The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series

Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments. Here we explore the limits of a CNN for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9cm for deformation signals alone, and 6.3cm when atmospheric artefacts are considered. Over-wrapping reduces this to 1.8cm and 5.0cm respectively as more fringes are generated without altering SNR. We test the approach on timeseries of cumulative deformation from Campi Flegrei and Dallol, where over-wrapping improves classication performance by up to 15%. We propose a mean-filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5cm/yr was detected after 60days and at Dallol, deformation of 3.5cm/yr was detected after 310 days. This corresponds to cumulative displacements of 3 cm and 4 cm consistent with estimates based on synthetic data

The application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Timeseries

Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments. Here we explore the limits of a CNN for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9cm for deformation signals alone, and 6.3cm when atmospheric artefacts are considered. Over-wrapping reduces this to 1.8cm and 5.0cm respectively as more fringes are generated without altering SNR. We test the approach on timeseries of cumulative deformation from Campi Flegrei and Dallol, where over-wrapping improves classication performance by up to 15%. We propose a mean-filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5cm/yr was detected after 60days and at Dallol, deformation of 3.5cm/yr was detected after 310 days. This corresponds to cumulative displacements of 3 cm and 4 cm consistent with estimates based on synthetic data

Surface deformation of active volcanic areas retrieved with the SBAS-DInSAR technique: an overview

This paper presents a comprehensive overview of the surface deformation retrieval capability of the Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm, referred to as Small BAseline Subset (SBAS) technique, in the context of active volcanic areas. In particular, after a brief description of the algorithm some experiments relevant to three selected case-study areas are presented. First, we concentrate on the application of the SBAS algorithm to a single-orbit scenario, thus considering a set of SAR data composed by images acquired on descending orbits by the European Remote Sensing (ERS) radar sensors and relevant to the Long Valley caldera (eastern California) area. Subsequently, we address the capability of the SBAS technique in a multipleorbit context by referring to Mt. Etna volcano (southern Italy) test site, with respect to which two different ERS data set, composed by images acquired both on ascending and descending orbits, are available. Finally, we take advantage of the capability of the algorithm to work in a multi-platform scenario by jointly exploiting two different sets of SAR images collected by the ERS and the Environment Satellite (ENVISAT) radar sensors in the Campi Flegrei caldera (southern Italy) area. The presented results demonstrate the effectiveness of the algorithm to investigate the deformation field in active volcanic areas and the potential of the DInSAR methodologies within routine surveillance scenario

Insar Role in the Study of Earth's Surface and Synergic Use with Other Geodetic Data: the 2014 South Napa Earthquake

This work focuses on the role of SAR Interferometry (InSAR) in the study of many phenomena characterizing the Earth's surface. We propose an advanced integration method in order to merge the InSAR data with other geodetic data, i.e. Multiple Aperture Interferometry (MAI), Pixel Offset Tracking (POT) and Global Positioning System (GPS). We apply the method to constrain the full 3D displacement field produced by the Mw 6.1 2014 South Napa Valley earthquake and then we used the results from the integration to perform the source modeling. The first Chapter is meant to introduce the topic of the progressive use of Remote Sensing geodetic data to support the activities of monitoring and hazard mitigation related to natural phenomena. Chapter 2 shows the application of the InSAR technique to reconstruct and model surface displacement fields induced by several phenomena. In Chapter 3, the 3D coseismic displacement map due to the 2014 Mw 6.1 South Napa earthquake, close the San Andreas Fault system (California), is estimated by using a method to merge InSAR and GPS data. InSAR data are provided by the latest satellite of the European Space Agency (ESA), i.e. Sentinel-1, whereas the GPS data were obtained from the BARD network and several online archives. In Chapter 4 we propose an improved algorithm for the data integration and test it on the Napa earthquake. Geodetic data from MAI and POT are added in the processing chain and the GPS data interpolation is modified according to the specific phenomenon. Futhermore, the source modeling is performed by inversion of the obtained 3D displacement component. The best fit is obtained by simulating a fracture in the fault segment in agreement with previous works. Finally, in the last chapter we discuss about the advantages and disadvantages of the data integration and the future perspectives

Review of works combining GNSS and insar in Europe

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach

Automatic Detection of Volcanic Unrest Using Interferometric Synthetic Aperture Radar

A diverse set of hazards are posed by the world's 1500 subaerial volcanoes, yet the majority of them remain unmonitored. Measurements of deformation provide a way to monitor volcanoes, and synthetic aperture RaDAR (SAR) provides a powerful tool to measure deformation at the majority of the world's subaerial volcanoes. This is due to recent changes in how regularly SAR data are acquired, how they are distributed to the scientific community, and how quickly they can be processed to create time series of interferograms. However, for interferometric SAR (InSAR) to be used to monitor the world's volcanoes, an algorithm is required to automatically detect signs of deformation-generating volcanic unrest in a time series of interferograms, as the volume of new interferograms produced each week precludes this task being achieved by human interpreters. In this thesis, I introduce two complementary methods that can be used to detect signs of volcanic unrest. The first method centres on the use of blind signal separation (BSS) methods to isolate signals of geophysical interest from nuisance signals, such as those due to changes in the refractive index of the atmosphere between two SAR acquisitions. This is achieved through first comparing which of non-negative matrix factorisation (NMF), principal component analysis (PCA), and independent component analysis (ICA) are best suited for solving BSS problems involving time series of InSAR data, and how InSAR data should best be arranged for its use with these methods. I find that NMF can be used with InSAR data, providing the time series is formatted in a novel way that reduces the likelihood of any pixels having negative values. However, when NMF, PCA, and ICA are applied to a set of synthetic data, I find that the most accurate recovery of signals of interest is achieved when ICA is set to recover spatially independent sources (termed sICA). I find that the best results are produced by sICA when interferograms are ordered as a simple ``daisy chain'' of short temporal baselines, and when sICA is set to recover around 1-3 more sources than were thought to have contributed to the time series. However, I also show that in cases such as deformation centred under a stratovolcano, the overlapping nature of a topographically correlated atmospheric phase screen (APS) signal and a deformation signal produces a pair of signals that are no longer spatially statistically independent, and so cannot be recovered accurately by sICA. To validate these results, I apply sICA to a time series of Sentinel-1 interferograms that span the 2015 eruption of Wolf volcano (Galapagos archipelago, Ecuador) and automatically isolate three signals of geophysical interest, which I validate by comparing with the results of other studies. I also apply the sICA algorithm to a time series of interferograms that image Mt Etna, and through isolating signals that are likely to be due to instability of the east flank of the volcano, show that the method can be applied to stratovolcanoes to recover useful signals. Utilising the ability of sICA to isolate signals of interest, I introduce a prototype detection algorithm that tracks changes in the behaviour of a subaerial volcano, and show that it could have been used to detect the onset of the 2015 eruption of Wolf. However, for use in an detection algorithm that is to be applied globally, the signals recovered by sICA cannot be manually validated through comparison with other studies. Therefore, I seek to incorporate a module into my detection algorithm that is able to quantify the significance of the sources recovered by sICA. I achieve this through extensively modernising the ICASO algorithm to create a new algorithm, ICASAR, that is optimised for use with InSAR time series. This algorithm allows me to assess the significance of signals recovered by sICA at a given volcano, and to then prioritise the tracking of any changes they exhibit when they are used in my detection algorithm. To further develop the detection algorithm, I create two synthetic time series that characterise the different types of unrest that could occur at a volcanic centre. The first features the introduction of a new signal, and my algorithm is able to detect when this signal enters the time series by tracking how well the baseline sources are able to fit new interferograms. The second features the change in rate of a signal that was present during the baseline stage, and my algorithm is able to detect when this change in rate occurs by tracking how sources recovered from the baseline data are used through time. To further test the algorithm, I extended the Sentinel-1 time series I used to study the 2015 eruption of Wolf to include the 2018 eruption of Sierra Negra, and I find that my algorithm is able to detect the increase in inflation that precedes the eruption, and the eruption itself. I also perform a small study into the pre-eruptive inflation seen at Sierra Negra using the deformation signal and its time history that are outputted by ICASAR. A Bayesian inversion is performed using the GBIS software package, in which the inflation signal is modelled as a horizontal rectangular dislocation with variable opening and uniform overpressure. Coupled with the time history of the inflation signal provided by ICASAR, this allows me to determine the temporal evolution of the pre-eruptive overpressure since the beginning of the Sentinel-1 time series in 2014. To extend this back to the end of the previous eruption in 2005, I use GPS data that spans the entire interruptive period. I find that the total interruptive pressure change is ~13.5 MPa, which is significantly larger than the values required for tensile failure of an elastic medium overlying an inflating body. I conclude that it is likely that one or more processes occurred to reduce the overpressure within the sill, and that the change in rate of inflation prior to the final failure of the sill is unlikely to be coincidental. The second method I develop to detect volcanic deformation in a time series of interferograms uses a convolutional neural network (CNN) to classify and locate deformation signals as each new interferogram is added to the time series. I achieve this through building a model that uses the five convolutional blocks of a previously state-of-the-art classification and localisation model, VGG16, but incorporates a classification output/head, and a localisation output/head. In order to train the model, I perform transfer learning and utilise the weights made freely available for the convolutional blocks of a version of VGG16 that was trained to classify natural images. I then synthesise a set of training data, but find that better performance is achieved on a testing set of Sentinel-1 interferograms when the model is trained with a mixture of both synthetic and real data. I conclude that CNNs can be built that are able to differentiate between different styles of volcanic deformation, and that they can perform localisation by globally reasoning with a 224 x 224 pixel interferogram without the need for a sliding window approach. The results I present in this thesis show that many machine learning methods can be applied to both time series of interferograms, and individual interferograms. sICA provides a powerful tool to separate some geophysical signals from atmospheric ones, and the ICASAR algorithm that I develop allows a user to evaluate the significance of the results provided by sICA. I incorporate these methods into an deformation detection algorithm, and show that this could be used to detect several types of volcanic unrest using data produced by the latest generation of SAR satellites. Additionally, the CNN I develop is able to differentiate between deformation signals in a single interferogram, and provides a complementary way to monitor volcanoes using InSAR

Hydrothermal fluid venting in the offshore sector of Campi Flegrei caldera: A geochemical, geophysical, and volcanological study

The ongoing unrest at the Campi Flegrei caldera (CFc) in southern Italy is prompting exploration of its poorly studied offshore sector. We report on a multidisciplinary investigation of the Secca delle Fumose (SdF), a submarine relief known since antiquity as the largest degassing structure of the offshore sector of CFc. We combined high-resolution morphobathymetric and seismostratigraphic data with onshore geological information to propose that the present-day SdF morphology and structure developed during the initial stages of the last CFc eruption at Monte Nuovo in AD 1538. We suggest that the SdF relief stands on the eastern uplifted border of a N-S-trending graben-like structure formed during the shallow emplacement of the Monte Nuovo feeding dike. We also infer that the high-angle bordering faults that generated the SdF relief now preferentially allow the ascent of hot brines (with an equilibrium temperature of 1798C), thereby sustaining hydrothermal degassing on the seafloor. Systematic vertical seawater profiling shows that hydrothermal seafloor venting generates a sizeable CO2, pH, and temperature anomaly in the overlying seawater column. Data for the seawater vertical profile can be used to estimate the CO2 and energy (heat) outputs from the SdF area at 50 tons/d (0.53 kg/s) and 80 MW, respectively. In view of the cause-effect relationship with the Monte Nuovo eruption, and the substantial gas and energy outputs, we consider that the SdF hydrothermal system needs to be included in monitoring programs of the ongoing CFc unrest

New approaches to volcanic hazard mapping at Campi Flegrei, Southern Italy

Hundreds of hazards maps have been produced to show the location of volcanic hazard for at least the past 150 years. They are presented in a variety of ways and play a key role in communication of hazards at volcanoes. Not all maps, however, provide enough or the right information to make informed decisions. The published hazard map for Campi Flegrei is not effective at communicating the complex hazards and uncertainties associated with calderas volcanic activity at a local scale. Using an interdisciplinary approach, this thesis investigates four very different topics specific to hazard communication at Campi Flegrei. These include: challenging the assumptions in vent opening susceptibility studies, unrest scenario mapping, determining map user preferences, and the development of hazard maps online. By focusing on these crucial aspects, this thesis contributes in making maps more useful and usable to a variety of different audiences in this region. This study found that existing assumptions have had a large impact on how hazard areas are determined, in particular, how the choice of historical eruptions governs future estimations of vent opening probability. Analysing the results of the unrest scenarios highlighted that the impacts of unrest will vary greatly dependent on location. Also, large sections of the electricity and transportation network are exposed to seismic hazard and could be unusable during future unrest. Map preferences were identified from a survey of 245 individuals (both globally and at Campi Flegrei), concluding that whilst design preferences were generally similar, preferences for data and map types were diverse. Thus, a single map could not suit the preferences of all users. Finally, the development of a suite of new web maps for Campi Flegrei has shown how this format can be utilised and its application within volcanic hazard mapping in general