Magma and fluid migration at Yellowstone Caldera in the last three decades inferred from InSAR, leveling and gravity measurements

We studied the Yellowstone caldera geological unrest between 1977 and 2010 by investigating temporal changes in differential Interferometric Synthetic Aperture Radar (InSAR), precise spirit leveling and gravity measurements. The analysis of the 1992–2010 displacement time series, retrieved by applying the SBAS InSAR technique, allowed the identification of three areas of deformation: (i) the Mallard Lake (ML) and Sour Creek (SC) resurgent domes, (ii) a region close to the Northern Caldera Rim (NCR), and (iii) the eastern Snake River Plain (SRP). While the eastern SRP shows a signal related to tectonic deformation, the other two regions are influenced by the caldera unrest. We removed the tectonic signal from the InSAR displacements, and we modeled the InSAR, leveling, and gravity measurements to retrieve the best fitting source parameters. Our findings confirmed the existence of different distinct sources, beneath the brittle-ductile transition zone, which have been intermittently active during the last three decades. Moreover, we interpreted our results in the light of existing seismic tomography studies. Concerning the SC dome, we highlighted the role of hydrothermal fluids as the driving force behind the 1977–1983 uplift; since 1983–1993 the deformation source transformed into a deeper one with a higher magmatic component. Furthermore, our results support the magmatic nature of the deformation source beneath ML dome for the overall investigated period. Finally, the uplift at NCR is interpreted as magma accumulation, while its subsidence could either be the result of fluids migration outside the caldera or the gravitational adjustment of the source from a spherical to a sill-like geometr

Brillouin optical time-domain analysis for geotechnical monitoring

AbstractIn this paper, we show some recent experimental applications of Brillouin optical time-domain analysis (BOTDA) based sensors for geotechnical monitoring. In particular, how these sensors can be applied to detecting early movements of soil slopes by the direct embedding of suitable fiber cables in the ground is presented. Furthermore, the same technology can be used to realize innovative inclinometers, as well as smart foundation anchors

Precision agriculture to improve the monitoring and management of tomato insect pests

Human-based monitoring of arthropod pests of agricultural importance is usually a time-consuming and costly activity. The advent of technologies such as automatic traps opens new opportunities for remote monitoring. In this article, we present a novel Artificial Intelligence (AI)-based approach aimed to developing a smart trap for monitoring two major pests of greenhouse tomatoes, namely whiteflies, i.e., Bemisia tabaci and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae), and leaf miner flies, Liriomyza spp. (Diptera: Agromyzidae)

An integrated approach for rock slope failure monitoring: The case study of Coroglio tuff cliff (Naples, Italy) - Preliminary results

The paper re ports the i mple mentation of an integrate d syste m ai me d at the real-ti me monitoring of a series of physical parame ters controlling the r ock slope stability. The system has bee n installe d on the Cor oglio tuff cliff, loc ate d in the highly ur banize d coastal area of Naples (Italy) at the bor der of the acti ve volcanic cal der a of Campi Flegrei. Preliminar y results obtai ne d during the first ye ar of data ac quisition and monitoring acti vi ty (Dece mber 2014 – January 2016) are also discussed on the basis of statistical models. (3) (PDF) An integrated approach for rock slope failure monitoring: the case study of Coroglio tuff cliff (Naples, Italy) – preliminary results. Available from: https://www.researchgate.net/publication/299340773_An_integrated_approach_for_rock_slope_failure_monitoring_the_case_study_of_Coroglio_tuff_cliff_Naples_Italy_-_preliminary_results [accessed Feb 27 2020].Published242-2471IT. Reti di monitoraggio e sorveglianzaN/A or not JC

Uplift and magma intrusion at Long Valley caldera from InSAR and gravity measurements

The Long Valley caldera (California) formed ~760,000 yr ago following the massive eruption of the Bishop Tuff. Postcaldera volcanism in the Long Valley volcanic fi eld includes lava domes as young as 650 yr. The recent geological unrest is characterized by uplift of the resurgent dome in the central section of the caldera (75 cm in the past 33 yr) and earthquake activity followed by periods of relative quiescence. Since the spring of 1998, the caldera has been in a state of low activity. The cause of unrest is still debated, and hypotheses range from hybrid sources (e.g., magma with a high percentage of volatiles) to hydrothermal fl uid intrusion. Here, we present observations of surface deformation in the Long Valley region based on differential synthetic aperture radar interferometry (InSAR), leveling, global positioning system (GPS), two-color electronic distance meter (EDM), and microgravity data. Thanks to the joint application of InSAR and microgravity data, we are able to unambiguously determine that magma is the cause of unrest

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

A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps

Integrated Pest Management (IPM) is an essential approach used in smart agriculture to manage pest populations and sustainably optimize crop production. One of the cornerstones underlying IPM solutions is pest monitoring, a practice often performed by farm owners by using chromotropic sticky traps placed on insect hot spots to gauge pest population densities. In this paper, we propose a modular model-agnostic deep learning-based counting pipeline for estimating the number of insects present in pictures of chromotropic sticky traps, thus reducing the need for manual trap inspections and minimizing human effort. Additionally, our solution generates a set of raw positions of the counted insects and confidence scores expressing their reliability, allowing practitioners to filter out unreliable predictions. We train and assess our technique by exploiting PST - Pest Sticky Traps, a new collection of dot-annotated images we created on purpose and we publicly release, suitable for counting whiteflies. Experimental evaluation shows that our proposed counting strategy can be a valuable Artificial Intelligence-based tool to help farm owners to control pest outbreaks and prevent crop damages effectively. Specifically, our solution achieves an average counting error of approximately compared to human capabilities requiring a matter of seconds, a large improvement respecting the time-intensive process of manual human inspections, which often take hours or even days

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health

Novel Optical Chemical Sensor Based on Molecularly Imprinted Polymer Inside a Trench Micro-machined in Double Plastic Optical Fiber☆

Abstract For the detection of chemical agents in different environments, the combination of plastic optical fibers (POFs) and molecularly imprinted polymer (MIP) layers has been tested as a way to obtain a low cost, highly selective and sensitive surface plasmon resonance (SPR) chemical sensor. A novel type of optical chemical sensor based on POF-MIP has been designed and fabricated, and in this work it has been applied for the selective detection of dibenzyl disulfide (DBDS) in transformer oil. This analyte is important in the control of transformer oil, since it is responsible for the corrosive properties of the oil. The new optical sensor platform is based on two plastic optical fibers coupled through a polymer molecularly imprinted for DBDS. The new sensor has been found to be useful for the determination of DBDS in transformer oil