DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

Karimzadeh, Sadra; Feizizadeh, Bakhtiar; Matsuoka, M.

doi:10.3390/ijgi8120537

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…The previous methodologies suggested the individual proxy dependence of V S30 related to geomorphological, terrain, and geological properties through a stochastic approach, such as a lognormal linear regression model [11][12][13][14]44]. The best-fitting models for the training area had the lowest residuals for various cost function metrics and presented a good performance by applying K-fold cross-validation.…”

Section: Discussionmentioning

confidence: 99%

“…The digital elevation model (DEM) and digital terrain model (DTM) are useful for developing V S30 maps based on geographic information system (GIS)-based regression analysis [11]. DEMs of various resolutions for several regions across the United States were used to examine the use of optimum resolution DEMs in developing V S30 maps [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow

et al. 2021

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Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial zonation studies on irregular, surficial, and subsurface systems of inaccessible areas. Herein, a data-driven workflow for extracting the principal features using a digital terrain model (DTM) is proposed. In addition, geospatial grid information containing terrain features and the average shear wave velocity in the top 30 m of the subsurface (VS30) are employed using geostatistical interpolation methods; machine learning (ML)-based regression models were optimized and VS30-based seismic zonation in the test areas in North Korea were forecasted. The interrelationships between VS30 and terrain proxy (elevation, slope, and landform class) in the training area in South Korea were verified to define the input layer in regression models. The landform class represents a new proxy of VS30 and was subgrouped according to the correlation with grid-based VS30. The geospatial grid information was generated via the optimum geostatistical interpolation method (i.e., sequential Gaussian simulation (SGS)). The best-fitting model among four ML methods was determined by evaluating cost function-based prediction performance, performing uncertainty analysis for the empirical correlations of VS30, and studying spatial correspondence with the borehole-based VS30 map. Subsequently, the best-fitting regression models were designed by training the geospatial grid in South Korea. Then, DTM and its terrain features were constructed along with VS30 maps for three major cities (Pyongyang, Kaesong, and Nampo) in North Korea. A similar distribution of the VS30 grid obtained using SGS was shown in the multilayer perceptron-based VS30 map.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow

et al. 2021

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“…Figure 14 shows the σln at each class and the total data points. In the case of Dbedrock, the reduction in σln is high for high class numbers (9)(10)(11)(12)(13)(14)(15)(16), where the maximum…”

Section: Dbedrock and Vssoil Prediction Performancementioning

confidence: 99%

“…Table 1 lists dominant landforms and lithologic units per terrain class in Japan [14]. Low-number classes (1-8) are related to mountains and volcanic landforms, while high-number classes (9)(10)(11)(12)(13)(14)(15)(16) are related to soil deposits. These terrain classes effectively represent the geomorphic and geologic features of the surface, but as noted in IP07, the classification's effectiveness might vary depending on regional differences, resolution, and vertical intervals in the DEMs used.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Vilanova et al [15] developed a V S30 model for Portugal that incorporated terrain classifications to predict seismic site behaviors. Similarly, other studies have utilized DEM-based terrain classifications to map seismic risks and site conditions in diverse regions like Iran [16], Indonesia [17], North Korea [18], and California [19]. Moreover, Furze et al [20] demonstrated the use of terrain attributes in mapping D bedrock with a random forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Terrain Classification Methods for the Determination of Bedrock Depth and the Average Shear Wave Velocity of Soil

Choi,

Kwak

2024

Remote Sensing

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The advancement of remote sensing has enabled the creation of high-resolution Digital Elevation Models (DEMs). Topographic features such as slope gradient (SG), local convexity (LC), and surface texture (ST), derived from DEMs, are related to subsurface geological conditions. In South Korea, bedrock depth (Dbedrock) and the average shear wave velocity of soil (VSsoil) serve as metrics for determining the site class, which represents the degree of site amplification in seismic design criteria. These metrics, typically measured through geotechnical and geophysical investigations, require predictive methods for preliminary estimation over large areas. Previous studies developed an automatic terrain classification (AC) scheme using SG, LC, and ST, and subsequent research revealed that terrain classification effectively represents subsurface conditions such as Dbedrcok and average shear wave velocity down to 30 m depth. However, AC intrinsically depends on the regional features of DEMs, dividing regions based on nested means of topographic features (SG, LC, and ST). In this study, we developed two terrain classification methods to determine the thresholds of class divisions, aiming to optimize Dbedrock and VSsoil predictions: Sequentially Optimized Classification (SOC) and Non-Sequentially Optimized Classification (NOC). Through the study of the sensitivity of terrain classification methods, smoothing levels, and threshold levels for terrain class generation, we identified the best classification method by comparing it with the geological and mountainous region distribution. Subsequently, we developed DEM-dependent regression models for each class to enhance the accuracy of predicting Dbedrock and VSsoil. The main findings of this study are: (1) the terrain class map suggested in this study represents the distribution of alluvial plane and mountainous regions well, and (2) the DEM calibration for each class provides increased accuracy of Dbedrock and VSsoil predictions in South Korea. We anticipate that the terrain class map, along with Dbedrock and VSsoil maps, will be effectively utilized in geological interpretations and land-use planning for seismic design.

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A site amplification model for Switzerland based on site-condition indicators and incorporating local response as measured at seismic stations

Bergamo,

Fäh,

Panzera

et al. 2023

Bull Earthquake Eng

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The spatial estimation of the soil response is one of the key ingredients for the modelling of earthquake risk. We present a ground motion amplification model for Switzerland, developed as part of a national-scale earthquake risk model. The amplification model is based on local estimates of soil response derived for about 240 instrumented sites in Switzerland using regional seismicity data by means of empirical spectral modelling techniques. These local measures are then correlated to continuous layers of topographic and geological soil condition indicators (multi-scale topographic slopes, a lithological classification of the soil, a national geological model of bedrock depth) and finally mapped at the national scale resorting to regression kriging as geostatistical interpolation technique. The obtained model includes amplification maps for PGV (peak ground velocity), PSA (pseudo-spectral acceleration) at periods of 1.0, 0.6 and 0.3 s; the modelled amplification represents the linear soil response, relative to a reference rock profile with VS30 (time-averaged shear-wave velocity in the uppermost 30 m of soil column) = 1105 m/s. Each of these amplification maps is accompanied by two layers quantifying its site-to-site and single-site, within event variabilities, respectively (epistemic and aleatory uncertainties). The PGV, PSA(1.0 s) and PSA(0.3 s) maps are additionally translated to macroseismic intensity aggravation layers. The national-scale amplification model is validated by comparing it with empirical measurements of soil response at stations not included in the calibration dataset, with existing city-scale amplification models and with macroseismic intensity observations from historical earthquakes. The model is also included in the Swiss ShakeMap workflow.

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DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

Cited by 16 publications

References 31 publications

Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow

Terrain Proxy-Based Site Classification for Seismic Zonation in North Korea within a Geospatial Data-Driven Workflow

Optimizing Terrain Classification Methods for the Determination of Bedrock Depth and the Average Shear Wave Velocity of Soil

A site amplification model for Switzerland based on site-condition indicators and incorporating local response as measured at seismic stations

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