SEISMICITY of BAIKAL and TRANSBAIKALIA in 2015

We consider the character of the seismic process in the Baikal and Transbaikalia regions in 2015. 36430 earthquakes with KR≥3 were recorded by seismic stations of permanent and temporary networks during the year due to the sharp increase of a number of seismic events at the north-east of the study region in the area of the large Muyakan seismic activation. 53 earthquakes were felt in the cities, towns and local settlements with an intensity not exceeding 6. The largest Tallaysk earthquake (KR=14.0, Mw=5.1) occurred at the North-Muya Ridge and was followed by few aftershocks. Focal mechanisms were determined for 118 seismic events from P-wave first-arrival polarities and based on seismic moment tensors inverted from the surface wave data. It has been found, that normal faults are realized in the sources of 49 % of earthquakes with the obtained focal mechanisms.

A Superior Seismic Data Preprocessing Technique to Improve the Resolution of Surface wave Velocity Spectrums

A high-resolution surface wave velocity spectrum, also known as dispersion image, is of paramount importance for any seismic survey to accurately predict subsurface earth’s properties. The presence of diversified noise in the field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for different wavefield transformation algorithm to produce a detailed and precise velocity spectrum. Standard seismic data preprocessing technique like trace normalisation or bandpass filter fails to address all issues appropriately. Here we have presented a new superior preprocessing technique that can eradicate most of the shortcomings adequately. Experimental field data and published results are used to demonstrate the accuracy of the proposed method. The proposed method also found to produce superior results when compared against the popular commercially available software package Surfseis 6. Overall, the proposed method improves the quality of the velocity spectrum significantly, and it produces a sharper dispersion image even for the extremely noisy data. The work presented here enhances our ability to interpret the surface wave data precisely and help explore accurate properties of the subsurface earth.

A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint inversion of 3D MT and Surface-Wave Data

Joint probabilistic inversions of magnetotelluric (MT) and seismic data has great potential for imaging the thermochemical structure of the lithosphere as well as mapping fluid/melt pathways and regions of mantle metasomatism. In this contribution we present a novel probabilistic (Bayesian) joint inversion scheme for 3D MT and surface-wave dispersion data particularly designed for large-scale lithospheric studies. The approach makes use of a recently developed strategy for fast solutions of the 3D MT forward problem (Manassero et al.,2020) and combines it with adaptive Markov chain Monte Carlo (MCMC) algorithms and parallel-in-parallel strategies to achieve extremely efficient simulations. To demonstrate the feasibility, benefits and performance of our joint inversion method to image the temperature and conductivity structures of the lithosphere, we apply it to two numerical examples of increasing complexity. The inversion approach presented here is timely and will be useful in the joint analysis of MT and surface wave data that are being collected in many parts of the world. This approach also opens up new avenues for the study of translithospheric and transcrustal magmatic systems, the detection of metasomatised mantle and the incorporation of MT into multi-observable inversions for the physical state of the Earth's interior.

Reconstruction, with tunable sparsity levels, of shear-wave velocity profiles from surface wave data

Summary The analysis of surface wave dispersion curves is a way to infer the vertical distribution of shear-wave velocity. The range of applicability is extremely wide: going, for example, from seismological studies to geotechnical characterizations and exploration geophysics. However, the inversion of the dispersion curves is severely ill-posed and only limited efforts have been put in the development of effective regularization strategies. In particular, relatively simple smoothing regularization terms are commonly used, even when this is in contrast with the expected features of the investigated targets. To tackle this problem, stochastic approaches can be utilized, but they are too computationally expensive to be practical, at least, in case of large surveys. Instead, within a deterministic framework, we evaluate the applicability of a regularizer capable of providing reconstructions characterized by tunable levels of sparsity. This adjustable stabilizer is based on the minimum support regularization, applied before on other kinds of geophysical measurements, but never on surface wave data. We demonstrate the effectiveness of this stabilizer on: i) two benchmark—publicly available— datasets at crustal and near-surface scales; ii) an experimental dataset collected on a well-characterized site. In addition, we discuss a possible strategy for the estimation of the depth of investigation. This strategy relies on the integrated sensitivity kernel used for the inversion and calculated for each individual propagation mode. Moreover, we discuss the reliability, and possible caveats, of the direct interpretation of this particular estimation of the depth of investigation, especially in the presence of sharp boundary reconstructions.