
 The spectral ratio between horizontal and vertical components (H/V ratio) of microtremors measured at the ground surface has been used to estimate fundamental periods and amplification factors of a site, although this technique lacks theoretical background. The aim of this article is to formulate the H/V technique in terms of the characteristics of Rayleigh and Love waves, and to contribute to improve the technique. The improvement includes use of not only peaks but also troughs in the H/V ratio for reliable estimation of the period and use of a newly proposed smoothing function for better estimation of the amplification factor. The formulation leads to a simple formula for the amplification factor expressed with the H/V ratio. With microtremor data measured at 546 junior high schools in 23 wards of Tokyo, the improved technique is applied to mapping site periods and amplification factors in the area.

Ground Motion Characteristics Estimated from Spectral Ratio between Horizontal and Verticcl Components of Mietremors.

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.

/pdf/guidelines-for-the-good-practice-of-surface-wave-analysis-a-1p7bc6o3xk.pdf

Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project

Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand an...

/pdf/shear-wave-velocity-based-probabilistic-and-deterministic-3f4srp88ew.pdf

Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

Radar interferometry

Hydraulic Fracturing-Induced Seismicity

Passive seismic source location imaging is important to various scientific and engineering research topics spanning from unconventional reservoir development in exploration seismology to seismic hazard prevention in the earthquake seismology community. The emerging machine-learning (ML) techniques enable the location of passive seismic sources with unprecedented efficiency and accuracy. Most of the state-of-the-art ML methods are based on waveforms, as required by the most popular convolutional neural network (CNN) architecture, which is prone to the sensitivity of velocity models. Here, we present a traveltime-based ML method, RFloc3D, to locate passive seismic sources from manually or automatically picked P- and S-wave arrivals. The proposed method is similar to traditional traveltime-based location methods, where the inverse mapping from arrival times to the passive source location is obtained by inverting a nonlinear inverse problem, but differs in leveraging the random forest (RF) method to learn the inverse mapping relation from numerous eikonal-based forward simulations. Details and analyses of the proposed RFloc3D method are illustrated based on a microseismic monitoring setup. Numerical and real data examples show that the proposed method is capable of real-time location. The inclusion of S-wave arrivals, most importantly, the differential time between P- and S-wave arrivals, helps significantly to reduce the depth error (e.g., decreasing the mean absolute error (MAE) to a half) of the located sources.

RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals

\n We have analyzed shear-wave splitting (SWS) data from local earthquakes in the Permian basin in west Texas to understand crustal stress change and induced seismicity. Two SWS parameters, the fast polarization direction and the delay time, are computed using a semiautomatic algorithm. Most measurements are determined in the Delaware basin and the Snyder area. In both regions, SWS fast directions are mostly consistent with local SHmax at stations that are relatively far from the earthquake clusters. Varying fast directions at one station are related to different ray paths and are probably caused by heterogeneity. In the Snyder area, most northeast–southwest fast directions are from the events in the northern part of the cluster, whereas the northwest–southeast fast directions are mostly from the southern part. The northeast–southwest and northwest–southeast fast directions could be attributed to the northeast-trending normal faults and the northwest-trending strike-slip faults, respectively. SWS results in the Delaware basin have two unique features. First, most shallow earthquakes less than 4 km deep produce relatively large delay times. This observation implies that the upper crust of the Delaware basin is highly fractured, as indicated by the increasing number of induced earthquakes. Second, diverse fast directions are observed at the stations in the high-seismicity region, likely caused by the presence of multiple sets of cracks with different orientations. This situation is possible in the crust with high pore pressure, which is expected in the Delaware basin due to extensive wastewater injection and hydraulic fracturing. We propose that the diversity of SWS fast directions could be a typical phenomenon in regions with a high rate of induced seismicity.

Complex Shear‐Wave Anisotropy from Induced Earthquakes in West Texas


 New sensing techniques like the nodal geophones and distributed acoustic sensing enable a spatial sampling ratio that was unprecedentedly high in earthquake seismology. The much higher sampling of seismic wavefields that is close to the level in exploration seismology calls for a unified processing approach for multichannel seismic data regardless of the research interest, for example, oil and gas oriented or earthquake-study oriented. Here, we present the first Python package for multichannel seismic data that benefits both communities, that is, exploration and earthquake seismology, called Pyseistr. The Pyseistr is a Python package that is designed to make full use of the structural patterns in multichannel seismic data to facilitate the data processing. The Pyseistr package currently includes several fundamental functions like slope estimation, structural mean and median filtering, and structural reconstruction of missing data. The Pyseistr package is continuously developed to include more functions that benefit both exploration and earthquake communities.

Pyseistr: A Python Package for Structural Denoising and Interpolation of Multichannel Seismic Data

Crustal Structure in Southeastern Texas from Joint Inversion of Ambient Seismic Noise and P to S Receiver Functions.

Erratum: “The 8 June 2008 Mw 6.5 Achaia–Elia, Greece, Earthquake: Source Characteristics, Ground Motions, and Ground Failures” [Earthquake Spectra 26, 399–424 (2010)]

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