In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure computational efficiency. The viscoelastic nature of the subsurface is often unaccounted for, and two popular approximations-the acoustic and linearized Born inversion-are widely used. This leads to reconstruction errors since the approximations ignore realistic (physical) aspects of seismic wave propagation in the heterogeneous Earth. In this study, we show that the Bayesian approximation error approach can be used to partially recover from errors, addressing elastic and viscous effects in acoustic Born inversion for viscoelastic media. The results of numerical examples indicate that neglecting the modeling errors induced by the approximations results in very poor recovery of the subsurface velocity fields.

This work assesses the feasibility of the direct use of surface-wave dispersion curves from seismic ambient noise to gain insight into the crustal structure of Bransfield Strait and detect seasonal seismic velocity changes. We cross-correlated four years of vertical component ambient noise data recorded by a seismic array in West Antarctica. To estimate fundamental mode Rayleigh wave Green's functions, the correlations are computed in 4-hr segments, stacked over 1-year time windows and moving windows of 3 months. Rayleigh wave group dispersion curves are then measured on two spectral bands-primary (10-30 s) and secondary (5-10 s) microseisms-using frequency-time analysis. We analyze the temporal evolution of seismic velocity by comparing dispersion curves for the successive annual and 3-month correlation stacks. Our main assumption was that the Green's functions from the cross-correlations, and thus the dispersion curves, remain invariant if the crustal structure remains unchanged. Maximum amplitudes of secondary microseisms were observed during local winter when the Southern Ocean experiences winter storms. The Rayleigh wave group velocity ranges between 2.1 and 3.7 km/s, considering our period range studied. Inter-annual velocity variations are not much evident. We observe a slight velocity decrease in summer and increase in winter, which could be attributed to the pressure melting of ice and an increase in ice mass, respectively. The velocity anomalies observed within the crust and upper mantle structure correlate with the major crustal and upper mantle features known from previous studies in the area. Our results demonstrate that the direct comparison of surface wave dispersion curves extracted from ambient noise might be a useful tool in monitoring crustal structure variations.In this study, we used vertical component continuous seismic data from 4 stations in the Argentine Antarctica region from ASAIN (Antarctic Seismographic Argentine-Italian Network) in West Antarctica (Fig. 1). The stations Carlini-formerly Jubany (JUBA)-station, Esperanza (ESPZ), Orcada (ORCD) and San Martin (SMAI) are part of the 5 seismological stations that have been set under ASAIN collaborative agreement.The seismologic network provide essential data to study the internal structure of the Antarctic continent, monitor seismicity, and to monitor large-scale phenomena, such as melting of the Antarctic ice sheet, among others. We analyzed 4 years (2008-2011) of seismic data recorded recorded by the 4 stations with a sampling rate of 1 sample per second. The initial data preparation involves unpacking the raw data from its standard SEED format to obtain noise data in SAC format. We have to ensure that the two amplitudes of each record are completely consistent by running a time normalization so that the records share the same absolute time and the same amplitude information is retained. It is important to note that if the instruments of two stations are inconsistent, it is necessary to remove instrument responses from the raw data.Here ...

<p>One of the challenges for students of geosciences is learning to read geological maps, interpret structural geology, and understand the link between geology and geophysical properties. Augmented Reality (AR) sandboxes are interactive visualization tools that are becoming increasingly popular to demonstrate various earth processes. </p><p>An AR sandbox consists of a box filled with white sand and uses a Kinect 3D camera to continuously scan the topography of the sand surface. The topographic view of the structures sculpted by the user is then blended with digital information and a computed image is projected back onto the sand surface. Due to their intuitive operation, AR Sandboxes serve as a powerful science outreach and communication tool by making abstract concepts easy to see through the leveraging of playful learning and visualization, offering huge potential for teaching geological and geophysical principles.</p><p>Several versions of AR Sandboxes have been developed for a whole range of scenarios, spanning a wide variety of Earth Science topics and learning environments. The most common scenarios are from physical geography, hydrology and ecology. Their underlying data models stay at or close to the surface, making it hard to incorporate geological models. </p><p>Recently, an Open-AR-Sandbox software was published by researchers at the Institute for Computational Geoscience and Reservoir Engineering (CGRE), RWTH Aachen University, Germany. With this AR Sandbox, geological models can be projected onto real sand and the relations of subsurface structures, topography and outcrop can be explored in an AR environment. </p><p>We tested the Open-AR-Sandbox software after successfully installing and running a conventional AR sandbox software. The combination of the Sandbox and GemPy geomodelling tool offers unique 3D interactive modelling solutions to explore geoscientific data and processes, with linkages to other software tools. We can use the AR sandbox to project a variety of geophysical measurement data onto the sand surface, offering an interactive experience that integrates geological and geophysical data. The Open-AR-Sandbox is, therefore, an innovative tool in geoscience education for the public as well as the classroom because of its benefits for teaching geological mapping, structural geology and geophysics.</p>

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