Seispy is a graphical interface Python module for receiver function (RF) calculation and postprocessing in seismological research. Automated workflows of RF calculations facilitate processing large volume of different types of seismic data. The graphical user interface enables an intuitive and straightforward evaluation of RF quality. All parameters about the preprocessing for RF estimation can be adjusted based on user preference. Water-level frequency-domain deconvolution and iterative time-domain deconvolution for RF estimation are available in Seispy. The current version of Seispy contains five main modules for the postprocessing of RF, such as H-κ stacking, crustal anisotropic estimation, harmonic decomposition, and 2D and 3D common conversion point (CCP) stacking. The CCP stacking in the different application scenarios can be handled by a rich collection of modules, such as time-to-depth conversion, 2D or 3D CCP stacking, and adaptive station or bin selection for CCP stacking profiles in a dense seismic array or a linear seismic array. As a Python module, functions in the Seispy can be called easily in Python scripts for other purposes. The modular design allows new functionality to be added in a collaborative development environment. Licensed under GPLv3, Seispy allow users and developers to freely use, change, share, and distribute copies of the package.

Eastern Asia is a prime location for the study of intracontinental tectono‐magmatic activity. For instance, the origin of widespread intraplate volcanism has been one of the most debated aspects of East Asian geological activity. Measurements of attenuation of teleseismic phases may provide additional constraints on the source regions of volcanism by sampling the upper mantle. This study uses data from three seismic arrays to constrain lateral variations in teleseismic P wave attenuation beneath the Central Asian Orogenic Belt and the North China Craton. We invert relative observations of attenuation for a 2‐D map of variations in attenuation along with data and model uncertainties by applying a Hierarchical Bayesian method. As expected, low attenuation is observed beneath the Ordos block. High attenuation is observed beneath most of the volcanoes (e.g., the Middle Gobi volcano, the Bus Obo volcano, and the Datong volcano) in the study area, and estimated asthenospheric Qp values span from 95 to 200. These values are within the range of globally average asthenosphere. We infer that these volcanoes may tap melt from ambient asthenosphere and occur where the lithosphere is thin, which is consistent with previous petrologic studies. More complex mantle drivers of volcanism are not rejected but are not needed to explain eruptions in this area. In contrast, at the Xilinhot‐Abaga volcanic site, the observed low attenuation (as low as beneath the Ordos block) excludes a typical shallow melting column. Fluids from the subducted Pacific plate may initiate the deep melting and would be consistent with petrological constraints.

Mongolia Plateau, located at the center of Central Asian Orogenic Belt (CAOB) and close to the frontier of the subducting Pacific slab, is widely distributed with the young active intracontinental volcanism (Figure 1). There is a controversial debate about the mechanism or origin of the widely dispersed, long-term intraplate Cenozoic magmatism. Several upper mantle low-velocity or low-density zones (M.

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When an earthquake occurs, railway bridges will suffer from different degrees of seismic damage, and it is necessary to assess the seismic risk of bridges. Unfortunately, the majority of studies were done on highway bridges without taking into account railway bridge characteristics; hence they are not applicable to railway bridges. Furthermore, current research methods for risk assessment cannot be performed quickly, and suffer from the problems of subjective personal experience, complicated calculations, and time-consuming. This paper we use machine learning for earthquake damage prediction and empirical vulnerability curves to represent risk assessment results, creating a rapid risk assessment procedure. We gathered and tallied seismic damage data from 335 railway bridges that were damaged in the Tangshan and Menyuan earthquakes, found six variables that had a substantial impact on seismic risk outcomes, and categorized the damage levels into five categories. It is essentially a multi-classification and prediction problem. In order to solve this problem, four algorithms were tested: Random Forest (RF) Back Propagation Artiifcial Neural Network (BP-ANN), PSO-Support Vector Machine (PSO-SVM), and K Nearest Neighbor (KNN). It was found that RF is the most effective method, with an accuracy rate of up to 93.31% for the training set and 89.39% for the test set. Then this study describes the new procedure in detail for rapidly assessing seismic risk to 269 bridges chosen at random from the sample pool. Firstly, the seismic damage data of bridges are collated, then the seismic damage rating is predicted using RF, and finally the empirical vulnerability curve is drawn using a two-parameter normal distribution function for the purpose of seismic damage risk assessment. The study’s findings can be used as a guide for choosing a machine learning approach and its inputs to build a rapid assessment model for railway bridges.