the horizontal components. As ShakeMaps are released for the maximum of the horizonal components, the ShakeMap values are converted to geoH according to the empirical relation proposed by Beyer and Bommer (2006). The three profiles were chosen because they represent three very different geological and geomorphological structures: a narrow valley (section AA' in Fig. 10, Arquata del Tronto), a plateau of soft ground (section BB' in Fig. 10, Amatrice), the morphology of a mountain peak covered with soft ground (section CC' in Fig. 10, close to Amatrice). As a matter of fact, the adopted ML approach reproduces the socalled valley effect, as in the case of the Arquata del Tronto shallow valley (see the trend for $\mathrm{200}\le x\le \mathrm{400}$ m in AA'); combined lithostratigraphic and topographic effects, as in the case of the village of Amatrice (see the trend for $\mathrm{200}\le x\le \mathrm{500}$ m in BB'); and topographic amplification, as in the case of the AMT (Amatrice station code) accelerometric station (see the trend for $\mathrm{100}\le x\le \mathrm{200}$ m in CC'). It should be noted that the trend of the values of our study reproduces that of the numerical simulations and get closer to the recorded values at the Osservatorio Sismico delle Strutture (OSS, a network of buildings and bridges monitored in continuum by the Italian Civil Protection Department) site and AMT station (Luzi et al., 2019; stars in BB' and CC'). Moreover, the profiles provided by the ML approach are much more articulated and complex than the constant value (horizontal dashed line) of the ShakeMap, which obviously fails to grasp the local site effects at this scale. The difference between the different methodology and the recorded values were quantified according to the following equation and are provided in Table 5.
Intensity and frequency contents of ground motions can be altered by many factors. Up until now, numerous empirical models of ground motion amplification have been developed based on conventional regression analyses, considering a few key factors such as intensity measures of rock motions, shearwave velocities of soils, and territory morphology. Since machine learning techniques have been applied to many fields, this work investigated on efficacy of using such techniques for developing models to predict ground motion over large areas with a 50 m resolution raster.
A set of about 16 000 ground motion data points from Italian and European networks were adopted to train a Gaussian process regression model, while recordings by 241 stations of the seismic events occurred in Italy on 30 October 2016 were used to test the same model. Peak ground acceleration and velocity, as well as spectral acceleration at three periods (i.e., 0.3, 1, and 3 s), were compared to the recorded data, allowing for obtaining residuals. With reference to the training dataset, the mean value and standard deviation of the residuals' distribution were found to equal about 0 and about 0.1, respectively. With reference to the test dataset characterized by a magnitude equal to 6.5, the mean value and standard deviation of the residuals' distribution were found to equal 0.01 and 0.3, respectively. Hence, the performance of the adopted machine learning technique was confirmed satisfactory also for a magnitude higher than 6.
In addition, maps of ground motion in terms of peak ground acceleration, peak ground velocity, and spectral acceleration at the three selected periods were produced for the central Italy seismic event that occurred on 24 August 2016. Profiles of intensity measures were in satisfactory agreement with those obtained by means of advanced numerical simulations of seismic site response referring to the same seismic event. Moreover, the adopted machine learning approach greatly improves the performance of existing methods for the analyzed case studies.
Two main novelties of the work are synthesized in the following.

The forecast of ground motion with high resolution (i.e., a 50 m × 50 m raster) is in agreement with results of localscale numerical modeling. This outcome is achieved by means of machine learning techniques and large datasets including morphological, geological, geophysical, and geotechnical features (mainly the seismicmicrozonation dataset; DPC, 2021). Moreover, about 1000 seismic events recorded by 1435 accelerometric stations (ESM, 2021; ITACA, 2021) were analyzed. The machine learning approach combines morphological and subsurface proxies: elevation, first and secondorder topographic gradient (defining the morphological characteristics of the territory), mean shearwave velocity in the upper 30 m (defining the dynamic response of a site as induced by the subsoil condition), magnitude, and epicentral and hypocentral distances provide the source conditions.

Robust statistical techniques such as Gaussian process regression were used. Among the machinelearningbased models, the model developed by the regression and Gaussian approach provides the best performance in terms of both precision and accuracy, which are the standard deviation and mean value of the residuals' distribution, respectively.
In a nutshell, the novelty of this work is the use of the machine learning approach based on the analysis of a huge database of geological, geophysical, and geotechnical data, built with seismicmicrozonation studies for the entire Italian territory. The quality and quantity of this database allow for a robust application of machine learning including the prediction of local site effects (i.e., lithostratigraphic and morphological) on the seismic ground motion.
In terms of applications, the ground motion maps generated by means of the proposed machine learning approach are useful both for urban planning (aimed at reducing seismic risk) and for emergency management (aimed at a nearrealtime estimation of shaking scenarios). With reference to the emergency phase, by knowing the position and depth of the hypocenter and the magnitude of the event (in Italy these data are available a few minutes after the event), it is possible to produce ground motion maps in near real time. Overall, considering that the paradigm should be shifted from managing disasters to managing risk, the proposed methodology could represent a key tool in seismicrisk mitigation strategies deployed both before and after the seismic event.
Evaluation of the spatialcorrelation structure was studied to provide the relation between local site effects and the spatial resolution of ground motion maps; results of such an analysis were not reported in the main text, since it is out of the scope of this paper, while preliminary results in terms of sill and range are reported in Appendix A4, referring to the seismic event that occurred on 30 October 2016 (i.e., the strongest of the central Italy seismic sequence).
In conclusion, the research on this topic will continue and focus on specific goals, which include the following:

improving the method with more input proxies which are to be made available after the seismicmicrozonation project for the whole national territory (in detail, maps of the depth to the engineering bedrock and of the fundamental frequency of the deposit will be soon available and allow for using such parameters as input data for the machine learning approach)

improving the method with a worldwide seismological dataset

improving the spatial resolution of existing input proxies integrating remote sensing data

improving the spatialcorrelation analysis.
A1 The contribution of each proxy to the total reduction in the standard deviation of the residuals for the PGA
A4 Spatialcorrelation structure of the predicted maps
Here we want to preliminarily deal with the spatial correlation of the IM parameters. In fact, the spatial correlation of ground motion IMs represents a key issue in the seismicrisk assessment, particularly in loss analysis (Infantino et al., 2021; Schiappapietra et al., 2020, 2021). The geostatistical tool widely adopted to analyze the spatial correlation of geological and geotechnical data (Paolella et al., 2021; Raspa et al., 2008; Salvatore et al., 2019; Spacagna et al., 2018) is the semivariogram (Chilès and Delfiner, 2012). The spatial structure is evaluated by assessing the dissimilarity of the variables measured at different locations. First, referring to the variable of interest (in this case, one of the selected IMs), the experimental semivariogram, $\widehat{\mathit{\gamma}}\left(h\right)$, is calculated from data using the method of moments (Chilès and Delfiner, 2012):
where z(x_{i}) and z(x_{i}+h) are the observed values of the variable z (i.e., one of the selected IMs) at the location x_{i} and x_{i}+h separated by h and n(h) is the number of pairs at lag h. Under the assumption of secondorder stationary, the semivariogram increases with h up to a constant value of $\widehat{\mathit{\gamma}}\left(h\right)$. In this study, to assess the spatial structure of the variables (predicted IMs), the experimental variogram estimated from the predicted maps is fitted with the bestfit model (i.e., the exponential model):
where parameters a and C are called, respectively, range and sill. The range defines the correlation distance, namely, the separation distance at which the data are spatially independent, and the sill represents the variance of the random process, the limit value of γ(h).
For the central Italy event that occurred on 30 October 2016 and for all the
predicted IM maps (i.e., PGA, Sa_{0.3}, and Sa_{1}; see Fig. 6 and the Supplement), the spatial structure was performed with the gstat
package (Pebesma, 2004) of the R software program (R Core Team, 2021). The IM
values were extracted from the predicted maps with a regular punctual grid
of 50 m × 50 m. The isotropic experimental semivariograms were computed and
fitted with the abovementioned exponential model. As an example, Fig. A4
shows the semivariogram of the predicted Sa_{0.3} map. The spatial
structure of all predicted IM maps was characterized by the nested
exponential model. The nested variograms highlight the presence of a double
structure at different scales, i.e., a shortscale and a longscale
variability.
In this case, two ranges and two sills are obtained for two levels of variability. Table A1 shows the sill and range values for the nested exponential models of all predicted IM maps. The first range, or shortscale structure, captures the first source of variability (first sill) over hundreds of meters induced by lithostratigraphic site conditions and morphological variability. The longscale structure captures the variability over thousands of meters and could be referred to regional geological units and largescale morphological features. Furthermore, a significant part of the variance, around 30 %–40 % of the total, is captured at the short scale.
An exhaustive treatment of this topic is beyond the scope of this work. We are now studying the spatial variability of input parameters that contribute to generate the target IM maps, and this will be the subject of a future paper. By the way, the preliminary results enlighten the importance to generate ground motion prediction maps with a spatial resolution on the order of hundreds of meters to improve their quality in terms of predictivity. Seismichazard maps should also include these specifications to consider the shortscale effects, even if starting from basic hazard maps with a resolution on the order of 2–5 km.
No specific code was created.
Accelerometric data are available at https://data.ingv.it/dataset/404#additionalmetadata (INGV, 2022a) and https://data.ingv.it/dataset/446#additionalmetadata (INGV, 2022b). Microzonation data for Italy are available at https://www.webms.it/servizi/viewer.php (Sistema WebGIS, 2022). The V_{S30} map for Italy is available at https://doi.org/10.17632/8458tgzc73.1 (Mori, 2020c). The DEM is available at https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm (ALOS, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess229472022supplement.
FM and GA conceptualized the project. FM, AM, and RS curated the data. FM and RS performed the formal analysis. MM acquired funding. FM, AM, GF, GA, RS, MM, and GN conceptualized the methodology. MM administered the project. FM, MM, and GN supervised the project. FM, AM, GF, and GA validated the data. AM and GF visualized the data. FM, GF, and GN wrote the original draft of the paper. FM, AM, GF, GA, RS, MM, and GN reviewed and edited the paper.
The contact author has declared that neither they nor their coauthors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Earthquakeinduced hazards: ground motion amplification and ground failures”. It is not associated with a conference.
The authors would like to thank Fabrizio Bramerini, Sergio Castenetto, Antonella Gorini, and Daniele Spina (Italian Civil Protection Department) for the useful discussions. We also thank Silvia Giallini and Iolanda Gaudiosi (both from CNR IGAG, Italy) for providing the ground motion data obtained by means of numerical simulation for the Amatrice and Arquata del Tronto areas (Italy).
This research was supported by the Presidency of the Council of Ministers of the Italian Department for Civil Protection in the framework of the project “Contratto concernente l'affidamento di servizi per il programma per il supporto al rafforzamento della Governance in materia di riduzione del rischio sismico e vulcanico ai fini di protezione civile nell'ambito del PON Governance e Capacità Istituzionale 2014–2020” (grant no. CIG6980737E65; Massimiliano Moscatelli, scientific coordinator for CNR).
This paper was edited by HansBalder Havenith and reviewed by four anonymous referees.
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 Abstract
 Introduction
 Input and output data for machine learning training and validation
 Seismological parameters
 Geophysical data
 Morphological data
 Method
 Ground motion prediction map for the central Italy seismic event that occurred on 24 August 2016 and comparison with numerical modeling
 Discussion and conclusions
 Appendix A: Spatialcorrelation structure of the predicted maps and some clarifications requested during the revision phase
 Code availability
 Data availability
 Author contributions
 Competing interests
 Disclaimer
 Special issue statement
 Acknowledgements
 Financial support
 Review statement
 References
 Supplement
 Abstract
 Introduction
 Input and output data for machine learning training and validation
 Seismological parameters
 Geophysical data
 Morphological data
 Method
 Ground motion prediction map for the central Italy seismic event that occurred on 24 August 2016 and comparison with numerical modeling
 Discussion and conclusions
 Appendix A: Spatialcorrelation structure of the predicted maps and some clarifications requested during the revision phase
 Code availability
 Data availability
 Author contributions
 Competing interests
 Disclaimer
 Special issue statement
 Acknowledgements
 Financial support
 Review statement
 References
 Supplement