Imaging the Volcanic Structures Beneath Gran Canaria Island Using New Gravity Data

Abstract

From a new gravity data set that covers homogeneously the whole surface of Gran Canaria (Canary Islands, Spain) and marine gravity data in the nearest offshore, we have obtained a Bouguer anomaly gravity map of the island which improves the previous ones. Using these gravity anomalies, we have applied a gravity inversion approach to investigate the structures beneath the surface of Gran Canaria Island and derive a 3D gravity sources model. The geometry of structures with anomalous density values is constrained up to a depth of approximately 20,000 m below the sea level. The interpretation of the density model identified structures related to the different volcanic stages of Gran Canaria. Several deep‐rooted high‐density structures represent the intrusive bodies emplaced in the early formation of Gran Canaria and the magma plumbing system of the Miocene volcanic edifices. A low‐density body in the center of the island may be associated with the syenitic core of the felsic central volcanic edifice (Tejeda Caldera). Shallow low‐density structures identified fractures which acted as feeder dikes of monogenetic volcanoes during the rejuvenated stage. Finally, the NW‐SE rift, which is the most important volcano‐tectonic structure of Gran Canaria, has a characteristic gravimetric signature and represents a long‐lived extensional fracture zone that has controlled the volcanic activity at least since the Miocene.

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling

“…Rocks with a null density contrast represent the typical subaerial volcanic products of Cumbre Vieja (lava flows and pyroclastic deposits). These density contrasts are based on the typical values for volcanic rocks of the Canary Islands (e.g., [52,53]) and are supported by other gravity studies in this area (e.g., [7,41]), as well as the density values obtained from rock samples in this work (Supporting Information, Table S5).…”

“…Dense magmas can be trapped inside lower-density crustal areas depending on buoyancy levels and changes in rheological properties traversed during ascent. Low-density zones are usually explained by the existence of magma reservoirs under volcanic calderas [3,73], whereas high-density bodies under volcanic areas are usually related to zones where basaltic magma intrusions were intense in the past [7,74,75].…”

“…Histogram of the final residuals of the covariance analysis); Section S5: Energy spectrum analysis of the gravity data (Figure S4: Logarithm of the power spectrum of the Bouguer anomaly); Section S6: Gravitational effect of the new topography on the gravity data (Table S5: Bulk and grain density values at Cumbre Vieja; Figure S5: Gravity effect induced by the new topography); Section S7: Gravity Inversion model (Figure S6: Histogram of the fit between the local gravity data and the calculated gravity from the inversion model); Section S8: Estimation of the width of the feeder dyke and related sills from aspect ratio analysis (Figure S7: Location of the VT events); Section S9: Model of the magma plumbing system for the 2021 eruption of Cumbre Vieja volcano (Figure S8: Geometric characteristics of the simple magma plumbing system model; Figure S9: Conceptual model of the inferred magma system). References [7,12,16,19,30,[39][40][41]44,45,47,49,50,56,57,75,[84][85][86][87][88][89][90][91][92][93][94] are cited in the Supplementary Materials.…”

“…Potential field methods have been widely used to study volcanic islands to obtain 3D models of the inner volcanic systems to image magma intrusion beneath the surface. This allows us to delineate the main volcanic structures and constrain the locations of dense intrusive basaltic and ultramafic bodies, as well as geothermal areas, related to the heat flow along the plumbing system (e.g., [3][4][5][6][7][8][9][10]).…”

Implicit Seismic Full Waveform Inversion With Deep Neural Representation

“…However, in existing joint inversion approaches, parameters' relationships in different physical domains are usually depicted by empirical formulas or coupled generalization functions. With the framework of IFWI, we can construct a single neural network to represent physical parameters of interest in multiple domains, the multi‐physics joint inversion can be performed by optimizing this single DNR with complementary constraints from different kinds of geophysical measurements, such as seismic, well‐log(Jeong et al., 2020), gravity (Montesinos et al., 2022), electromagnetic (Blanco‐Montenegro et al., 2008; Yao et al., 2022), ground penetrating radar(Meles et al., 2011), and remote sensing (Sun, Wauthier, et al., 2020). By analyzing this single DNR, further insight into the intrinsic relationships between parameters in different domains may be discovered.…”