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In geophysical inference problems, quantification of data uncertainties is required to balance the data-fitting ability of the model and its complexity. The transdimensional hierarchical Bayesian approach is a powerful tool to evaluate the level of uncertainty and determine the complexity of the model by treating data errors and model dimensions as unknown. In this article, we take account of the uncertainty through the whole procedure, thus developing a two-step fully Bayesian approach with coupled uncertainty propagation to estimate the crustal isotropic and radial anisotropy (RA) model based on Rayleigh and Love dispersion as well as receiver functions (RFs). First, 2D surface-wave tomography is applied to determine period-wise ambient noise phase velocity maps and their uncertainty for Rayleigh and Love waves. Probabilistic profiles of the isotropic average VS and RA as a function of depth are then derived at station sites by inverting the local surface-wave dispersion and model errors and RFs jointly. The workflow is applied to a temporary seismic broadband array covering all of Sri Lanka. The probabilistic results enable us to effectively quantify the uncertainty of the final RA model and provide robust inferences. The shear-wave velocity results show that the range of Moho depths is between 30 and 40 km, with the thickest crust (38-40 km) beneath the central Highland Complex. Positive RA (VSH > VSV) observed in the upper crust is attributed to subhorizontal alignment of metamorphic foliation and stretched layers resulting from deformation. Negative RA (VSH > VSV) in the midcrust of central Sri Lanka may indicate the existence of melt inclusions and could result from the uplift and folding process. The positive RA in the lower crust could be caused by crustal channel flow in a collision orogeny. [ABSTRACT FROM AUTHOR]

Low sea levels during the last Ice Age exposed millions of square kilometers of Arctic shelves which have been subsequently submerged, creating subsea permafrost. In onshore settings, permafrost can also exist beneath water bodies such as coastal lagoons, rivers, and thermokarst lakes. We explored passive seismology as a method for mapping unfrozen sediment thickness above subsea and sub‐aquatic permafrost. We present passive seismic data collected with the Mobile Ocean Bottom Seismic Instrument (MOBSI) from the Beaufort Sea near Tuktoyaktuk in Canada, Ivashkina Lagoon on the Bykovsky Peninsula, as well as a lake and river in the Lena Delta, Siberia, Russia. We use borehole data and frost probe measurements to identify permafrost‐related H/V measurement peaks and calibrate shear wave velocities for frequency‐to‐depth conversion. We employ the shortest path and maximum signal amplitude to connect peaks and generate geological profiles. The MOBSI detected the ice‐bonded permafrost table beneath the Beaufort Sea, as well as beneath a Siberian lake and lagoon. At Tuktoyaktuk, an ocean bottom seismometer revealed a 5% scatter about the peak frequency for three‐minute time windows and over 8 hr of recording time. With peak frequencies ranging from 4.9 ± 0.2 Hz to 27.6 ± 1.4 Hz, the depth to subsea permafrost ranged from 1.4 ± 0.1 m bsl at the shoreline to 14.0 ± 0.4 m bsl 240 m offshore. Given an accurate shear wave velocity, our findings highlight that MOBSI deployment times as short as 3 min are adequate for detecting Arctic subsea and sub‐aquatic permafrost. Plain Language Summary: During the Ice Age, low sea levels exposed large areas of the Arctic, creating perennially frozen ground known as permafrost. When sea levels rose, this permafrost became submerged. Permafrost can also exist under bodies of water like coastal lagoons, rivers, and lakes. However, we have limited direct measurements of its characteristics and how it is changing over time. To address this, we used a technique called passive seismology to map the thickness of unfrozen sediment above subsea and sub‐aquatic permafrost. We collected passive seismic data using the Mobile Ocean Bottom Seismic Instrument (MOBSI) in the Beaufort Sea near Tuktoyaktuk in Canada, Ivashkina Lagoon on the Bykovsky Peninsula, and a lake and river in the Lena Delta, Siberia, Russia. This method involves recording ambient seismic noise to identify differences in the speed of shear waves in the subsurface. These boundaries with contrasting velocities are associated with geological interfaces like the top of permafrost. We used data from boreholes and frost probe measurements to calibrate and verify our passive seismic results. Key Points: We present passive seismic data from Arctic lake, river, lagoon, and subsea permafrost settingsWe applied the maximum H/V amplitude and shortest path to connect H/V peaks and interpret subsurface shear wave velocity contrastsPassive seismic H/V ratio measurements of just a few minutes of recording time can be used to investigate the depth of submerged permafrost [ABSTRACT FROM AUTHOR]

Seismic methods are emerging as efficient tools for imaging the subsurface to investigate the weathering zone. The structure of the weathering zone can be identified by differing shear wave velocities as various weathering processes will alter the properties of rocks. Currently, 3D subsurface modelling of the weathering zone is gaining increasing importance as results allow the identification of the weathering imprint in the subsurface not only from top to bottom but also in three dimensions. We investigated the 3D weathering structure of monzogranite bedrock near the Pan de Azúcar National Park (Atacama Desert, northern Chile), where the weathering is weak due to the arid climate conditions. We set up an array measurement that records seismic ambient noise, which we used to extract the horizontal-to-vertical spectral ratio (HVSR) curves. The curves were then used to invert for 1D shear wave velocity (Vs) models, which we then used to compile a pseudo-3D model of the subsurface structure in our study area. To invert the 1D Vs model, we applied a transdimensional hierarchical Bayesian inversion scheme, allowing us to invert the HVSR curve with minimal prior information. The resulting 3D model allowed us to image the granite gradient from the surface down to ca. 50 m depth and confirmed the presence of dikes of mafic composition intruding the granite. We identified three main zones of fractured granite, altered granite, and the granite bedrock in addition to the mafic dikes with relatively higher Vs. The fractured granite layer was identified with Vs of 1.4 km s -1 at 30–40 m depth, while the granite bedrock was delineated with Vs of 2.5 km s -1 and a depth range between 10 and 50 m depth. We compared the resulting subsurface structure to other sites in the Chilean coastal cordillera located in various climatic conditions and found that the weathering depth and structure at a given location depend on a complex interaction between surface processes such as precipitation rate, tectonic uplift and fracturing, and erosion. Moreover, these local geological features such as the intrusion of mafic dikes can create significant spatial variations to the weathering structure and therefore emphasize the importance of 3D imaging of the weathering structure. The imaged structure of the subsurface in Pan de Azúcar provides the unique opportunity to image the heterogeneities of a rock preconditioned for weathering but one that has never experienced extensive weathering given the absence of precipitation. [ABSTRACT FROM AUTHOR]

Seismic methods are emerging as efficient tools for imaging the subsurface to investigate the weathering zone. The structure of the weathering zone can be identified by differing shear wave velocities as various weathering processes will alter the properties of rocks. Currently, 3D subsurface modelling of the weathering zone is gaining increasing importance as their results allow the identification of the weathering imprint in the subsurface not only from top to bottom but also in three dimensions. We investigated the 3D weathering structure of monzogranite bedrock near the Pan de Azúcar National Park (Atacama Desert, Northern Chile), where the weathering is weak due to the arid climate condition. We set up an array measurement that records seismic ambient noise, which we used to extract the horizontal-to-vertical spectral ratio (HVSR) curves. The curves were then used to invert for 1D shear wave velocity models, which we then used to compile a pseudo-3D model of the subsurface structure in our study area. To invert the 1D shear wave velocity model, we apply a trans-dimensional hierarchical Bayesian inversion scheme, allowing us to invert the HVSR curve with minimum prior information. The resulting 3D model allowed us to image the granite gradient from the surface down to ca. 50 meters depth and confirmed the presence of dikes of mafic composition intruding the granite. We identified three main zones of fractured granite, altered granite, and the granite bedrock in addition to the mafic dikes with relatively higher Vs. The fractured granite layer was identified with Vs of 1.4 km/s at 30 – 40 meter depth, while the granite bedrock was delineated with Vs of 2.5 km/s and a depth range between 10 and 50 meters depth. We compared the resulting subsurface structure to other sites in the Chilean coastal cordillera located in various climatic conditions and found that the weathering depth and structure at a given location depends on a complex interaction between surface processes such as precipitation rate, tectonic uplift and fracturing, and erosion. Moreover, these local geological features such as intrusion of mafic dikes can create significant spatial variations to the weathering structure and therefore emphasize the importance of 3D imaging of the weathering structure. The imaged structure of the subsurface in Pan de Azúcar provides the unique opportunity to image the heterogeneities of a rock preconditioned for weathering, but one that has never experienced extensive weathering given the absence of precipitation. [ABSTRACT FROM AUTHOR]

Author(s): Georg Rümpker (corresponding author) [1]; Trond Ryberg [1]; Günter Bock [1]; Desert Seismology Group [1] Lithospheric-scale transform faults play an important role in the dynamics of global plate motion. [...]

We have constructed a composite image of the fault systems of the M 6.7 San Fernando (1971) and Northridge (1994), California, earthquakes, using industry reflection and oil test well data in the upper few kilometers of the crust, relocated aftershocks in the seismogenic crust, and LARSE II (Los Angeles Region Seismic Experiment, Phase II) reflection data in the middle and lower crust, In this image, the San Fernando fault system appears to consist of a decollement that extends 50 km northward at a dip of ~25[degrees] from near the surface at the Northridge Hills fault, in the northern San Fernando Valley, to the San Andreas fault in the middle to lower crust. It follows a prominent aseismic reflective zone below and northward of the main-shock hypocenter. Interpreted upward splays off this decollement include the Mission Hills and San Gabriel faults and the two main rupture planes of the San Fernando earthquake, which appear to divide the hanging wall into shingleor wedge-like blocks. In contrast, the fault system for the Northridge earthquake appears simple, at least east of the LARSE II transect, consisting of a fault that extends 20 km southward at a dip of ~33[degrees] from ~7 km depth beneath the Santa Susana Mountains, where it abuts the interpreted San Fernando decollement, to ~20 km depth beneath the Santa Monica Mountains. It follows a weak aseismic reflective zone below and southward of the mainshock hypocenter. The middle crustal reflective zone along the interpreted San Fernando decollement appears similar to a reflective zone imaged beneath the San Gabriel Mountains along the LARSE I transect, to the east, in that it appears to connect major reverse or thrust faults in the Los Angeles region to the San Andreas fault. However, it differs in having a moderate versus a gentle dip and in containing no mid-crustal bright reflections. Keywords: crustal structure, tectonics, earthquakes, seismic imaging, southern California.

Ambient seismic noise tomography is a novel, low-impact method to investigate the Earth's structure. While most passive seismic studies focus on structures at crustal scales, there are only few examples of this technique being applied in a mineral exploration context. In this study, we performed an ambient seismic experiment to ascertain the relationship between the shallow shear wave velocity and mineralized zones in the Erzgebirge in Germany, one of the most important metal provinces in Europe. Late Variscan mineralized greisen and veins occurring in the Geyer-Ehrenfriedersdorf mining district of the Central Erzgebirge were mined from medieval times until the end of the 19th century. These occurrences represent a significant resource for commodities of high economic importance, such as tin, tungsten, zinc, indium, bismuth and lithium. Based on ambient noise data from a dense "LARGE-N" network comprising 400 low-power, short-period seismic stations, we applied an innovative tomographic inversion technique based on Bayesian statistics (transdimensional, hierarchical Monte Carlo search with Markov Chains using a Metropolis/Hastings sampler) to derive a three-dimensional shear wave velocity model. An auxiliary 3D airborne time-domain electromagnetic dataset is used to provide additional insight into the subsurface architecture of the area. The velocity model shows distinct anomalies down to approximately 500 m depth that correspond to known geological features of the study area, such as (a) gneiss intercalations in the mica schist-dominated host rock, imaged by a SW-NE striking low velocity zone with a moderately steep northerly dip, and (b) a NW-trending strike-slip fault, imaged as a subvertical linear zone cross-cutting and offsetting this low velocity domain. Similar to the velocity data, the electromagnetic data exhibit north-dipping (high-conductivity) structures in the mica schists, corresponding to the strike and dip of the predominant metamorphic fabric. An unsupervised classification performed on the bivariate 3D dataset yielded nine spatially coherent classes, one of which shows a high correspondence to drilled greisen occurrences in the roof zone of a granite pluton. The relatively high mean shear velocity and resistivity values of this class could be explained by changes in density and composition during greisen formation as observed in other areas of the Erzgebirge. Our study demonstrates the great potential of the cost-efficient and low-impact ambient noise technology for mineral exploration, especially when combined with other independent geophysical datasets. [ABSTRACT FROM AUTHOR]

In this study, we analyzed a large seismological dataset from temporary and permanent networks in the southern and eastern Alps to establish high-precision hypocenters and 1-D VP and VP/VS models. The waveform data of a subset of local earthquakes with magnitudes in the range of 1–4.2 ML were recorded by the dense, temporary SWATH-D network and selected stations of the AlpArray network between September 2017 and the end of 2018. The first arrival times of P and S waves of earthquakes are determined by a semi-automatic procedure. We applied a Markov chain Monte Carlo inversion method to simultaneously calculate robust hypocenters, a 1-D velocity model, and station corrections without prior assumptions, such as initial velocity models or earthquake locations. A further advantage of this method is the derivation of the model parameter uncertainties and noise levels of the data. The precision estimates of the localization procedure is checked by inverting a synthetic travel time dataset from a complex 3-D velocity model and by using the real stations and earthquakes geometry. The location accuracy is further investigated by a quarry blast test. The average uncertainties of the locations of the earthquakes are below 500 m in their epicenter and ∼ 1.7 km in depth. The earthquake distribution reveals seismicity in the upper crust (0–20 km), which is characterized by pronounced clusters along the Alpine frontal thrust, e.g., the Friuli-Venetia (FV) region, the Giudicarie–Lessini (GL) and Schio-Vicenza domains, the Austroalpine nappes, and the Inntal area. Some seismicity also occurs along the Periadriatic Fault. The general pattern of seismicity reflects head-on convergence of the Adriatic indenter with the Alpine orogenic crust. The seismicity in the FV and GL regions is deeper than the modeled frontal thrusts, which we interpret as indication for southward propagation of the southern Alpine deformation front (blind thrusts). [ABSTRACT FROM AUTHOR]