Exploration of Data Space Through Trans-Dimensional Sampling: A Case Study of 4D Seismics.

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.

Sedimentary basins investigation using teleseismic P-wave time delays.

Passive seismic methods have been proven successful in recent years at retrieving information about the large-scale structure of a sedimentary basin. These methods are based on ambient noise recordings, and local and distant (teleseismic) earthquake data. In particular, it has been previously observed that the arrival time of teleseismic P-waves recorded inside a sedimentary basin shows time delays and polarization that both strongly depend on the basin properties and structure. In this paper, we present a new methodology for determining seismic P-wave velocity in a sedimentary basin, based on the time delay of a teleseismic P-wave travelling through the low-velocity basin infill, with respect to a teleseismic wave recorded outside the basin. The new methodology is developed in a Bayesian framework and, thus, it includes estimates of the uncertainties of the P-wave velocities. For this study, we exploit synchronous recordings of teleseismic P-wave arrivals at a dense linear array of broadband seismic stations, using data from two teleseismic events coming from two different incoming angles. The results obtained by the new proposed methodology are successfully compared to classical cross-correlation measurements, and are used to infer properties of a sedimentary basin, such as the basin bounding fault's geometry and the average P-wave velocity of the sedimentary basin fill.

Flexible Coupling in Joint Inversions: A Bayesian Structure Decoupling Algorithm.

When different geophysical observables are sensitive to the same volume, it is possible to invert them simultaneously to jointly constrain different physical properties. The question addressed in this study is to determine which structures (e.g., interfaces) are common to different properties and which ones are separated. We present an algorithm for resolving the level of spatial coupling between physical properties and to enable both common and separate structures in the same model. The new approach, called structure decoupling (SD) algorithm, is based on a Bayesian trans-dimensional adaptive parameterization, where models can display the full spectra of spatial coupling between physical properties, from fully coupled models, that is, where identical model geometries are imposed across all inverted properties, to completely decoupled models, where an independent parameterization is used for each property. We apply the algorithm to three 1-D geophysical inverse problems, using both synthetic and field data. For the synthetic cases, we compare the SD algorithm to standard Markov chain Monte Carlo and reversible-jump Markov chain Monte Carlo approaches that use either fully coupled or fully decoupled parameterizations. In case of coupled structures, the SD algorithm does not behave differently from methods that assume common interfaces. In case of decoupled structures, the SD approach is demonstrated to correctly retrieve the portion of profiles where the physical properties do not share the same structure. The application of the new algorithm to field data demonstrates its ability to decouple structures where a common stratification is not supported by the data.

Discovering geothermal supercritical fluids: a new frontier for seismic exploration.

Exploiting supercritical geothermal resources represents a frontier for the next generation of geothermal electrical power plant, as the heat capacity of supercritical fluids (SCF),which directly impacts on energy production, is much higher than that of fluids at subcritical conditions. Reconnaissance and location of intensively permeable and productive horizons at depth is the present limit for the development of SCF geothermal plants. We use, for the first time, teleseismic converted waves (i.e. receiver function) for discovering those horizons in the crust. Thanks to the capability of receiver function to map buried anisotropic materials, the SCF-bearing horizon is seen as the 4km-depth abrupt termination of a shallow, thick, ultra-high (>30%) anisotropic rock volume, in the center of the Larderello geothermal field. The SCF-bearing horizon develops within the granites of the geothermal field, bounding at depth the vapor-filled heavily-fractured rock matrix that hosts the shallow steam-dominated geothermal reservoirs. The sharp termination at depth of the anisotropic behavior of granites, coinciding with a 2 km-thick stripe of seismicity and diffuse fracturing, points out the sudden change in compressibility of the fluid filling the fractures and is a key-evidence of deep fluids that locally traversed the supercritical conditions. The presence of SCF and fracture permeability in nominally ductile granitic rocks open new scenarios for the understanding of magmatic systems and for geothermal exploitation.