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Surface wave methods gained in the past decades a primary role in many seismic projects. Specifically, they are often used to retrieve a 1D shear wave velocity model or to estimate the VS,30 at a site. The complexity of the interpretation process and the variety of possible approaches to surface wave analysis make it very hard to set a fixed standard to assure quality and reliability of the results. The present guidelines provide practical information on the acquisition and analysis of surface wave data by giving some basic principles and specific suggestions related to the most common situations. They are primarily targeted to non-expert users approaching surface wave testing, but can be useful to specialists in the field as a general reference. The guidelines are based on the experience gained within the InterPACIFIC project and on the expertise of the participants in acquisition and analysis of surface wave data.

Southeastern Tibet, which has complex topography and strong tectonic activity, is an important area for studying the subsurface deformation of the Tibetan Plateau. Through the two-station method on 10-year teleseismic Rayleigh wave data from 132 permanent stations in the southeastern Tibetan Plateau, which incorporates ambient noise data, we obtain the interstation phase velocity dispersion data in the period range of 5–150 s. Then, we invert for the shear wave velocity of the crust and upper mantle through the direct 3-D inversion method. We find two low-velocity belts in the mid-lower crust. One belt is mainly in the SongPan-GangZi block and northwestern part of the Chuan-Dian diamond block, whereas the other belt is mainly in the Xiaojiang fault zone and its eastern part, the Yunnan-Guizhou Plateau. The low-velocity belt in the Xiaojiang fault zone is likely caused by plastic deformation or partial melting of felsic rocks due to crustal thickening. Moreover, the significant positive radial anisotropy ( V SH > V SV ) around the Xiaojiang fault zone further enhances the amplitude of low velocity anomaly in our V SV model. This crustal low-velocity zone also extends southward across the Red River fault and farther to northern Vietnam, which may be closely related to heat sources in the upper mantle. The two low-velocity belts are separated by a high-velocity zone near the Anninghe-Zemuhe fault system, which is exactly in the inner and intermediate zones of the Emeishan large igneous province (ELIP). We find an obvious high-velocity body situated in the crust of the inner zone of the ELIP, which may represent maficultramafic material that remained in the crust when the ELIP formed. In the upper mantle, there is a large-scale low-velocity anomaly in the Indochina and South China blocks south of the Red River fault. The low-velocity anomaly gradually extends northward along the Xiaojiang fault zone into the Yangtze Craton as depth increases. Through our velocity model, we think that southeastern Tibet is undergoing three different tectonic modes at the same time: (1) the upper crust is rigid, and as a result, the tectonic mode is mainly rigid block extrusion controlled by large strike-slip faults; (2) the viscoplastic materials in the middle-lower crust, separated by rigid materials related to the ELIP, migrate plastically southward under the control of the regional stress field and fault systems; and (3) the upper mantle south of the Red River fault is mainly controlled by large-scale asthenospheric upwelling and may be closely related to lithospheric delamination and the eastward subduction and retreat of the Indian plate beneath Burma.

Establishing a prediction model of soil liquefaction is an effective way to evaluate the site's quality and prevent the relevant loss caused by the earthquake. Considering the complexity of the liquefaction mechanism and the disadvantage of shear wave not being able to test the type of soil, the standard penetration test (SPT) data and the grey wolf optimization (GWO) algorithm were applied to try to improve the prediction accuracy of the SVM model in this paper. First, the optimal value of C and g of SVM was calculated and selected by iterating the GWO; then, the selected parameters were submitted into the SVM to train the prediction model with the training set; finally, the initial parameter of GWO was judged and updated by testing the test set and evaluating whether the performance of trained model until the goal of accuracy was meet. Besides, the GWO-SVM based on the dataset without the parameter of the shear wave velocity was also trained and tested to prove the advantage of combining the SPT data and shear wave data. It was indicated that the GWO algorithm could not only improve the accuracy of SVM fitting and optimize the performance of the prediction but also can fasten the operation; combining the SPT data and shear wave data was able to improve the prediction accuracy.

Thailand is not known to be an earthquake-prone country; however, in 2014, an unexpected moderate earthquake caused severe damage to infrastructure and resulted in public panic. This event caught public attention and raised awareness of national seismic disaster management. However, the expertise and primary data required for implementation of seismic disaster management are insufficient, including data on soil character which are used in amplification analyses for further ground motion prediction evaluations. Therefore, in this study, soil characterization was performed to understand the seismic responses of soil rigidity. The final output is presented in a seismic microzoning map. A geomorphology map was selected as the base map for the analysis. The geomorphology units were assigned with a time-averaged shear wave velocity of 30 m (VS30), which was collected by the spatial autocorrelation (SPAC) method of microtremor array measurements. The VS30 values were obtained from the phase velocity of the Rayleigh wave corresponding to a 40 m wavelength (C(40)). From the point feature, the VS30 values were transformed into polygonal features based on the geomorphological characteristics. Additionally, the automated geomorphology classification was explored in this study. Then, the seismic microzones were compared with the locations of major damage from the 2014 records for validation. The results from this study include geomorphological classification and seismic microzoning. The results suggest that the geomorphology units obtained from a pixel-based classification can be recommended for use in seismic microzoning. For seismic microzoning, the results show mainly stiff soil and soft rocks in the study area, and these geomorphological units have relatively high amplifications. The results of this study provide a valuable base map for further disaster management analyses.

The investigation of soil response to dynamic loads is necessary to predict site-specific seismic hazard. This paper presents the results of cyclic and dynamic laboratory tests carried out after the 2016–2017 Central Italy Earthquake sequence, within the framework of the seismic microzonation studies of the most damaged municipalities in the area. The database consists of 79 samples investigated by means of dynamic resonant column tests, cyclic torsional shear tests or cyclic direct simple shear tests. Results are firstly analysed in terms of field and laboratory values of small-strain shear wave velocity, highlighting the influence of the sample disturbance and of the mean effective consolidation pressure. The cyclic threshold shear strains as a function of plasticity index are then compared with findings from the published literature and the outliers are analysed. Subsequently, the dynamic soil behaviour is investigated with reference to the small-strain damping ratio. Differences between results from different tests are analysed in the light of the loading frequencies. Finally, the database is used to develop a predictive model for soil nonlinear curves according to plasticity index, mean effective confining stress, and loading frequency. The model represents a useful tool to predict the nonlinear stress–strain behaviour of Central Italy soils, necessary to perform site-specific ground response analyses.

Mineral systems can be thought of as a combination of several critical elements, including the whole‐lithosphere architecture, favorable geodynamic/tectonic events, and fertility. Because they are driven by processes across various scales, exploration benefits from a scale‐integrated approach. There are open questions regarding the source of ore‐forming fluids, the depth of genesis, and their transportation through the upper crust to discrete emplacement locations. In this study, we investigate an Au–Cu metal belt located at the margin of an Archean‐Paleoproterozoic microcontinent. We explore the geophysical signatures by analyzing three‐dimensional models of the electrical resistivity and shear‐wave velocity throughout the lithosphere. Directly beneath the metal belt, narrow, vertical, finger‐like low‐resistivity features are imaged within the resistive upper‐middle crust and are connected to a large low‐resistivity zone in the lower crust. A broad low‐resistivity zone is imaged in the lithospheric mantle, which is well aligned with a zone of low shear‐wave velocity, examined with a correlation analysis. In the upper‐middle crust, the resistivity signatures give evidence for ancient pathways of fluids, constrained by a structure along a tectonic boundary. In the lower lithosphere, the resistivity and velocity signatures are interpreted to represent a fossil fluid source region. We propose that these signatures were caused by a combination of factors related to refertilization and metasomatism of the lithospheric mantle by long‐lived subduction at the craton margin, possibly including iron enrichment, F‐rich phlogopite, and metallic sulfides. The whole‐lithosphere architecture controls the genesis, evolution, and transport of ore‐forming fluids and thus the development of the mineral system. Plain Language Summary The whole‐lithosphere structure of mineral systems, the link between deep and shallow regions, and the nature, origin, and depth of the source fluids that form mineral deposits are open questions. In this study, we investigate a gold and copper metal belt that is located at the margin of an ancient microcontinent and craton with a history of long‐lived subduction. We explore the region by examining three‐dimensional geophysical images of both the electrical resistivity structure and the shear‐wave velocity structure throughout the lithosphere. Narrow, vertical, fingers of low resistivity in the crust give evidence for ancient pathways of fluids beneath the metal belt. Low velocity and low resistivity signatures in the lower lithosphere are interpreted to represent a fossil fluid source region. We suggest that the geophysical signatures observed were caused by a combination of factors related to mantle metasomatism caused by long‐lived subduction and magmatism. The possible causes include iron enrichment in a more fertile mantle, the presence of F‐rich phlogopite in the lithospheric mantle, and metallic sulfides in the lower lithosphere, including at the base of the crust. The whole‐lithosphere structure and favorable geodynamic/tectonic events control the evolution of ore‐forming fluids that create metal/mineral deposits. Key Points Vertical, finger‐like low‐resistivity zones in the upper crust beneath an Au‐Cu metal belt give evidence for ancient pathways of fluids Low velocity and low resistivity in the lower lithosphere are interpreted to represent a fossil source region for ore‐forming fluids Signatures may be due to a combination of factors: Metallic sulfides, phlogopite, and iron enrichment by refertilization and metasomatism

Seismic bedrock depth represents one of the crucial steps in site response analysis as the input seismic ground motion is propagated from the bedrock level through soil profile column to estimate surface ground motion. In the last two decades, several studies estimated bedrock depth using microtremor horizontal-to-vertical-spectral ratio fundamental (resonance) frequency and known (mostly boreholes) bedrock depth in different regions through power-law regression. In this study, seismic bedrock (H800—depth of the bedrock formation identified by shear wave velocity VS ≥ 800 m/s) is estimated using H/V forward modelling routine in which shear wave velocity profile from geophysical measurements was used as an initial soil model. Based on geological, geophysical and microtremor data, empirical relationships between average shear wave velocity in top 30 m, resonance frequency and bedrock depth are derived. Given evaluation of sedimentary thicknesses showed to be within the range of published studies for similar geological and site characteristics if the errors between different methods, approaches and site parameters are considered. The main limitation to validate proposed relationships is the lack of realistic deep borehole data like in similar studies. Further work is required that should include deep data from borehole drilling, seismic refraction and reflection or vertical electrical sounding. The intention is that presented regressions between resonance frequency, bedrock depth and VS30 would be implemented into nonlinear site amplification model and further development towards Ground Motion Prediction Equations for Croatia.

We present the seismic site characterization study using joint modelling of Horizontal-to-Vertical Spectral-Ratio (HVSR) and Rayleigh wave-phase velocity-dispersion curves obtained from Multi-channel Simulation with One Receiver (MSOR) in a part of Dhanbad, Jharkhand, India. The joint analysis of these two different but complementary datasets puts stronger constraints on the model parameter search space than one dataset and may help us in finding more unique shear-wave velocity model. The microtremor data from 12 observation points were utilized to iteratively search 1D shear-wave velocity profiles in a predefined model search space. These 1D shear-wave velocity models were interpolated to generate a 2D shear-wave velocity profile of the site using the cubic spline method. Our results show that the high peak amplitude value of HVSR is associated with low peak-period values of HVSR at a distance of ~ 60 m from the southern end of the profile; which may indicate the presence of the Basin Edge Effect. We identified four layers based on significant changes in the shear wave velocities to a depth of ~ 60 m. The major impedance contrasts are located at average depths of ~ 13 m, ~ 40 m and ~ 55 m, respectively. These layers from the surface may indicate the presence of soil, highly weathered rock mass, moderately weathered rock and bedrock, respectively. The depth of engineering solid bedrock (Vs > 600 m/s) is found at the depth of 55 m in the south which gradually decreases to a depth of 40 m in the northern end of the profile. The shear-wave velocity (Vs 30) for this area varies between 293 and 357 m/s; which can be classified as “D-type site”. For validation and comparison of our results, the Electrical Resistivity Tomography (ERT) data were also recorded along the same traverse using Wenner and Schlumberger configurations. Our results show a significant amount of correlation between the 2D shear-wave velocity and resistivity profiles obtained from joint analysis of tremor and ERT data.

The ground motion intensity of an earthquake is significantly changed when seismic waves propagate from the bedrock to the near-surface soft geological materials. The ground where the shear wave velocity (Vs) exists greater than 760 m/s is generally considered as bedrock. As a common practice in the last three decades, the surface ground motion of a soil site is estimated by multiplying the bedrock motion with the site coefficient that is empirically determined from the time-averaged shear wave velocity in the top 30 m (Vs30) of the site. The site coefficient is defined as the ratio of the ground motion intensity at the ground surface to that of the bedrock. If the bedrock of a site exists at a depth of greater than 30 m, the site effect from the depth of 30 m to the bedrock is not accounted in the Vs30-based site coefficient. In Dhaka City, the minimum depth of the bedrock is approximately 175 m. Therefore, the use of the Vs30-based site coefficient to estimate the surface ground motion is not appropriate for the soft and deep sedimentary deposits of this city. In this study, site response analysis using the Vs30-based site coefficient, linear, equivalent-linear, and nonlinear approaches has been performed to estimate the surface ground motion at different sites of Dhaka City and to compare the results of different approaches. It is observed that the surface ground motion is decreased with increasing the depth of the bedrock due to low shear strain and viscous damping in the soft sedimentary deposits.

Shear wave velocity (Vs) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating Vs without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the Vs. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of Vs, and it has led to noticeable increase in the accuracy of model calculations.