New insight into the December 2018 Etna eruption through the joint inversion of ground deformation and gravity data

2020 ◽  
Author(s):  
Mahak Singh Chauhan ◽  
Flavio Cannavò ◽  
Daniele Carbone ◽  
Filippo Greco
Keyword(s):  
Search Algorithm ◽  
Ground Deformation ◽  
Joint Inversion ◽  
Gravity Data ◽  
Monitoring Network ◽  
Pattern Search ◽  
Gravity Change ◽  
Model Parameters ◽  
Geometrical Parameters ◽  
Superconducting Gravimeters

<p>We focus on the eruption of Mt. Etna which took place on 24 December, 2018. The eruption occurred after a month of unrest and was accompanied by a seismic swarm that culminated in the M4.9 earthquake on the 26th, with epicentre on the eastern flank of the volcano. We jointly analyse ground deformation and gravity data to estimate the geometrical and kinematic parameters of the source structure, together with the density of the intruding material. The data used in this study were recorded by stations in the INGV-OE monitoring network (21 GPS stations and 2 gravity stations equipped with superconducting gravimeters), during the interval of 23 to 28 December (pre to post eruption). We assume a dike-type source for the forward calculation in the defined objective function. A pattern search algorithm (PSA) is used for the iterative minimization of the misfit error. In order to estimate the posterior probability density function (PDFs) of the model parameters, we also use a Markov Chain Monte Carlo (MCMC) approach. Indeed, the calculated PDFs provide more information about the uncertainties of the model parameters, which helps to understand overall tendencies of the solutions. We first test the constrained inversion of the gravity data, to calculate the density of eruptive magmatic body, by fixing the geometrical parameters of the dike, previously retrieved through inversion of the deformation data only. Using this approach, it is possible to suitable explain the deformation data and the gravity change observed at the station in the near field (MNT), while the gravity change at the other station (SLN) remain unexplained. We then invert jointly both deformation and gravity datasets, in order to adequately fit all the observations. The final model gives a density value of ~1.8-2.0 g/cm3. This value is significantly lower than the density of bubble-free magma and indicates either the involvement of gas in the intrusive process, or the formation of dry fissures during the emplacement of the dyke.</p>

scholarly journals An application of the NSGA-II algorithm in Pareto joint inversion of 2D magnetic and gravity data

2021 ◽  
Vol 47 (2) ◽  
pp. 59-70
Author(s):  
Katarzyna Miernik ◽  
Elżbieta Węglińska ◽  
Tomasz Danek ◽  
Andrzej Leśniak
Keyword(s):  
Pareto Front ◽  
Joint Inversion ◽  
Gravity Data ◽  
Model Parameters ◽  
Final Solution ◽  
Inversion Process ◽  
Nsga Ii ◽  
Heat Map ◽  
Real Model ◽  
All Solutions

Joint inversion is a widely used geophysical method that allows model parameters to be obtained from the observed data. Pareto inversion results are a set of solutions that include the Pareto front, which consists of non-dominated solutions. All solutions from the Pareto front are considered the most feasible models from which a particular one can be chosen as the final solution. In this paper, it is shown that models represented by points on the Pareto front do not reflect the shape of the real model. In this contribution, a collective approach is proposed to interpret the geometry of models retrieved in inversion. Instead of choosing single solutions from the Pareto front, all obtained solutions were combined in one “heat map”, which is a plot representing the frequency of points belonging to all returned objects from the solution set. The conducted experiment showed that this approach limits the problem of equivalence and is a promising way of representing the geometry of the model that was retrieved in the inversion process.

scholarly journals Pareto Joint Inversion of 2D magnetometric and gravity data- synthetic study

2019 ◽  
Vol 133 ◽  
pp. 01009
Author(s):  
Tomasz Danek ◽  
Andrzej Leśniak ◽  
Katarzyna Miernik ◽  
Elżbieta Śledź
Keyword(s):  
Pareto Front ◽  
Joint Inversion ◽  
Gravity Data ◽  
Group Analysis ◽  
Geophysical Methods ◽  
Model Parameters ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Promising Tool ◽  
Real Model

Pareto joint inversion for two or more data sets is an attractive and promising tool which eliminates target functions weighing and scaling, providing a set of acceptable solutions composing a Pareto front. In former author’s study MARIA (Modular Approach Robust Inversion Algorithm) was created as a flexible software based on global optimization engine (PSO) to obtain model parameters in process of Pareto joint inversion of two geophysical data sets. 2D magnetotelluric and gravity data were used for preliminary tests, but the software is ready to handle data from more than two geophysical methods. In this contribution, the authors’ magnetometric forward solver was implemented and integrated with MARIA. The gravity and magnetometry forward solver was verified on synthetic models. The tests were performed for different models of a dyke and showed, that even when the starting model is a homogeneous area without anomaly, it is possible to recover the shape of a small detail of the real model. Results showed that the group analysis of models on the Pareto front gives more information than the single best model. The final stage of interpretation is the raster map of Pareto front solutions analysis.

Joint Gramian inversion of geophysical data with different resolution capabilities: case study in Yellowstone

2021 ◽  
Author(s):  
Xiaolei Tu ◽  
Michael S Zhdanov
Keyword(s):  
Joint Inversion ◽  
Gravity Data ◽  
Geophysical Methods ◽  
Structural Similarity ◽  
Geophysical Data ◽  
P Wave ◽  
Model Parameters ◽  
Magmatic System ◽  
Low Velocity ◽  
Velocity Anomaly

Summary Joint inversion of multiphysics data is a practical approach to the integration of geophysical data, which produces models of reduced uncertainty and improved resolution. The development of effective methods of joint inversion requires considering different resolutions of different geophysical methods. This paper presents a new framework of joint inversion of multiphysics data, which is based on a novel formulation of Gramian constraints and mitigates the difference in resolution capabilities of different geophysical methods. Our approach enforces structural similarity between different model parameters through minimizing a structural Gramian term, and it also balances the different resolutions of geophysical methods using a multiscale resampling strategy. The effectiveness of the proposed method is demonstrated by synthetic model study of joint inversion of the P-wave traveltime and gravity data. We apply a novel method based on Gramian constraints and multiscale resampling to jointly invert the gravity and seismic data collected in Yellowstone national Park to image the crustal magmatic system of the Yellowstone. Our results helped to produce a consistent image of the crustal magmatic system of the Yellowstone expressed both in low-density and low-velocity anomaly just beneath the Yellowstone caldera.

scholarly journals Forty-three years of absolute gravity observations of the Fennoscandian postglacial rebound in Finland

2021 ◽  
Vol 95 (2) ◽  
Author(s):  
Mirjam Bilker-Koivula ◽  
Jaakko Mäkinen ◽  
Hannu Ruotsalainen ◽  
Jyri Näränen ◽  
Timo Saari
Keyword(s):  
Gravity Data ◽  
Gravity Change ◽  
Global Navigation Satellite Systems ◽  
Absolute Gravity ◽  
Data Sets ◽  
Postglacial Rebound ◽  
Land Uplift ◽  
Satellite Systems ◽  
Gravity Measurements ◽  
Global Navigation Satellite

AbstractPostglacial rebound in Fennoscandia causes striking trends in gravity measurements of the area. We present time series of absolute gravity data collected between 1976 and 2019 on 12 stations in Finland with different types of instruments. First, we determine the trends at each station and analyse the effect of the instrument types. We estimate, for example, an offset of 6.8 μgal for the JILAg-5 instrument with respect to the FG5-type instruments. Applying the offsets in the trend analysis strengthens the trends being in good agreement with the NKG2016LU_gdot model of gravity change. Trends of seven stations were found robust and were used to analyse the stabilization of the trends in time and to determine the relationship between gravity change rates and land uplift rates as measured with global navigation satellite systems (GNSS) as well as from the NKG2016LU_abs land uplift model. Trends calculated from combined and offset-corrected measurements of JILAg-5- and FG5-type instruments stabilized in 15 to 20 years and at some stations even faster. The trends of FG5-type instrument data alone stabilized generally within 10 years. The ratio between gravity change rates and vertical rates from different data sets yields values between − 0.206 ± 0.017 and − 0.227 ± 0.024 µGal/mm and axis intercept values between 0.248 ± 0.089 and 0.335 ± 0.136 µGal/yr. These values are larger than previous estimates for Fennoscandia.

scholarly journals Anatomy of a Paroxysmal Lava Fountain at Etna Volcano: The Case of the 12 March 2021, Episode

2021 ◽  
Vol 13 (15) ◽  
pp. 3052
Author(s):  
Sonia Calvari ◽  
Alessandro Bonaccorso ◽  
Gaetana Ganci
Keyword(s):  
Lava Flow ◽  
Ground Deformation ◽  
Monitoring Network ◽  
Lava Flows ◽  
Etna Volcano ◽  
Lava Fountain ◽  
Eruptive Episode ◽  
Lava Fountains ◽  
Borehole Strainmeter ◽  
Ash Plume

On 13 December 2020, Etna volcano entered a new eruptive phase, giving rise to a number of paroxysmal episodes involving increased Strombolian activity from the summit craters, lava fountains feeding several-km high eruptive columns and ash plumes, as well as lava flows. As of 2 August 2021, 57 such episodes have occurred in 2021, all of them from the New Southeast Crater (NSEC). Each paroxysmal episode lasted a few hours and was sometimes preceded (but more often followed) by lava flow output from the crater rim lasting a few hours. In this paper, we use remote sensing data from the ground and satellite, integrated with ground deformation data recorded by a high precision borehole strainmeter to characterize the 12 March 2021 eruptive episode, which was one of the most powerful (and best recorded) among that occurred since 13 December 2020. We describe the formation and growth of the lava fountains, and the way they feed the eruptive column and the ash plume, using data gathered from the INGV visible and thermal camera monitoring network, compared with satellite images. We show the growth of the lava flow field associated with the explosive phase obtained from a fixed thermal monitoring camera. We estimate the erupted volume of pyroclasts from the heights of the lava fountains measured by the cameras, and the erupted lava flow volume from the satellite-derived radiant heat flux. We compare all erupted volumes (pyroclasts plus lava flows) with the total erupted volume inferred from the volcano deflation recorded by the borehole strainmeter, obtaining a total erupted volume of ~3 × 106 m3 of magma constrained by the strainmeter. This volume comprises ~1.6 × 106 m3 of pyroclasts erupted during the lava fountain and 2.4 × 106 m3 of lava flow, with ~30% of the erupted pyroclasts being remobilized as rootless lava to feed the lava flows. The episode lasted 130 min and resulted in an eruption rate of ~385 m3 s−1 and caused the formation of an ash plume rising from the margins of the lava fountain that rose up to 12.6 km a.s.l. in ~1 h. The maximum elevation of the ash plume was well constrained by an empirical formula that can be used for prompt hazard assessment.

Assessing Model Uncertainty for the Scaling Function Inversion of Potential Fields

Geophysics ◽  
2021 ◽  
pp. 1-39
Author(s):  
Mahak Singh Chauhan ◽  
Ivano Pierri ◽  
Mrinal K. Sen ◽  
Maurizio FEDI
Keyword(s):  
Simulated Annealing Algorithm ◽  
Scaling Function ◽  
Vertical Section ◽  
Gravity Data ◽  
Geometric Model ◽  
Salt Dome ◽  
The Body ◽  
Gravity Inversion ◽  
Geometrical Parameters ◽  
The Scaling Function

We use the very fast simulated annealing algorithm to invert the scaling function along selected ridges, lying in a vertical section formed by upward continuing gravity data to a set of altitudes. The scaling function is formed by the ratio of the field derivative by the field itself and it is evaluated along the lines formed by the zeroes of the horizontal field derivative at a set of altitudes. We also use the same algorithm to invert gravity anomalies only at the measurement altitude. Our goal is analyzing the different models obtained through the two different inversions and evaluating the relative uncertainties. One main difference is that the scaling function inversion is independent on density and the unknowns are the geometrical parameters of the source. The gravity data are instead inverted for the source geometry and the density simultaneously. A priori information used for both the inversions is that the source has a known depth to the top. We examine the results over the synthetic examples of a salt dome structure generated by Talwani’s approach and real gravity datasets over the Mors salt dome and the Decorah (USA) basin. For all these cases, the scaling function inversion yielded models with a better sensitivity to specific features of the sources, such as the tilt of the body, and reduced uncertainty. We finally analyzed the density, which is one of the unknowns for the gravity inversion and it is estimated from the geometric model for the scaling function inversion. The histograms over the density estimated at many iterations show a very concentrated distribution for the scaling function, while the density contrast retrieved by the gravity inversion, according to the fundamental ambiguity density/volume, is widely dispersed, this making difficult to assess its best estimate.

scholarly journals Disjoint interval bound constraints using the alternating direction method of multipliers (ADMM) for geologically constrained inversion: Application to gravity data

Geophysics ◽  
2020 ◽  
pp. 1-45
Author(s):  
Vitaliy Ogarko ◽  
Jérémie Giraud ◽  
Roland Martin ◽  
Mark Jessell
Keyword(s):  
Gravity Data ◽  
Alternating Direction Method ◽  
Rock Properties ◽  
Model Parameters ◽  
Method Of Multipliers ◽  
Geological Modeling ◽  
Bound Constraints ◽  
Data Inversion ◽  
Alternating Direction ◽  
Geological Information

To reduce uncertainties in reconstructed images, geological information must be introduced in a numerically robust and stable way during the geophysical data inversion procedure. In the context of potential (gravity) data inversion, it is important to bound the physical properties by providing probabilistic information on the number of lithologies and ranges of values of possibly existing related rock properties (densities). For this purpose, we introduce a generalization of bounding constraints for geophysical inversion based on the alternating direction method of multipliers (ADMM). The flexibility of the proposed technique enables us to take into account petrophysical information as well as probabilistic geological modeling, when it is available. The algorithm introduces a priori knowledge in terms of physically acceptable bounds of model parameters based on the nature of the modeled lithofacies in the region under study. Instead of introducing only one interval of geologically acceptable values for each parameter representing a set of rock properties, we define sets of disjoint intervals using the available geological information. Different sets of intervals are tested, such as quasi-discrete (or narrow) intervals as well as wider intervals provided by geological information obtained from probabilistic geological modeling. Narrower intervals can be used as soft constraints encouraging quasi-discrete inversions. The algorithm is first applied to a synthetic 2D case for proof-of-concept validation and then to the 3D inversion of gravity data collected in the Yerrida basin (Western Australia). Numerical convergence tests show the robustness and stability of the bound constraints we apply, which is not always trivial for constrained inversions. This technique can be a more reliable uncertainty reduction method as well as an alternative to other petrophysically or geologically constrained inversions based on more classical “clustering” or Gaussian-mixture approaches.

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