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The processing of potential field datasets requires many steps; one of them is the inverse modeling of potential field data. Using a measurement dataset, the purpose is to evaluate the physical and geometric properties of an unidentified model in the subsurface. Because of the ill-posedness of the inverse problem, the determination of an acceptable solution requires the imposition of a regularization term to stabilize the inversion process. We also need a regularization parameter that determines the comparative weights of the stabilization and data fit terms. This work offers an evaluation of automated strategies for the estimation of the regularization parameter for underdetermined linear inverse problems. We look at the methods of generalized cross validation, active constraint balancing (ACB), the discrepancy principle, and the unbiased predictive risk estimator. It has been shown that the ACB technique is superior by applying the algorithms to both synthetic data and field data, which produces density models that are representative of real structures and demonstrate the method's supremacy. Data acquired over the chromite deposit in Camaguey, Cuba, are utilized to corroborate the procedures for the inversion of experimental data. The findings gathered from the three-dimensional inversion of gravity data from this region demonstrate that the ACB approach gives appropriate estimations of anomalous density structures and depth resolution inside the subsurface. [ABSTRACT FROM AUTHOR]

Pertamina Hulu Energi discovered a new source of oil and gas reserves in Tambelang, Bekasi Regency, which was named East Pondok Aren (EPN-001). These oil and gas reserves have great potential based on the oil flow test in the second Drill Stem Test. The drill data needs to be correlated with other supporting data such as geophysical methods to ensure the accuracy. Therefore, this research was conducted to examine the subsurface structure of oil and gas reserves in EPN-001 through 2D inversion modeling using the gravity method. Gravity satellite data was taken via the GGMPlus website with a total of 23,898 data in the form of gravity disturbance, geoid, and topography, so only terrain and bouguer corrections were carried out to get the Complete Bouguer Anomaly value. The research results show low anomaly values from -0.4463 – (-0.0339) mGal which are thought to be associated with alluvium lithology and the Lower Cibulakan Formation. In the 2D model, it can be seen that the drilling point in the Lower Cibulakan Formation layer has a density contrast value of -0.002 – 0.001 g/cc at a depth of 2590 m, and is known to be a reservoir that stores hydrocarbon reserves in the research area.

Abstract We present a new filtering method for potential fields, based on modelling the fields in terms of very compact solutions, i.e., the sources are expected to occupy the smallest allowable volume in the source domain. The selected solutions, which we call “Extremely Compact Sources” (ECS) form a sort of atomized model, which still satisfies the non-unique inverse problem of gravity and magnetic fields. The ECS model is not only characterized by sparsity, but also by large values of the physical property (density or magnetic susceptibility). The sparse nature of the model allows for the definition of a highly localized filter, which can be obtained by simply specifying the atoms to be selected in a given area. This feature allows managing tasks normally impossible with traditional filters, such as the separation of interfering anomalies having a similar wavenumber content. In addition, the procedure can perform a very effective regional/residual separation. We demonstrate the method on synthetic cases and apply it in the real case of gravity data of Campi Flegrei volcanic area (Italy), where we use the ECS filtering to isolate the gravity effect of the Mount Olibano dome.

The Maritaing region in East Nusa Tenggara Province, Indonesia, offers significant geothermal potential for direct power generation and use. This study aims to re-evaluate the geothermal system model in the Maritaing geothermal field using high-resolution gravity inversion. High-resolution gravimetric data were collected using satellite observations and data processing techniques, allowing a better understanding of the subsurface geological structure and density distribution of rocks associated with the geothermal reservoir. This study applied data corrections to acquire complete Bouguer anomalies, gravity gradient analysis to detect geological structures, and inversion modeling of residual gravity anomalies to predict subsurface rock density distribution. The complete Bouguer anomalies exhibit positive values ranging from 70 to 140 mGal and have a northwest–southeast orientation, indicating the existence of andesitic lava and pyroclastic volcanic rocks. Gravity gradient analysis determines the geological structures, such as faults or fractures, used as conduits to create Kura Hot Springs. The model provides useful insights into geological occurrences, such as faults, rock layers, and reservoir accumulations that support geothermal fluids. Based on the present study’s findings, we provide recommendations for developing and managing geothermal resources in the Maritaing area. [ABSTRACT FROM AUTHOR]

Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area. [ABSTRACT FROM AUTHOR]

The NW‐SE oriented Middle Niger Basin, which is a Campano‐Maastrichtian inland basin has been the subject of debate among geoscientists regarding its origin and development. This paper aims to unravel the basin's origin and evolution by using high‐resolution magnetic, gravity, and topographical data. The integration of aeromagnetic and gravity data provides a means to assess the influence of igneous intrusions during the basin's evolution. The study presents a new perspective on the origin of the basin, suggesting a rift origin where orthogonal extension played a crucial role in its evolution. The research also investigates the relationship between intrusive rocks and the formation of the basin, finding these rocks are solely located within the basement. The magnetic and gravity maps reveal anomalies associated with intrusive structures within the Precambrian crystalline basement. This is a common feature shared with rifted basins on a global scale. Plain Language Summary: The Middle Niger Basin, which spans about 350 km from Dekina in the SE to Shegwa in the NW, is unique among Nigerian basins due to the absence of a universally accepted description of its evolution within the geoscientific community. Magnetic data analysis reveals circular and elliptical anomalies that correspond to intrusive rocks. These anomalies are further enhanced by 2‐D forward modeling and gravity data inversion. Additionally, the SRTM data identifies lineaments that border the rifted basin, supporting the interpretations from the magnetic and gravity data. Our study suggests that fractures facilitated igneous intrusions, shaping the basin's evolution and providing new insights into its tectonic history. The new findings from this research provide valuable insights into the evolution of the Middle Niger Basin and may serve as a useful guide for future exploration initiatives. Key Points: The axis of the Middle Niger Basin is marked by structural discontinuitiesIntrusions in the basement of the basin were constrained by gravity inversionEvolution of the Middle Niger Basin is primarily controlled by crustal extension [ABSTRACT FROM AUTHOR]

The Bakreswar geothermal province represents a medium enthalpy geothermal system with its Bakreswar and Tantloie hot springs. It lies within the Chotanagpur Granite Gneissic Complex in the eastern part of the Indian Peninsula. The province has a high heat flow and a high geothermal gradient of 90°C/km. Magnetotelluric data from 95 sites in a frequency range of 10 kHz–10 Hz were acquired over the Bakreswar geothermal province to obtain an electrical conductivity model and map the geothermal reservoir with its fluid pathways and related geological structures. Subsurface conductivity models obtained from three‐dimensional inversions of the Magnetotelluric data exhibit several prominent anomalies, which are supplemented by gravity results. The conductivity model maps three features which act as a conduit (a) a northwest–southeast trending feature, (b) an east–west trending feature to the south of the northwest–southeast trending feature (which lies 1 km north of the Oil and Natural Gas Corporation fault marked by previous studies) and (c) shallow conducting features close to Bakreswar hot spring. The northwest–southeast trending feature coincides with the boundary of the high‐density intrusive block. This northwest–southeast trending feature provides the pathway for the meteoric water to reach a maximum depth of 2.7 km, where it gets heated by interacting with deep‐seated structures and then it rises towards the surface. The radiogenic process occurring within the granites of Chotanagpur Granite Gneissic Complex provides the heat responsible for heating the meteoric water. The northwest–southeast and east–west trending features are responsible for the transport of meteoric water to deeper depths and then towards the shallow regions of the Earth. The near surface features close to the Bakreswar hot spring are responsible for carrying the water further towards the hot spring. The resistivity of these structures plotted as a function of salinity and temperatures for saline crustal fluids suggests the involvement of meteoric water. Further, applying Archie's law to this resistivity suggests that the conduit path has a porosity greater than 10%. This study successfully maps the anomalous structures which might foster the migration of geothermal fluid in Bakreswar geothermal province. [ABSTRACT FROM AUTHOR]

Many geological features of scientific and/or economic interest have structural orientations that are neither horizontal nor vertical. Being able to recover such dipping structures from geophysical inversions is, therefore, important. Different regularization strategies have been proposed to help recover dipping structures. One notable example is total variation. However, there seems to be a lack of understanding within the geophysical community regarding why total variation regularization allows dipping structures to be recovered, whereas L1 norm regularization does not. In this paper, we compare these two regularization strategies from an optimization point of view using two simple block models. We also perform three‐dimensional inversions using a synthetic example and a field data example involving gravity gradient data to demonstrate the resolving power of total variation in potential field data inversion. [ABSTRACT FROM AUTHOR]

The field of geophysics faces the daunting task of monitoring complex reservoir dynamics and imaging carbon dioxide storage up to several decades into the future. This presents numerous challenges, including sensitivity to parameter changes, resolution of obtained results and the cost of long‐term deployment. To effectively store CO2 subsurface, it is necessary to monitor and account for the injected CO2. The gravity method provides several advantages for CO2 monitoring, as changes in fluid saturation correspond directly and uniquely to observed density changes. Three‐axis borehole gravity has demonstrated significant promise as a next‐generation tool for reliably monitoring reservoir dynamics across a range of depths and sizes. However, the gravity inverse problem is highly ill‐posed, necessitating regularization that incorporates prior knowledge. To address this issue, we propose using a feed‐forward neural network, a machine learning method, to invert time‐lapse three‐axis borehole gravity data and monitor CO2 movement within a reservoir. By training the neural network on models that analyse changes in density and corresponding gravity responses resulting from perturbations made to the reservoir model, we can create scenarios that train the algorithm to identify unexpected CO2 migration in addition to the normal movement of CO2. Our method is demonstrated using reservoir models for the Johansen formation in offshore Norway. We convert reservoir saturation models into density changes and generate their corresponding three‐axis gravity data in a set of boreholes. Our results show that the developed machine learning inversion algorithm has high reliability and resolution for imaging density change associated with CO2 plumes, as demonstrated in the Johansen reservoir models utilized by the simulator. We also investigate machine learning inversion using regularization parameters and show that it is robust, with a strong tolerance for higher levels of noise. Our study demonstrates that the developed machine learning algorithm is a powerful tool for inverting three‐axis borehole gravity data and monitoring the migration and long‐term storage of injected CO2. [ABSTRACT FROM AUTHOR]

A quantum‐enhanced implementation of the binary inversion method for gravity data acquisition is discussed. The subsurface structure of a single density anomaly with an assigned density contrast is calculated by using a D‐Wave adiabatic quantum computer. In particular, an iterative heuristic based on quantum annealing that recovers a sharp shape of the subsurface anomaly is developed. Such a task is accomplished by collecting partial images obtained by quantum annealing processes for optimal Lagrange penalty coefficients. The results are compared with those obtained according to the same cost function minimized via genetic algorithms by conventional hardware on a realistic 2D dataset. The outcomes of this work are promising as the reconstructed model is obtained in tenths of iterations instead of the hundreds required in conventional methods. Moreover, for the part of the computation that resides in the quantum processing unit, the computational cost of the single quantum annealing descent is constant with respect to the number of degrees of freedom of the subsurface grid. The implemented method is likely to reveal its full potential on forthcoming quantum annealing devices, outperforming existing techniques. [ABSTRACT FROM AUTHOR]

The Princess Elizabeth Land landscape in East Antarctica was shaped by a complex process, involving the supercontinent's breakup and convergence cycle. However, the lack of geological knowledge about the subglacial bedrock has made it challenging to understand this process. Our study aimed to investigate the structural characteristics of the subglacial bedrock in the Mount Brown region, utilizing airborne geophysical data collected from the China Antarctic Scientific Expedition in 2015–2017. We reconstructed bedrock density contrast and magnetic susceptibility models by leveraging Tikhonov regularized gravity and magnetic inversions. The deep bedrock in the inland direction exhibited different physical properties, indicating the presence of distinct basement sources. The east–west discontinuity of bedrock changed in the inland areas, suggesting the possibility of large fault structures or amalgamation belts. We also identified several normal faults in the western sedimentary basin, intersected by the southwest section of these survey lines. Furthermore, lithologic separators and sinistral strike-slip faults may exist in the northeast section, demarcating the boundary between Princess Elizabeth Land and Knox Valley. Our study provides new insights into the subglacial geological structure in this region, highlighting the violent impact of the I-A-A-S (Indo-Australo-Antarctic Suture) on the subglacial basement composition. Additionally, by identifying and describing different bedrock types, our study redefines the potential contribution of this region to the paleocontinent splicing process and East Antarctic basement remodeling. [ABSTRACT FROM AUTHOR]

Geophysical inversions give non-uniqueness solutions and unless constrained by appropriate initial values and geological constraints can give unrealistic results. One of the critical constraints can be the physical property values of different lithologies. We have compiled a density and magnetic susceptibility database consisting of thousands of measurements collated from different organisations and/or projects across the Abitibi greenstone belt. Statistical tools (histograms, quantile-quantile probability plots and boxplots) are applied to characterise systematically major and minor lithologies. We observed that the magnetic susceptibility frequently has a bimodal distribution, while density is typically unimodal. Our results are summarized in a table that includes the representative mean (or median) and a range of acceptable values. These values can be used to better understand the regional geology, but in this paper, we used the tabulated properties in a geophysical/petrophysical inversion of gravity data from the Chicobi area in the Abitibi subprovince to show the level of improvements that the petrophysical constraints can add to an unconstrained model. When our density database is used to seed the initial guess in a gravity inversion, an anomalous zone becomes apparent that was less evident on an unconstrained inversion. [ABSTRACT FROM AUTHOR]

The debate about the conceptual model of mud volcanoes functioning is still alive in the literature. A large part of the literature focuses on the characterization of the deep reservoir where expelled fluids are expected to originate. Another part of literature is focused on the study of the shallow system of mud volcanoes, which could influence the short‐term variations in mud volcanoes activity. We present and analyze a new data set of micro‐gravimetric data to study the area of the Nirano Salse, Italy. Unlike what is commonly assumed for the study area, our results suggest that the geomorphology of the Nirano Salse is not related to a caldera collapse above a shallow mud chamber, but to the surface expression of slip distribution of a fault termination along which the fluids ascended to the surface. We believe that gravimetric data can significantly improve the study of hydrocarbon seeps and mud volcanism. Plain Language Summary: Mud volcanoes are broadly distributed throughout the globe, both on‐and off‐shore. They are formed by the episodic eruption of mud and rock fragments. The eruptions are mainly driven by of gravitative instability, due to the overall low density of clay on surrounding soil types and fluid overpressures. To date, a physical conceptual model of the area of Nirano Salse (Northern Italy), ascribes the mud eruptions to the presence of over‐pressurized fluids that are expelled from a main deep reservoir. The latter is put into communication with the surface due to the episodical reactivation of pre‐existing faults or pipes. The debate about this conceptual model is still open. To improve it, a new data set of gravimetric data was acquired during 2022. Our results suggest that the morphology of the study area is due to the presence of an almost vertical fault, which acts as a preferential route for the ascent of deep fluids. Key Points: A microgravimetric survey coupled with laboratory density measurements allows to image the shallow to medium subsurface below mud volcanoesGravimetric data allow to distinguish between morphologies due to caldera collapse and the surface expression of slip along a faultFor the first time the attenuation volume caused by mud intrusion is imaged by gravimetry along a fault [ABSTRACT FROM AUTHOR]

Critical minerals are an integral part of the energy transition because of their important and, sometimes irreplaceable, uses in solar panels, wind turbines, electrical vehicles, etc. Mapping critical mineral resources is, therefore, essential to achieving the net‐zero emission goal by 2050. We present a case study on using airborne geophysical data, borehole and physical property measurements to characterize the Elk Creek Carbonatite complex located in the southeast of Nebraska, USA. It hosts the largest known niobium deposit in the United States and contains a high level of rare earth element mineralization. Our goal is to develop a better understanding of the three‐dimensional structures and composition of the Elk Creek Carbonatite complex as well as its critical mineral resource potential. We performed three‐dimensional joint inversion of the airborne gravity gradiometry and magnetic data to produce structurally similar density and susceptibility models. We carried out geology differentiation, a process of classifying recovered physical property values into distinct units, and obtained a three‐dimensional quasi‐geology model for the Elk Creek Carbonatite complex. We identified 12 geologic units, each of which is characterized by a distinct range of physical property values. Our quasi‐geology model, especially Units 9 and 11, shows a remarkable consistency with the borehole assay measurements. Unit 9 is spatially coincident with the known niobium ore zone. Unit 11 represents a significant volume of highly dense and magnetic materials below the deepest boreholes. These materials are likely associated with unexplored niobium mineralization. Our study demonstrates the added value of three‐dimensional geophysical joint inversions and geology differentiation in the context of critical mineral exploration under a thick sedimentary overburden. [ABSTRACT FROM AUTHOR]

Because of the limitations of manipulating single geophysical data sets to interpret subsurface anomalies for many cases, it is required to combine geophysical data to decrease the ambiguity and non-uniqueness of the interpretation. Integration interpretation of two different geophysical data sets is one of the most common ways to integrate geophysical data and in this paper, we want to utilize the combination of gravity and magnetic data for the Golgohar mine in Iran. This mining case is located in the Sanandaj-Sirjan zone in the province of Kerman. Gravity and magnetic data are interpreted using a MATLAB code written based on the damped weighted minimum length solution for which the model weighting is the product of the multiplying of compactness and depth weighting constraints. At first, the inversion algorithm is applied to the synthetic case to investigate its reliability for practical application on real data. Reconstructed models from the noise-contaminated synthetic data are suggestive of the productivity of the inversion algorithm. Ultimately, the algorithm is applied for the interpretation of the real data and the inversion results of both data sets show a high correlation between the magnetite anomaly position horizontally and vertically. The results represent an anomaly with the depth ranging approximately from 25 to 130 m with the horizontal extension of about 120 m from 280 to 400 m relative to the start of the interesting profile.

Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area.

The Singhbhum Craton, Singhbhum Mobile Belt along its northern, eastern, and western edges, and Chotanagpur Gneissic Complex farther north are all parts of the Precambrian eastern Indian shield. Modern isotope dates and associated geological evidence suggest that these crustal units may be one cratonic block that developed sequentially between 3.55 and 1.00 Ga. The region has always been the focus of numerous geoscientific studies due to its complex evolutionary history and abundant mineralisation. We used the terrestrial gravity data from the Gravity Map Series of India and the EGM2008 global gravity dataset in the Bay of Bengal to model the 3D Moho geometry of the eastern Indian shield and the adjoining Bay of Bengal by inverting the gravity data. The Bouguer gravity data were filtered at several levels before applying the Parker–Oldenburg iterative inversion procedure. The Moho depth measurement is then computed by presuming a constant density contrast. The effects of sediments were eliminated from gravity data by collecting thickness and density details of the sediment from a worldwide sedimentary thickness map CRUST1.0 and applying a correction comparable to the Bouguer correction that uses the density difference of 0.24 g/cm3. Spectral analysis is used to fix a reference depth level and the low-frequency range associated with Moho deflection in the Bouguer anomaly filtered for sedimentary overburden. We subsequently executed the gravity inversion of a basic two-layer structure having a constant density difference of 0.40 g/cm3 across the Moho fixed at an average depth of 35 km. The gravity inversion analysis shows that the Moho depth within the Bay of Bengal is between 18 and 24 km. In the continent, the Moho depth varies from 34 km near the coastline to 38 km towards the Singhbhum Craton and Chhotanagpur Gneiss Complex. In the northern portion of the region, the Moho depth increases to over 40 km underneath the convergence of the Mahanadi–Damodar Gondwana basins and the Ganga foreland basin. [ABSTRACT FROM AUTHOR]

As geophysical parameters are not always functionally related, treating multiple geophysical data sets to have a realistic geological model is not straightforward. An effective strategy to derive a geological interpretation is a combination of several geophysical methods with geological and well observations, each with its advantages and limitations. Gravity and magnetic methods are encouraging tools to investigate salt domes due to enough density and susceptibility contrasts between salt minerals and the background sedimentary rocks. In this paper, the dome‐shaped salt unit in the Qarah‐Aghaje area in Zanjan, located in the northwestern part of Iran, is investigated through the integration of 3D inversion models obtained from gravity and magnetic data and geological and well information. The 3D inversion of both data sets is made using a weighted damped minimum length solution for which model weighting is constructed from multiplying depth weighting and compactness constraints. At first, a synthetic case with a high degree of similarity to the real case is considered to evaluate the efficiency of the adopted inverse algorithm. Then, the inversion algorithm is implemented on the collected gravity and magnetic methods, and interpretation is made utilizing 3D obtained inverse models and geologic and well data. The result shows a dome‐shaped potash‐bearing salt unit that starts from a depth of 12 m and continues to the depth of 200 m. The drilled well in the centre of the main source of the salt (Well 3) demonstrates a depth range of 11–115 m. [ABSTRACT FROM AUTHOR]

Potential field geophysical measurements were conducted in the west of Kifl region in central Iraq to image a plausible oil-trapping reservoir. Ground-based magnetometry and gravimetry surveys were conducted to investigate this region by covering an area of 16 24 km by designing a regular grid spacing of 250 m. After preprocessing potential field data, different filters were utilized to separate the residuals from the regional anomalies. The complicated tectonic setting of the studied area was imaged by recognition of the fault system through simulation of the magnetic and gravity anomalies, which facilitates the configuration display of the oil-trapping mechanism. The geometry of a fault system was derived from parametric inversion of gravity data. The magnetic anomalies were extended with the trends of NS, NW, and NE and reached a maximum value of 55 nT. However, the gravity anomalies appeared with the same extensions and values ranging from -3.3 to 1.5 mGal. The intense magnetic susceptibility amount of the reservoir rocks is arising from chemical processes and iron-oxide ion replacements, accompanied by the migration and accumulation of hydrocarbon. Incorporating the results from the Euler’s depth estimation, parametric data modeling along with logging data assisted simultaneous modeling of the magnetic and gravity data. The 2D geological model of the subsurface layers at the Kifl area presents a graben-horst fault system within a thick sequence of sediment. Geological characteristics extracted from geophysical data modeling provided insightful information on the nature and essence of the hydrocarbon reservoirs in the Kifl area. It has formed through tectonic deformation and tension over the Arabian plate during the Permian – Paleocene cycle. Hence, it can be concluded that the aforementioned fault system has divided the hydrocarbon reservoirs.

Because of the limitations of manipulating single geophysical data sets to interpret subsurface anomalies for many cases, it is required to combine geophysical data to decrease the ambiguity and non-uniqueness of the interpretation. Integration interpretation of two different geophysical data sets is one of the most common ways to integrate geophysical data and in this paper, we want to utilize the combination of gravity and magnetic data for the Golgohar mine in Iran. This mining case is located in the Sanandaj-Sirjan zone in the province of Kerman. Gravity and magnetic data are interpreted using a MATLAB code written based on the damped weighted minimum length solution for which the model weighting is the product of the multiplying of compactness and depth weighting constraints. At first, the inversion algorithm is applied to the synthetic case to investigate its reliability for practical application on real data. Reconstructed models from the noisecontaminated synthetic data are suggestive of the productivity of the inversion algorithm. Ultimately, the algorithm is applied for the interpretation of the real data and the inversion results of both data sets show a high correlation between the magnetite anomaly position horizontally and vertically. The results represent an anomaly with the depth ranging approximately from 25 to 130 m with the horizontal extension of about 120 m from 280 to 400 m relative to the start of the interesting profile. [ABSTRACT FROM AUTHOR]

The use of potential field methods for geophysical exploration purposes is nowadays quite common: these techniques consent to retrieve geological knowledge over extended regions and can give complementary information where other invasive or expensive techniques, such as seismic acquisitions, fail (e.g., in the recovery of geometries of geological horizons beneath a thick salt layer). Recent dedicated satellite gravity and magnetic missions, such as GRACE, GOCE and SWARM together with the exploitation of offshore satellite altimetry and airborne/shipborne surveys, have paved the way to the realization of a variety of global models, characterized by spatial resolutions of about 4 km (both for gravity anomaly and lithosphere magnetic anomalies) and high accuracy (about 3-5$3\text{-}5$ mGal and 20 nT). These models are a valuable source of information to study the geological evolution and characterization of the lithosphere structure, especially at a regional scale. In the present work, some preliminary technical aspects related to the use of these models to perform three‐dimensional inversion are discussed, thus defining an empirical but rigorous procedure to set up gravity and magnetic inversion. In particular, we address the questions whether the classical planar approximation is acceptable for regional inversions or if a spherical one is required. We also provide guidance for choosing the best gravity functional (e.g., gravity anomalies or second radial derivative of the anomalous potential) and the optimal sizing of the three‐dimensional volume area to be modelled depending on the specific target investigated. The application of the proposed methods to the Mediterranean case study is also presented. [ABSTRACT FROM AUTHOR]

The inversion of geophysical data is a classical ill‐posed problem that is complicated by considerable uncertainty and ambiguity in the resulting inverse models. One way to reduce this uncertainty is based on the cooperative inversion of multiphysics data. In most cases, the information provided by different geophysical data is mutually complementary, making it natural to consider a cooperative (joint) inversion of different geophysical data to a shared earth model. Many existing joint inversion methods are based on the known relationships between the different physical properties of the rocks. This paper introduces a new approach to cooperative geophysical inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different models' parameters. Our approach is based on a novel joint minimum entropy stabilizer, which forces the simplest multiphysics solution that fits the multimodal data. This novel stabilizer characterizes the degree of joint disorder or uncertainty in the distribution of the different model parameters. By minimizing this stabilizing functional in the framework of the regularized inversion, we produce a consistent image of the same geological structure expressed in different geophysical data. We implement a joint minimum entropy stabilizer in the context of re‐weighted regularized conjugate gradient inversion. The paper demonstrates the developed method using a synthetic model study and by joint inversion of airborne gravity and magnetic data collected in the McFaulds Lake area of Ontario, Canada. [ABSTRACT FROM AUTHOR]

The Vitória-Trindade Ridge, situated in the central South Atlantic Ocean, is the longest aseismic ridge. The ridge comprises alkaline volcanic formations, including seamounts such as Davis, Colúmbia, Congress banks, Jaseur, Montague, and Vitória. The VTR resulted from a mantle plume, making it structurally complex. A study was conducted to investigate the structure of the VTR using gravity anomalies and bathymetric data. The study revealed the 3D structure of seamounts in terms of their density, depth, and volume. The results showed that the volume of seamounts with depth of VTR ranges from 10 to 14 km. Additionally, the uneven distribution of density contrast indicates the complex structure of the VTR region. • 3D density structure of the Vitória-Trindade Ridge, gravity anomalies. • The region has unique features like potential magma chamber indications in central elevations. • Davis Bank emerges as the deepest, while Victoria Seamount exhibits the highest volume, offering valuable insights into the ridge's varied topography. [ABSTRACT FROM AUTHOR]

We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs.

Potential field geophysical measurements were conducted in the west of Kifl region in central Iraq to image a plausible oil-trapping reservoir. Ground-based magnetometry and gravimetry surveys were conducted to investigate this region by covering an area of 16 × 24 km by designing a regular grid spacing of 250 m. After preprocessing potential field data, different filters were utilized to separate the residuals from the regional anomalies. The complicated tectonic setting of the studied area was imaged by recognition of the fault system through simulation of the magnetic and gravity anomalies, which facilitates the configuration display of the oil-trapping mechanism. The geometry of a fault system was derived from parametric inversion of gravity data. The magnetic anomalies were extended with the trends of NS, NW, and NE and reached a maximum value of 55 nT. However, the gravity anomalies appeared with the same extensions and values ranging from -3.3 to 1.5 mGal. The intense magnetic susceptibility amount of the reservoir rocks is arising from chemical processes and iron-oxide ion replacements, accompanied by the migration and accumulation of hydrocarbon. Incorporating the results from the Euler's depth estimation, parametric data modeling along with logging data assisted simultaneous modeling of the magnetic and gravity data. The 2D geological model of the subsurface layers at the Kifl area presents a graben-horst fault system within a thick sequence of sediment. Geological characteristics extracted from geophysical data modeling provided insightful information on the nature and essence of the hydrocarbon reservoirs in the Kifl area. It has formed through tectonic deformation and tension over the Arabian plate during the Permian - Paleocene cycle. Hence, it can be concluded that the aforementioned fault system has divided the hydrocarbon reservoirs. [ABSTRACT FROM AUTHOR]

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In Mexico, agriculture in semi-arid regions is highly dependent on groundwater resources, where most of the aquifers' characterization is a pending task. In particular, the depth to the basement is unknown for most of the Mexican territory. Hence, the development and performance of new techniques for the basement relief estimation is imperative for further hydrogeological studies. In this paper, we present a depth to the basement estimation using non-linear gravimetric inversion employing satellite data. Gravity forward modeling was implemented using both gravitational attraction due to juxtapositioned blocks and gravimetric non-linear inversion using conjugate gradient least squares to minimize the objective function in terms of a depth model. All of this took place under the sparse system framework. We present a synthetic result using the SEG-Bishop depth model taken for calibration purposes. Then, we recollected gravity data from The Satellite Geodesy group from SCRIPPS for the depth to the basement estimation of an unconfined aquifer in the northern-central semi-arid region of Zacatecas, Mexico. Both synthetic and satellite data were recovered, consistent depth models for both cases were presented, and a comparison with conventional gravimetric linear inversion for density estimation was performed. [ABSTRACT FROM AUTHOR]

In this paper, an inversion algorithm for gravity data is presented. The algorithm exploits a model weighting matrix derived from multiplication of compactness and the modified depth weighting, with its exponent being dependent on the source type. Regularisation parameter and the exponent of depth weighting are the critical inverse parameters: the first is adopted according to the maximum value of the kernel matrice (MaxKer) and the second takes the value of the structural index associated with the source type of interest. The productivity of the proposed method is examined through three synthetic and two real data sets. According to the derived results from synthetic examples, the optimised value found for the regularisation parameter to successfully invert the perfect and noisy data sets are 10-7xMaxKer and 10-1xMaxKer, respectively. Ultimately, we manipulate the inversion algorithm on the two real data sets, from the: i) salt dome of Rogun in Tajikistan and ii) Safo manganese mine in Iran. The suggested value for the regularisation parameter in real cases is 0.05xMaxKer, which is close to the corresponding value to invert the noise contaminated data. Since source types for both cases were known from a priori information, 2 is attributed to the exponent of depth weighting. [ABSTRACT FROM AUTHOR]

We invert gravity and magnetic anomalies for basement relief at the Harrat Rahat Volcanic Field (HRVF) for the purpose of evaluating its geothermal energy prospects. HRVF is dominated by basaltic scoria cones and other volcanic rocks overlying the Proterozoic basement. The area considered for this study is located within the northern HRVF and consists mainly of alkali basalts with lesser amounts of benmoreite, mugearite, hawaiite, and trachyte. Our approach adopts a global optimization technique using Particle Swarm Optimization with automated parameter selection, and a two-dimensional gravity-magnetic (GM) forward modeling procedure. The results of the PSO-based approach indicate a depth to the basement at 0.10–624 m, with greater depths within the central region of a solitary anomalous density body in the HRVF. The obtained basement geometry is corroborated by the depth estimates obtained from other potential field inversion methods. The regions with higher prospects are mapped for a targeted future geothermal energy exploration at the HRVF, based on our inversion results. [ABSTRACT FROM AUTHOR]

We introduce a novel approach to three-dimensional gravity inversion in the presence of the sediment-basement interface with a strong density contrast. This approach makes it possible to incorporate the known information about the basement depth in the inversion. It also allows the user to determine the depth-to-basement in the initial inversion phase. One can then use this interface to constrain the final inversion phase. First, the inversion generates the depth-to-basement model based on the 3D Cauchy-type integral representation of the gravity field. Then, in the second phase, full 3D voxel-type inversion applies the depth-to-basement model determined in the first phase as an a priori constraint. We use this approach to the 3D inversion of the Bouguer gravity anomaly data observed in Utah, USA. The results of inversion generated a 3D density model of the top layers of the earth's crust, including unconsolidated sediments and the top of the crystalline basement. [ABSTRACT FROM AUTHOR]

The crustal structure beneath the United Arab Emirates (UAE) is still relatively unknown. Here, we use regional gravity data to constrain the subsurface density distribution and structure of the crust of the UAE by applying diverse gravity derivatives methods such as horizontal derivative (HDR), analytic signal (AS), and tilt angle (TA) to analyze the subsurface structure and perform three-dimensional (3D) gravity inversion for imaging crustal structure from the surface down to 35 km depth. The results are compared with known geological regional structures and the location of the petroleum fields.

The Central‐Eastern Mediterranean region is known to be a complex area due to the interaction of four tectonic plates, namely Arabia, Africa, Anatolia and Eurasia, and by the presence of an ancient oceanic crust in the Herodotus and Ionian Basins. The analysis of the available literature highlights that the distribution of the freely available geophysical data (i.e. seismic, gravity and magnetic observations) is quite disparate. In this framework, high‐resolution global gravity field models, such as XGM2019e, mainly based (offshore) on satellite data, can be profitably used as a uniform dataset to study, in a coherent way, large regions. In the current work, we exploit the XGM2019e model, together with a set of a priori information, derived mainly from geophysical data retrieved from the literature, to study the structure of the crust in the Central‐Eastern Mediterranean area. The study is organized in three different phases: in the first one, we enhanced the map of geological crustal provinces by means of an automatic Bayesian classification algorithm applied to the second radial derivatives of the gravitational potential. In the second phase, using as observation a grid of gravity anomalies, we applied a full three‐dimensional inversion procedure (always based on a Bayesian paradigm) to estimate the mass density variations and the geometries of the main geological units in the whole study area. Finally, in the third phase, we performed a refined three‐dimensional local inversion on the Cyprus area to improve the modelling of the shallowest layers. The main results of this study, carried out in the framework of the European Space Agency GIADA project, are freely available, upon request, at https://www.g‐red.eu/geophysics/. [ABSTRACT FROM AUTHOR]

In this paper, a modified version of strength Pareto evolutionary algorithm SPEA (II) is used as a multi-objective optimization method in gravity data modelling. In this method, a two-dimensional gravity inversion problem is solved by iteratively random creation of forward models. It is shown that it can be used as a fast and effective inversion tool in the depth modelling of two-dimensional layer problems with applications in depth-to-basements, geometry of bedrocks and sedimentary basins modelling cases. Owing to the direct use of the regularization term as a separate objective function, smooth models have a high chance of being selected as final solutions, which makes the results more acceptable and easier to interpret. The most important advantages of this method are that it works independently of the regularization coefficient; thus, there is no need to run the algorithm so many times to find a proper regularization parameter. Furthermore, there is no need to directly deal with inverse formulations, and last but not least, by using a multi-objective algorithm as a global optimization method, convergence to a stable solution does not depend on the initial model, the way classical inversion methods do. For testing the algorithm, a synthetic model is used for layer boundary modelling and to assess the stability of this algorithm, white Gaussian noise is added to the synthetic model. To evaluate the validity of this method, real data from the Recôncavo basin in Brazil is considered for processing and inversion, and the results are compared to the ones from previous studies. All computations have been done in the GNU Octave 5.1.0 environment. [ABSTRACT FROM AUTHOR]

In gravity field inversion we usually dealing with underdetermined problems and for obtaining realistic solutions can introduce a depth-weighting function to the inversion algorithm. We employ a linear inversion method for determining the underground density distribution of the gravity causative mass. The validation and accuracyof method is tested on two synthetic gravity anomaly from different models, while the data are noise- free and corrupted with noise. In this paper, We also invert the 2D gravity anomaly produced by a salt dome from the northwest of Iran. The salt domes in the region under investigation are a rich source of Potash. The inverted structure demonstrate on average a depth to top and bottom of 27 m and 65 m, respectively. For comparison, we also have simulated the salt dome using the nonlinear inverse modeling. The results are mostly similar.

The Blackfoot Reservoir volcanic field (BRVF), Idaho, USA, is a bimodal volcanic field that has hosted silicic eruptions during at least two episodes, as recently as 58 ka. Using newly collected ground and boat‐based gravity data, two large negative anomalies (−16 mGal) are modeled as shallow (<1 km) intrusions beneath a NE‐trending alignment of BRVF rhyolite domes and tuff rings. Given the trade‐off between density contrast and model volume, best‐fit gravity inversion models yield a total intrusion volume of 50−120 km3; a density contrast of −400 kg m−3 results in two intrusions, each ∼9 km ×4.5 km and about 0.5 km thick, with cumulative volume of 100 km3. A network of 340°−360° trending faults lies directly above and on the margins of the mapped gravity anomalies. Most of these faults have 5−10 m throw; one has throw up to ∼50 m. We suggest that the emplacement of shallow sill‐like intrusions produced this fault zone and also created a ENE‐trending fault set, indicating widespread ground deformation during intrusion emplacement. The intrusions and silicic domes are located 3−5 km E of a regional, 20 mGal step in gravity. We interpret this step in gravity as thickening of the Upper Precambrian to lowermost Cambrian quartzites in the Meade thrust sheet, part of the Idaho‐Wyoming Thrust Belt. Silicic volcanism in the BRVF is a classic example of volcanotectonic interaction, influenced by regional structure and creating widespread deformation. We suggest volcanic hazard assessments should consider the possibility of large‐volume silicic eruptions in the future. Plain Language Summary: On Earth, gravity anomalies occur where there are significant, subsurface, lateral density variations. We map two gravity anomalies located in the Blackfoot Reservoir volcanic field, Idaho, a site which has experienced explosive volcanic eruptions as recently as 58,000 years ago. Our numerical models of the gravity anomalies indicate that they are caused by two saucer‐shaped intrusions, magma bodies that likely fed eruptions at the surface and triggered fault displacement. Although these magma bodies have cooled, they have large volumes and the timing of the emplacements suggest that large‐volume explosive volcanic eruptions are possible in this volcanic field in the future. Key Points: Large‐amplitude gravity anomalies are mapped in a combined ground and boat‐based gravity survey in the Blackfoot Reservoir volcanic field, Idaho (BRVF), adjacent to young (1.5 Ma, 58 ka) topaz rhyolite domes and tuff rings within a Quaternary basaltic volcanic fieldBest‐fit 3D inversion of the gravity data, constrained by density contrast estimates and excess mass calculations, indicates the presence of two bodies with thick sill‐like shapes in the uppermost crust, with cumulative volume of ∼100 km3 and volume uncertainty in the range 50−120 km3Extensive volcanotectonic interaction is suggested by comparison of mapped gravity with fault distribution. The western edges of the gravity anomalies coincide with normal faults with vertical displacements that range from 5−10 m (maximum 50 m) [ABSTRACT FROM AUTHOR]

Gravity surveys in regional geophysical research can be used to estimate the depth of the sediment‐basement interface. In this study, we investigate a novel method using the convolutional neural network (CNN) for depth‐to‐basement inversion directly from gravity data. Based on the Random‐Midpoint‐Displacement method (RMD) and the features of the observed gravity data, we can generate a large set of realistic sediment‐basement interface models. This new method for model generation can significantly reduce the size of the training data sets which is usually considerably large to train a pervasive network. The application on synthetic models shows that the developed CNN inversion is able to capture the detailed features of the sediment‐basement interface for the complex geological model. However, so far, the training set obtained from the proposed method is still continuous and the CNN inversion still cannot effectively recover the models such as abrupt faults. We also successfully applied the developed method and workflow to a field study. The proposed approach opens a new window for estimating the physical contrast interfaces using potential field. Plain Language Summary: The sedimentary basin is related with the dynamic process of the earth and it plays an important role in natural resources exploration. As a result, it is of great importance to know the shape and depth of the basin. Generally, the sediments and the underlaid basement rocks have different densities. The depth of the basin varies at different places and such variation can cause the change of the gravity field (gravity anomaly) on the surface. Use the technique called inversion, we can transform the gravity anomaly to the depth of the basin. However, such transformation is non‐unique. In this study, we introduce the machine learning (ML) method to this field. Use tons of models and data, we train the computer to learn the relationship between the gravity anomaly and the basin depth. When the machine obtains such ability, it can automatically convert the gravity anomaly to the basin depth. This method is validated by several numerical models. In addition, we applied this method to the gravity anomaly collected at Big Bear Lake area, California. Using the ML method, we obtain a reliable basin model which fits well with the known geology and can explain the geodynamic process. Key Points: Machine learning based on the convolutional neural network is used to recover the depth‐to‐basement using gravity dataWe propose a novel model generation method for training the network to avoid adding smoothing constraints to the loss functionThe developed method can effectively recover the sediment‐basement interface and it has been successfully applied to field study [ABSTRACT FROM AUTHOR]

A Matlab‐based optimization algorithm is introduced for inverting fault structures from observed gravity anomalies. A convenient graphical user interface is also presented for incorporating the input parameters without any technical complexity to any users. The inversion code uses particle swarm optimization, and all control parameters are tuned initially for faster convergence. There is no requirement of prior choice of an initial model, that is the advantage of using global optimization. The optimization technique is versatile enough to handle any depth‐varying density distributions. The maximum number of iterations and stopping criterion is fixed initially for getting the best optimized solution. The inverted model's output in terms of fault structure, observed and inverted gravity anomalies and dip, and vertex location of fault plane can be viewed in the graphical user interface at the end of the optimization process. The optimization algorithm is applied to different synthetic models with fixed and depth‐varying density contrasts. All synthetic models are further contaminated with white Gaussian noise for sensitivity analysis, and detailed uncertainty appraisal was also performed for the reliability estimation. Finally, the optimization is implemented for fault structure inversion of the Aswaraopet boundary fault, India, and found that the optimized solution provides a good agreement with the previously published literature. Optimized results indicate that this novel optimization approach demonstrates a robust implementation of fault inversion for any depth‐varying density distributions. [ABSTRACT FROM AUTHOR]

A gravity survey was conducted at a salt mine in Nemocón, Cundinamarca, Colombia, located in the axial part of the Eastern Cordillera of the northern Andes. This part of the Eastern Cordillera is characterized by successions of synclines and anticlines sometimes containing salt bodies at their core, and the mine is located at the top of one of these anticlines called the Nemocón anticline. As is typical for salt and other mines in Colombia, very little data on the internal structure and geology are publicly available. The purpose of our study is to show how gravity data and modelling can be used to infer near‐surface properties above the mine in the absence of other sources of information. Relative gravity measurements were made over an irregular grid of 107 stations and tied to the Instituto Geográfico Augustin Codazi national absolute gravity network. The total and residual Bouguer anomalies as well as the analytic signal were calculated over the area of the salt body. Using grid data, we first determine the main inversion parameters (e.g., depth weighting) and model resolution using a checkerboard model with the addition of random noise. Then, applying 3D inversion modelling to the residual Bouguer anomaly, we find that the positive and negative anomalies correspond to few zones of positive density contrasts and two main zones of negative density contrasts. Some of the positive anomalies are located above the fault trace putting in contact the Arenisca Dura formation with the Conejo formation containing the salt body. The negative anomalies are not well correlated with the underground position of the salt mine, but some influence of the low‐density 'rute' material, consisting of black clays with shaly claystone parts covering the salt body might be important. This study showcases how 3D inverse modelling can provide important information on the near‐surface structure of a salt mine. [ABSTRACT FROM AUTHOR]

The gravity and magnetic data sets over a geothermal source are investigated to explore the geological features and geothermal system manifestations. The raw gravity data sets are firstly corrected for drift, latitude and free-air effects to obtain free-air anomalies. A new approach is utilized for Bouguer and terrain corrections. The model space, which is the mass from the reference surface to the ground surface, is parameterized by a very accurate and advanced meshing algorithm (octree and quadtree). The gravity effect of the model space is computed via numerical forward modelling and is considered as the Bouguer and terrain corrections. These corrections are subtracted from the free-air anomalies, which yields the complete Bouguer anomaly. The computed Bouguer anomalies are de-trended and transferred to residual anomalies by methods including polynomial fitting and two-stage methods. The residual gravity anomalies are inverted to obtain the subsurface density distribution. The density contrasts are estimated by minimizing the data and model objective functions through an efficient algorithm. The probable source of the geothermal system and other new gravity anomalies are successfully detected and inverted contrary to the results in previously published papers. The residual magnetic anomaly is also inverted and the obtained geometrical and physical parameters of the source body including minimum depth, shape and susceptibility are very close to the previous results. The new detected gravity anomaly and the main magnetic anomaly in the overlapped area of gravity and magnetic grids are located at almost the same place with almost the same depths, which confirms their probable common source. We developed the open source package (http://docs.simpeg.xyz/content/tutorials/03-gravity), which is accessible through SimPEG (Simulation and Parameter Estimation in Geophysics), for inverting the gravity and magnetic data sets. [ABSTRACT FROM AUTHOR]

Seismic reflection surveys are effective for imaging the crustal architecture of sedimentary basins including the top-of-basement, which is an important interface for understanding rift tectonics, structural controls imposed on basin fill, and basin system prospectivity. However, seismic data can be less effective where the basin is thick, logistics limit acquisition, or where vintage seismic are noisy. 3D inversion modelling of airborne gravity gradiometry data can be an effective complement or alternative. This study provides a logical, stepwise approach that tackles this problem by focussing on separating superimposed geophysical anomalies. We use the Otway Basin in southeast Australia as a 'laboratory' to demonstrate a systematic approach for understanding basin and basement geometry, using airborne gravity data in conjunction with other geoscientific datasets. The forward response of a starting model, built using only existing seismic interpretations, was a poor fit to the observed gravity data because gravity provides the net response of all rocks down into the mantle. Therefore, in areas where geology in the deep crust is complex - like the Otway Basin – the entire crust must be modelled to account for gravity signatures caused by basement geological heterogeneity, before the smaller gravity variations attributable to the sedimentary basin fill can be discriminated. This process targets anomalies in the residual gravity, speculates on their likely causes, builds a geological structure into the model to explain them, and then considers the next unexplained miss-match in the residual. Forward modelling and inversion results suggest gravity anomalies are caused not only by geological diversity in the basement, but also by Moho shape. Accounting for the gravity response of these deeper structures not only improves understanding of basement structures, but also improves the recovered geometry of the top-of-basement interface. Specifically, inversion results suggest the Portland Trough in the southwestern region is significantly deeper than previously thought. • Airborne gravity gradiometry data was used to resolve the 3D geological architecture of the Otway Basin in SE Australia. • A logical, stepwise approach focuses on separating superimposed gravity anomalies using 3D forward and inversion modelling. • Full crustal modelling was necessary to account for gravity signatures caused by the Moho, basement geology, and basin fill. • Inversion results suggest the Portland Trough in the western Otway Basin is significantly deeper than previously thought. [ABSTRACT FROM AUTHOR]

Global approaches for estimating geophysical model parameters have been proposed by several authors, including their application for gravity interpretation, which is currently limited to simple and fixed geometrical problems. This paper proposes implementation of Very Fast Simulated Annealing (VFSA) in two-dimensional gravity interpretation problems, which are still rarely addressed. The modeling domain was divided into smaller sub-domains and gravity anomaly calculation was carried out based on the Talwani formulation. To improve the uniqueness of the solution of under-determined problems, specific constraints were added in addition to the assumed known symmetry axes. The inversion of VFSA was tested on synthetic data generated by simple models and on previously published real data to evaluate the applicability of the proposed approach to the interpretation of field data.

In Mexico, agriculture in semi-arid regions is highly dependent on groundwater resources, where most of the aquifers’ characterization is a pending task. In particular, the depth to the basement is unknown for most of the Mexican territory. Hence, the development and performance of new techniques for the basement relief estimation is imperative for further hydrogeological studies. In this paper, we present a depth to the basement estimation using non-linear gravimetric inversion employing satellite data. Gravity forward modeling was implemented using both gravitational attraction due to juxtapositioned blocks and gravimetric non-linear inversion using conjugate gradient least squares to minimize the objective function in terms of a depth model. All of this took place under the sparse system framework. We present a synthetic result using the SEG-Bishop depth model taken for calibration purposes. Then, we recollected gravity data from The Satellite Geodesy group from SCRIPPS for the depth to the basement estimation of an unconfined aquifer in the northern-central semi-arid region of Zacatecas, Mexico. Both synthetic and satellite data were recovered, consistent depth models for both cases were presented, and a comparison with conventional gravimetric linear inversion for density estimation was performed.

The recovery of 3D volume of density from gravity field data is a key feature of geophysical and geological interpretations. Gravity field data inversion involves solving an underdetermined problem; therefore, large-scale data inversion is costly in time and memory consumption. Calculation efficiency is a primary concern for gravity field data inversion; therefore, multiple methods are considered and applied to increase the inversion efficiency. The solution for an inversion problem was formulated by incorporating constraints to obtain stable inversion results, and a new projected Barzilai-Borwein iterative algorithm was applied to accelerate convergence of the inversion method. To test the potential application of the projected Barzilai-Borwein iterative method, synthetic gravity data simulations and real data applications were carried out. Numerical performances and practical application indicate the fast convergence of projected Barzilai-Borwein iterative method and the increase in the calculated efficiency. [ABSTRACT FROM AUTHOR]

Long‐term monitoring of global mass transport within the Earth system improves our ability to mitigate natural hazards and better understand their relations to climate change. Satellite gravity is widely used to monitor surface mass variations for its unprecedented spatial and temporal coverage. However, the gravity data contain signals from visco‐elastic deformation in response to past ice sheet melting, preventing us from extracting signals of present‐day surface mass trend (PDMT) directly. Here we present a global inversion scheme that separates PDMT and visco‐elastic glacial isostatic adjustment (GIA) signatures by combining satellite gravimetry with satellite altimetry and ground observations. Our inversion provides global dual data coverage that enables a robust separation of PDMT and GIA spherical harmonic coefficients. It has the advantage of providing estimates of Earth's long wavelength deformation signatures and their uncertainties. Our GIA result, along with its uncertainty estimates, can be used in future GRACE processing to better assess the impact of GIA on surface mass change. Our GIA estimates include a rapid GIA uplift in the Southeast Alaska and the Amundsen Sea Embayment, due to the visco‐elastic response to recent glacial unloading. We estimate the average surface mass change rate from 2002–2010 to be −203 ± 3 GT·a−1 in Greenland, −126 ± 18 GT·a−1 in Antarctica and, −62 ± 5 GT·a−1 in Alaska. The GIA low degree spherical harmonic coefficients are sensitive to rheological properties in Earth's deep interior. Our low‐degree GIA estimates include geocenter motion and J˙2 which provide unique constraints to understand Earth's lower mantle and ice history. Plain Language Summary: Surface mass exchange between Earth's "spheres"—atmosphere, hydrosphere, cryosphere, biosphere, and pedosphere— are enormous. Monitoring surface mass change helps to understand climate change and mitigate hazardous effects such as extreme drought or flooding. Measurements of surface mass change are perturbed by subsurface processes, such as mantle flow underneath Earth's crust. Often, a model is used to correct the subsurface signals from observations, and likely to introduce un‐modeled process errors into the surface mass estimates. We use a mostly data‐driven method to extract present‐day surface mass change trends along with their error estimates. The results give an enhanced view of the surface mass processes and can help improving the accuracy of future surface mass estimations. Moreover, we get a more accurate picture of the subsurface processes, that are mainly caused by Earth's viscous response to past ice sheet melting. We find that Earth's crust bounces back more rapidly after glacier melting events than we assumed before, especially in places where the mantle viscosity is low. Key Points: Separation of present‐day mass transport and glacier isostatic adjustment signatures using multiple observation typesEstimation of geocenter motion due to past and present‐day surface mass changesEnhanced glacial isostatic adjustment signals detected in areas of low upper mantle viscosity and recent ice loss [ABSTRACT FROM AUTHOR]

The rhyolite‐producing Laguna del Maule volcanic field (LdMVF), Chile, has had numerous post‐glacial eruptions that produced large explosions and voluminous lava flows. During the Holocene ∼60 m of surface uplift is recorded by paleo‐shorelines of the fresh‐water Laguna del Maule, with an inflation source near the Barrancas volcanic complex. Rhyolites from the Barrancas complex erupted over ∼14 ka including some of the youngest (1.4 ± 0.6 ka) lava flows in the field. New gravity data collected on the Barrancas complex reveals a residual gravity low (−6 mGal, "Barrancas anomaly") that is distinct from the pronounced gravity low (−19 mGal; "Lake anomaly") associated with present‐day ground uplift to the northwest. Three‐dimensional inversion of the Barrancas anomaly indicates the presence of a magma body with a maximum density contrast with the host rock of −250 kg/m3 centered at a depth of ∼3 km below surface. Nearby Miocene high‐silica granites represent frozen remnants of highly evolved rhyolitic magma. Comparison of the densities measured from samples of these plutons with the geophysical model densities, and integration of thermodynamic modeling of silicic melt evolution, provide constraints on our interpretation. We propose a magma body, containing <30% melt phase and low volatile content, exists beneath Barrancas. The Barrancas and Lake gravity lows represent magma in different physical states, associated with past and present‐day storage beneath LdMVF. The gravity model mirrors geochemical observations which independently indicate that at least two distinct rhyolites were generated and stored as discrete magma bodies within the broader LdMVF. Plain Language Summary: The Laguna del Maule volcanic field (LdMVF) in Chile, has a long history of eruptions that produced large explosions and voluminous lava flows. Lava emitted during these eruptions comes from shallow sources of partially molten rock (magma) located a few kilometers below the surface. Magma pushing from below and into these shallow regions has caused the ground around the lake to rise over the last 10,000 years. To understand the causes of deformation and assess the likelihood of future eruptions, it is essential to know the location and present‐day state of magma within Earth's crust. Magma is typically less dense than the surrounding rock and these density variations cause small changes in the local gravity field, detectable with geophysical instruments. We collected a new set of gravity measurements at the Barrancas volcanic complex to determine if magma is present beneath the volcano that last erupted about 2000 years ago. Our data reveal a shallow magma body at depth beneath the Barrancas complex. This new magma body is separate from another magma body located in a different part of the LdMVF, which has important implications for the assessment of future volcanic hazards in the region. Key Points: At least two separate magma storage regions in different physical states exist beneath the Laguna del Maule volcanic fieldA −6 mGal gravity anomaly below the Barrancas complex is close to a postulated inflation source which produced ∼62 m of Holocene upliftComparing model densities to nearby plutons and seismic models indicates Barrancas magma is above solidus with a small melt proportion [ABSTRACT FROM AUTHOR]

We introduce a novel approach to three-dimensional gravity inversion in the presence of the sediment-basement interface with a strong density contrast. This approach makes it possible to incorporate the known information about the basement depth in the inversion. It also allows the user to determine the depth-to-basement in the initial inversion phase. One can then use this interface to constrain the final inversion phase. First, the inversion generates the depth-to-basement model based on the 3D Cauchy-type integral representation of the gravity field. Then, in the second phase, full 3D voxel-type inversion applies the depth-to-basement model determined in the first phase as an a priori constraint. We use this approach to the 3D inversion of the Bouguer gravity anomaly data observed in Utah, USA. The results of inversion generated a 3D density model of the top layers of the earth’s crust, including unconsolidated sediments and the top of the crystalline basement.

We invert gravity and magnetic anomalies for basement relief at the Harrat Rahat Volcanic Field (HRVF) for the purpose of evaluating its geothermal energy prospects. HRVF is dominated by basaltic scoria cones and other volcanic rocks overlying the Proterozoic basement. The area considered for this study is located within the northern HRVF and consists mainly of alkali basalts with lesser amounts of benmoreite, mugearite, hawaiite, and trachyte. Our approach adopts a global optimization technique using Particle Swarm Optimization with automated parameter selection, and a two-dimensional gravity-magnetic (GM) forward modeling procedure. The results of the PSO-based approach indicate a depth to the basement at 0.10–624 m, with greater depths within the central region of a solitary anomalous density body in the HRVF. The obtained basement geometry is corroborated by the depth estimates obtained from other potential field inversion methods. The regions with higher prospects are mapped for a targeted future geothermal energy exploration at the HRVF, based on our inversion results.

Compared with structured grids, unstructured grids are more flexible to model arbitrarily shaped structures. However, based on unstructured grids, gravity inversion results would be discontinuous and hollow because of cell volume and depth variations. To solve this problem, we first analyzed the gradient of objective function in gradient-based inversion methods, and a new gradient scheme of objective function is developed, which is a derivative with respect to weighted model parameters. The new gradient scheme can more effectively solve the problem with lacking depth resolution than the traditional inversions, and the improvement is not affected by the regularization parameters. Besides, an improved fuzzy c-means clustering combined with spatial constraints is developed to measure property distribution of inverted models in both spatial domain and parameter domain simultaneously. The new inversion method can yield a more internal continuous model, as it encourages cells and their adjacent cells to tend to the same property value. At last, the smooth constraint inversion, the focusing inversion, and the improved fuzzy c-means clustering inversion on unstructured grids are tested on synthetic and measured gravity data to compare and demonstrate the algorithms proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper reviews different approaches to the problem of finding the shape of the basement buried under sedimentary basins from gravity data and explores the applicability of a recently proposed method to a well-constrained real case, comparing the results obtained with the models computed using a variety of techniques. Many gravity inversion techniques to estimate the depth to basement based on rather different approaches have been proposed. As is well known, the interpretation of gravity data requires certain assumptions about the source, aimed at constraining the solution of an ambiguous problem. The different approaches imply different kinds of solutions, namely a density contrast distribution at depth, or a grid defining the depth to basement in the entire area of study or several single depth estimates. Each approach has its own advantages and weaknesses. In this context, special attention is given to the ITerative RESCaling method (ITRESC), which has been recently proposed. In this method, there is no need to assume a density function, which is estimated by a data-driven procedure and then used to generate a grid of the depth to basement. This technique is based on the depth–gravity relation plot, illustrating the link between the depth to basement, known at some control points (e.g., boreholes or interpretation of other geophysical data), and the values of the residual gravity anomaly. An important feature of the ITRESC method is that borehole control points are used globally rather than locally, providing constraints for all parts of the model. The main features of this innovative method are illustrated and evaluated by its application to the gravity anomalies of the Yucca Flat basin (Nevada). The results are compared with models obtained by previous gravity interpretations and by the processing of other geological and geophysical data. [ABSTRACT FROM AUTHOR]