Feasibility to Use Continuous Magnetotelluric Observations for Monitoring Hydrothermal Activity. Numerical Modeling Applied to Campi Flegrei Volcanic System (Southern Italy)

The magnetotelluric (MT) method is useful for monitoring geophysical processes because of a large dynamic depth range. In this paper, a feasibility study of employing continuous MT observations to monitor hydrothermal systems for both volcanic hazard assessment and geothermal energy exploitation is presented. Sensitivity of the MT method has been studied by simulating spatial and temporal evolution of temperature and gas saturation distributions in a hydrothermal system, and by calculating the MT response at different time steps. Two possible scenarios have been considered: the first is related to an increase in the fluid flow rate at the system source, the second is associated to an increase in the permeability of the rocks hosting the hydrothermal system. Numerical simulations have been performed for each scenario, and the sensitivity of the MT monitoring has been analyzed by evaluating the time interval needed to observe significant variations in the MT response. This study has been applied to the hydrothermal system of the Campi Flegrei (CF; Southern Italy) and it has shown that continuous MT measurements are not sensitive enough to detect a significant increase in the source fluid flow rate over time intervals less than 10 years. On the contrary, if the permeability of the upwelling zone increases, a measurable change in the MT response occurs over a time interval ranging from 6 months to 3 years, depending on the extent of the permeability increase. Such findings are promising and suggest that continuous MT observations in active volcanic areas can be useful for imaging volcano–hydrothermal system activity

Hydrothermal system monitoring by continuous magnetotelluric time series: sensitivity analysis and data denoising

The MT method is a good candidate for characterizing the dynamics of geothermal or hydrothermal systems as it is suitable for deep exploration of the subsurface (from one hundred meters to hundreds of kilometers) in terms of electrical resistivity values, which are strongly sensitive to variations in underground fluid temperature and gas saturation. However, at present, the potential of the MT method for monitoring purposes has not been completely assessed as the only studies in this field focus on monitoring fluid injections in Enhanced Geothermal Systems. The present PhD thesis aims to provide a contribution in this research field by presenting the first attempt for studying the sensitivity of the MT response to natural geothermal (or hydrothermal) system variations. The sensitivity of the MT method has been studied by simulating spatial and temporal evolution of temperature and gas saturation distributions in a hydrothermal system and by calculating the MT response at different time steps through continuous MT measurements. In particular, two possible scenarios have been considered: the first related to an increase in the fluid flow rate from the system source, the second associated to an increase in the permeability of the rocks hosting the hydrothermal system. For each scenario, the sensitivity has been analyzed by evaluating the time interval needed to observe significant variations in the MT response. This study has been applied to the hydrothermal system of the Campi Flegrei volcanic district (southern Italy) and it has shown that the MT monitoring is much more sensitive to changes in rock permeability rather than in the fluid flow rate emitted by the source. In general, long time intervals not useful for volcano monitoring purposes are found if only changes in fluid flow rate are assumed to govern the hydrothermal system dynamics. Conversely, by increasing the permeability of the hosting rocks up to about one order of magnitude, significant resistivity variations are observed over a period ranging from one year and a half to three months. Such findings are promising and encourage the use of the continuous MT measurements in active volcano-hydrothermal areas. Due to the high sensitivity of the MT data to presence of man-made noises that, if not properly detected, can lead to biased resistivity estimates, the present thesis focused also on the development of two non-standard denoising techniques for magnetotelluric data. Both the developed filters are based on the decomposition and analyses of the MT signal through the Discrete Wavelet Transform (DWT), whose use represents an innovative approach in MT processing. The DWT has been chosen considering the resolution that it provides in both time and frequency domains making it a powerful tool to deal with transient and non-stationary signals, as the man-made noise usually appears on the magnetotelluric recordings. The first proposed filter, called polarization filter, aims at detecting the presence of noise by analyzing the polarization of different portions of the electric components of the MT field and by removing those portions whose polarization is higher than a specific threshold. The second filter, called SOM filter, aims at discriminating noisy and clean impedance tensor estimates through a clustering performed with the Self-Organizing Map neural network. Both filters applied to synthetic and field MT data have proven effective in noise detection and in improving the quality of the impedance tensor estimates

Denoising of magnetotelluric data by polarization analysis in the discrete wavelet transform domain

The development of denoising techniques of magnetotelluric (MT) data affected by cultural noise is currently one of the most important objective to make magnetotellurics reliably in urban or industrialized areas. In this work, a new denoising technique of MT data affected by temporally localized noise is proposed. It is based on the polarization analysis of the MT field in the time-frequency domain achieved through a discrete wavelet transform. This transform, thanks to the possibility to operate in both time and frequency domains, allows the automatic detection of transient components within the MT signal possibly due to disturbances of anthropic nature. Unlike the continuous wavelet transform, it permits to reconstruct the denoised signal in the time domain in order to test the effectiveness of the filter. Applications to both synthetic and field MT data have shown the ability of the implemented filter to detect and remove effectively the cultural noise

Simulations of the emptying of a closed chamber by magma ascent dynamics based on self-organized fracture mechanisms

The emptying of a magma chamber can trigger eruptive series of events that can be very different in duration and explosivity degree. Usually, erupted magma is a mixture of magmas that originate at various depths and can significantly affect the style of the eruptive processes. In this work, possible correlations between depth of origin of magma and eruption size are investigated using a cellular automaton model that describes magma ascent in a buoyancy field as a diffusive dynamics on self-organized fracture networks. Interestingly, the model predicts that erupted magma is, generally, a mixture of magma that has continuously stopped during the whole ascent path from the chamber to the surface, except for eruptions above a given size threshold, for which it is possible to distinguish two dominant components deriving from specific depth ranges. Such a finding can provide a theoretical framework for the general feature of many volcanic eruptions whose deposits are characterized by two different magmas. Furthermore, in the repose times distribution, a timescale separation between short and long more probable repose times is found, which increases by deepening of the magma chamber. The identification of two different types of repose times suggests the presence of different patterns, which could help the understanding of magmatic processes responsible of different eruptive regimes that may characterize the life of a volcano

Magnetotellurics as a multiscale geophysical exploration method

Magnetotellurics (MT) is a geophysical method based on the use of natural electromagnetic signals to define subsurface electrical resistivity structure through electromagnetic induction. MT waves are generated in the Earth’s atmosphere and magnetosphere by a range of physical processes, such as magnetic storms, micropulsations, lightning activity. Since the underground MT wave propagation is of diffusive type, the longer is the wavelength (i.e. the lower the wave frequency) the deeper will be the propagation depth. Considering the frequency band commonly used in MT prospecting (10-4 Hz to 104 Hz), the investigation depth ranges from few hundred meters to hundreds of kilometers. This means that magnetotellurics is inherently a multiscale method and, thus, appropriate for applications at different scale ranging from aquifer system characterization to petroleum and geothermal research. In this perspective, the application of the Wavelet transform to the MT data analysis could represent an excellent tool to emphasize characteristics of the MT signal at different scales. In this note, the potentiality of such an approach is studied. In particular, we show that the use of a Discrete Wavelet (DW) decomposition of measured MT time-series data allows to retrieve robust information about the subsoil resistivity over a wide range of spatial (depth) scales, spanning up to 5 orders of magnitude. Furthermore, the application of DWs to MT data analysis has proven to be a flexible tool for advanced data processing (e.g. non-linear filtering, denoising and clustering)

Denoising of magnetotelluric signals by polarization analysis in the discrete wavelet domain

Magnetotellurics (MT) is one of the prominent geophysical methods for underground deep exploration and, thus, appropriate for applications to petroleum and geothermal research. However, it is not completely reliable when applied in areas characterized by intense urbanization, as the presence of cultural noise may significantly affect the MT impedance tensor estimates and, consequently, the apparent resistivity values that describe the electrical behaviour of the investigated buried structures. The development of denoising techniques of MT data is thus one of the main objectives to make magnetotellurics reliably even in urban or industrialized environments. In this work we propose an algorithm for filtering of MT data affected by temporally localized noise. It exploits the discrete wavelet transform (DWT) that, thanks to the possibility to operates in both time and frequency domain, allows to detect transient components of the MT signal, likely due to disturbances of anthropic nature. The implemented filter relies on the estimate of the ellipticity of the polarized MT wave. The application of the filter to synthetic and field MT data has proven its ability in detecting and removing cultural noise, thus providing apparent resistivity curves more smoothed than those obtained by using raw signals

Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy)

The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes

Quantitative interpretation ofmultiple self-potential anomaly sources by a global optimization approach

Multiple self-potential (SP) anomalies are analyzed by using a Genetic-Price Algorithm (GPA), which has been recently introduced for the inversion of SP data. The proposed approach is tested on multiple synthetic anomalies, which are modeled by horizontal cylinders. First, a forward modeling is used to analyze the resolution of such anomalies by varying all model parameters. Then, GPA is applied to invert synthetic multiple SP anomalies. The numerical analyses show that the proposed approach is able to fully characterize the anomaly sources by providing the correct values of the model parameters as well as the number of sources, even if Gaussian random noise is added to the synthetic data. Furthermore, to show the computational efficiency of GPA, the results of a comparative analysis with the Very Fast Simulated Annealing algorithm are given. The validity of the GPA approach is confirmed by its application to three examples of self-potential field data from mineral exploration and groundwater investigations, which are presented and discussed in relation to other inversion approaches. Finally, the quantitative interpretation of multiple anomalies along a SP profile crossing the Mt. Somma-Vesuvius volcano caldera (southern Italy) is provided

High-resolution geoelectrical characterization and monitoring of natural fluids emission systems to understand possible gas leakages from geological carbon storage reservoirs

Abstract Gas leakage from deep geologic storage formations to the Earth’s surface is one of the main hazards in geological carbon sequestration and storage. Permeable sediment covers together with natural pathways, such as faults and/or fracture systems, are the main factors controlling surface leakages. Therefore, the characterization of natural systems, where large amounts of natural gases are released, can be helpful for understanding the effects of potential gas leaks from carbon dioxide storage systems. In this framework, we propose a combined use of high-resolution geoelectrical investigations (i.e. resistivity tomography and self-potential surveys) for reconstructing shallow buried fracture networks in the caprock and detecting preferential gas migration pathways before it enters the atmosphere. Such methodologies appear to be among the most suitable for the research purposes because of the strong dependence of the electrical properties of water-bearing permeable rock, or unconsolidated materials, on many factors relevant to CO2 storage (i.e. porosity, fracturing, water saturation, etc.). The effectiveness of the suggested geoelectrical approach is tested in an area of natural gas degassing (mainly CH4) located in the active fault zone of the Bolle della Malvizza (Southern Apennines, Italy), which could represent a natural analogue of gas storage sites due to the significant thicknesses (hundreds of meters) of impermeable rock (caprock) that is generally required to prevent carbon dioxide stored at depth from rising to the surface. The obtained 3D geophysical model, validated by the good correlation with geochemical data acquired in the study area and the available geological information, provided a structural and physical characterization of the investigated subsurface volume. Moreover, the time variations of the observed geophysical parameters allowed the identification of possible migration pathways of fluids to the surface