Abstract. We use PoroTomo experimental data to compare the performance of distributed acoustic sensing (DAS) and geophone observations in retrieving data to execute standard subsurface mapping and seismic monitoring activities. The PoroTomo experiment consists of two âseismic systemsâ: (a) a 8.6âkm long optical fibre cable deployed across the Brady geothermal field and covering an area of 1.5âÃâ0.5âkm with 100âm long segments and (b) a co-located array of 238 geophones with an average spacing of 60âm. The PoroTomo experiment recorded continuous seismic data between 10 and 25 March 2016. During this period, a Mlâ4.3 regional event occurred in the southeast, about 150âkm away from the geothermal field, together with several microseismic local events related to the geothermal activity. The seismic waves generated from such seismic events have been used as input data in this study to tackle similarities and differences between DAS and geophone recordings of such wavefronts. To assess the quality of data for subsurface mapping tasks, we measure the propagation of the P wave generated by the regional event across the geothermal field in both seismic systems in term of relative time delays, for a number of configurations and segments. Additionally, we analyse and compare the amplitude and the signal-to-noise ratio (SNR) of the P wave in the two systems at high resolution. For testing the potential of DAS data in seismic event locations, we first perform an analysis of the geophone data to retrieve a reference location of a microseismic event, based on expert opinion. Then, we a adopt different workflow for the automatic location of the same microseismic event using DAS data. To assess the quality of the data for tasks related to monitoring distant events, we retrieve both the propagation direction and apparent velocity of the wave field generated by the Mlâ4.3 regional event, using a standard plane-wave-fitting approach applied to DAS data. Our results indicate that (1) at a local scale, the seismic P-wave propagation (i.e. time delays) and their characteristics (i.e. SNR and amplitude) along a single cable segment are robustly consistent with recordings from co-located geophones (delay times δtâ¼0.3 over 400âm for both seismic systems); (2) the DAS and nodal arrays are in mutual agreement when it comes to site amplifications, but it is not immediately clear which geological features are responsible for these amplifications. DAS could therefore hold potential for detailed mapping of shallow subsurface heterogeneities, but with the currently available information of the Brady Hot Springs subsurface geology, this potential cannot be quantitatively verified; (3) the interpretation of seismic wave propagation across multiple separated segments is less clear due to the heavy contamination of scattering sources and local velocity heterogeneities; nonetheless, results from the plane-wave-fitting approach still indicate the possibility for a consistent detection and location of the distant event; (4) automatic monitoring of microseismicity can be performed with DAS recordings with results comparable to manual analysis of geophone recordings in the case of events within or close to the DAS system (i.e. maximum horizontal error on event location around 70âm for both geophone and DAS data); and (5) DAS data preconditioning (e.g. temporal subsampling and channel stacking) and dedicated processing techniques are strictly necessary for making seismic monitoring procedures feasible and trustable.
We present a novel methodology for exploring 4D seismic data in the context of monitoring subsurface resources. Data-space exploration is a key activity in scientific research, but it has long been overlooked in favor of model-space investigations. Our methodology performs a data-space exploration that aims to define structures in the covariance matrix of the observational errors. It is based on Bayesian inferences, where the posterior probability distribution is reconstructed through trans-dimensional (trans-D) Markov chain Monte Carlo sampling. The trans-D approach applied to data-structures (termed "partitions") of the covariance matrix allows the number of partitions to freely vary in a fixed range during the McMC sampling. Due to the trans-D approach, our methodology retrieves data-structures that are fully data-driven and not imposed by the user. We applied our methodology to 4D seismic data, generally used to extract information about the variations in the subsurface. In our study, we make use of real data that we collected in the laboratory, which allows us to simulate different acquisition geometries and different reservoir conditions. Our approach is able to define and discriminate different sources of noise in 4D seismic data, enabling a data-driven evaluation of the quality (so-called "repeatability") of the 4D seismic survey. We find that: (a) trans-D sampling can be effective in defining data-driven data-space structures; (b) our methodology can be used to discriminate between different families of data-structures created from different noise sources. Coupling our methodology to standard model-space investigations, we can validate physical hypothesis on the monitored geo-resources.
Abstract The topography of orogenic belts responds to several contributions operating at short and long temporal and spatial (i.e., wavelengths) scales, from the surface to the deep mantle. Here, we aim to investigate the connection between morphometric characteristics, exhumation, and crustal deformation along and across the Italian Apennines, by comparing superficial with deeper data. Specifically, we present four sets of observations that are constructed by gathering previous data and adding new analyses and inferences, that include: (a) a new geomorphological set of analyses; (b) a database of available low temperature thermochronological cooling ages; (c) a reconstruction of drainage divide evolution in time and space based on the age of the youngest lacustrine deposits within each extensional basin; (d) Moho depth from receiver functions, gathering previous estimates and 13 new ones. From these sets of data, it emerges that across the main drainage divide of the Apennines, the morphological characteristics, the style of deformation and the spatial distribution of exhumation correlate with the geometries of the Moho and are associated with a strong asymmetry in the Northern‐Apennines and a clear symmetry in the Central‐Apennines. We interpret these results as evidence of a strong coupling between shallower and deeper geometries, that are most likely related to complex along‐strike variations in the Apennines geodynamic setting.
The project Retreating-trench, extension, and accretion tectonics, RETREAT, is a multidisciplinary study of the Northern Apennines (earth.geology.yale.edu/RETREAT/), funded by the United States National Science Foundation (NSF) in collaboration with the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV) and the Grant Agency of the Czech Academy of Sciences (GAAV). The main goal of RETREAT is to develop a self-consistent dynamic model of syn-convergent extension, using the Northern Apennines as a natural laboratory. In the context of this project a passive seismological experiment was deployed in the fall of 2003 for a period of three years. RETREAT seismologists aim to develop a comprehensive understanding of the deep structure beneath the Northern Apennines, with particular attention on inferring likely patterns of mantle flow. Specific objectives of the project are the crustal and lithospheric thicknesses, the location and geometry of the Adriatic slab, and the distribution of seismic anisotropy laterally and vertically in the lithosphere and asthenosphere. The project is collecting teleseismic and regional earthquake data for 3 years. This contribution describes the RETREAT seismic deployment and reports on key results from the first year of the deployment. We confirm some prior findings regarding the seismic structure of Central Italy, but our observations also highlight the complexity of the Northern Apennines subduction system.
Local earthquake tomography is a non-linear and non-unique inverse problem that uses event arrival times to solve for the spatial distribution of elastic properties. The typical approach is to apply iterative linearization and derive a preferred solution, but such solutions are biased by a number of subjective choices: the starting model that is iteratively adjusted, the degree of regularization used to obtain a smooth solution, and the assumed noise level in the arrival time data. These subjective choices also affect the estimation of the uncertainties in the inverted parameters. The method presented here is developed in a Bayesian framework where a priori information and measurements are combined to define a posterior probability density of the parameters of interest: elastic properties in a subsurface 3-D model, hypocentre coordinates and noise level in the data. We apply a trans-dimensional Markov chain Monte Carlo algorithm that asymptotically samples the posterior distribution of the investigated parameters. This approach allows us to overcome the issues raised above. First, starting a number of sampling chains from random samples of the prior probability distribution lessens the dependence of the solution from the starting point. Secondly, the number of elastic parameters in the 3-D subsurface model is one of the unknowns in the inversion, and the parsimony of Bayesian inference ensures that the degree of detail in the solution is controlled by the information in the data, given realistic assumptions for the error statistics. Finally, the noise level in the data, which controls the uncertainties of the solution, is also one of the inverted parameters, providing a first-order estimate of the data errors. We apply our method to both synthetic and field arrival time data. The synthetic data inversion successfully recovers velocity anomalies, hypocentre coordinates and the level of noise in the data. The Bayesian inversion of field measurements gives results comparable to those obtained independently by linearized inversion, reconstructing the geometry of the main seismic velocity anomalies. The quantification of the posterior uncertainties, a crucial output of Bayesian inversion, allows for visualizing regions where elastic properties are closely constrained by the data and is used here to directly compare our results to the ones obtained with the linearized inversion. In the case we examined the results of two inversion techniques are not significantly different.
We used receiver functions (RFs) from broad‐band seismic stations to investigate the crustal structure of the Northern Apennines, Italy. Additionally, we use data obtained in this study to provide initial constraints for a map of the Moho depth of Italy. Ten stations were deployed along a transect [N75°E] during the 1994 GeoModAp project. RF analysis shows the presence of lateral variations in the crust. We observed patterns of symmetric and anti‐symmetric converted phases from radial and tangential RFs vs. back‐azimuth. These patterns can be explained by the presence of dipping interfaces and/or anisotropy within the crust. We then inverted RFs following the inversion scheme proposed by Sambridge [1999] . The results show the presence of S ‐velocity inversions in the lower crust beneath the Apennines, the upwelling of Moho in the Tyrrhenian area and its progressive deepening from the Tyrrhenian Sea toward the Adriatic coast.
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