The Lower Shire Karoo Basin architecture using 3-D gravity inversion modelling

Morphology of the basement and Hormuz salt distribution in offshore Abu Dhabi from constrained 3-D inversion of gravity and magnetic data

A new structural analysis is presented for the Browse Basin of the Australian North West Margin, integrating new observations based on a regional 2D seismic data-set and potential field data. Previously published plate reconstructions and gravity inversion modelling were used to understand the mega-regional context of this interpretation and propose a new history of basin evolution. Key basin-forming northeast to southwest structural elements were developed during Carboniferous to Permian rifting, inherited fabrics from relaxed Proterozoic fold belts. Long-lived highs formed during this time delineated the structure of the basin through later Mesozoic rifting. Rifting was accommodated initially by inheritance of large basin-bounding Paleozoic listric faults and then development of new planar faults in the basin. This led to the formation of both rotated syn-rift sediment wedges and tilted fault block geometries. Structures related to several phases of inversion have been mapped, including a previously little-documented Early Cretaceous event. The influence of inherited structural trends and location of inversion structures is discussed. This work provides a new understanding of structural inheritance and rift architecture, and highlights the complexity of the inversion history of the Browse Basin. It has implications for petroleum systems development and the timing of potential hydrocarbon trap formation.

The North Australian Craton 3D Gravity and Magnetic Inversion Models: A trial for first pass modelling of the entire Australian continent

The structural setting of the Ischia resurgent caldera and its magmatic system has been investigated by a joint interpretation of a 3D inversion of previously collected gravimetric data and all the available geological, geophysical and petrological data. Starting from the available Bouguer gravity map of the Neapolitan volcanic area and a previous 2.5D modelling, a selection of on land and offshore gravity data has been used to perform a 3D inversion, adapting and merging the basic ideas of two already tested methods, used to detect isolated bodies and layered discontinuities respectively. The base of the map is a set of gravity values, covering the whole Neapolitan volcanic area and the Gulf of Naples, which results from the union of 862 offshore and about 2000 on land extant gravity data, made uniform and re-analysed. The final model proposed here allows us to outline a very detailed and well-constrained structural setting of the crustal sector beneath Ischia. In particular, the 3D gravity inversion allowed us to outline a body with negative density contrast under Mt Epomeo, interpreted as the resurgent block, and to describe the magmatic system underneath it as a complex system of intrusions, rather than a uniformly distributed laccolithic body.

Seismically Informed High-Resolution Geometric Gravity Inversion and Forward Modeling of Paleozoic Basement in Rock Valley, NV

The depth of the basin beneath Frenchman Flat is estimated using a gravity inversion method. Gamma-gamma density logs from two wells in Frenchman Flat constrained the density profiles used to create the gravity inversion model. Three initial models were considered using data from one well, then a final model is proposed based on new information from the second well. The preferred model indicates that a northeast-trending oval-shaped basin underlies Frenchman Flat at least 2,100 m deep, with a maximum depth of 2,400 m at its northeast end. No major horst and graben structures are predicted. Sensitivity analysis of the model indicates that each parameter contributes the same magnitude change to the model, up to 30 meters change in depth for a 1% change in density, but some parameters affect a broader area of the basin. The horizontal resolution of the model was determined by examining the spacing between data stations, and was set to 500 square meters.

Integrated geological and geophysical studies for delineation of laterite covered chromiferous ultramafic bodies around Bhuban, southwestern part of Sukinda ultramafic complex, Odisha

A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO2 monitoring, the first step is done with either controlled source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A model-based parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO2 sequestration in the Skade formation. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

This paper investigates subsurface structures within the United Arab Emirates University (UAEU) campus using microgravity survey. Gravity methods are widely used in geophysical exploration to detect density variations that may indicate subsurface anomalies. The primary objective of this research is to generate the Bouguer anomaly map of the UAEU campus to map subsurface structures and to assess potential geological hazards such as cavities. A total of 76 gravity stations were measured across the UAEU campus using the Scintrex CG-6 gravimeter. The acquired gravity data underwent standard processing to obtain the Bouguer anomaly, including tidal, instrumental drift, latitude, free air, Bouguer, and terrain corrections. The first Bouguer anomaly map of the UAEU campus was produced, revealing gravity values ranging from 112.8 to 115.1 mGal. High gravity anomalies are concentrated in the north-eastern and western regions, while lower gravity anomalies are predominantly observed in the southeastern and southwestern areas. Gravity gradient derivative techniques, such as the horizontal gradient, tilt angle, horizontal derivative of tilt angle and analytic signal were applied to the Bouguer anomaly. A 3-D gravity inversion was performed, modeling density variations from the surface down to a depth of 300 m below the sea level. The study successfully identified significant density contrasts indicative of probable cavities or low-density bodies, lithological variations and possible faults zones dominant structural trends along WNW-ESE, NW-SE, N-S, and E-W directions in the study area. Two distinct geological layers were identified: a shallow layer extending up to approximately 50 m and a comparatively deeper layer ranging from 50 to 100 m in depth. The 3-D inversion model delineated two prominent low-density anomalies at depths of 125–175 m from surface, suggesting the presence of cavities or low-density zones. This research represents the first high-resolution microgravity study of the UAEU campus, establishes a baseline microgravity dataset for the UAEU campus. The results enhance understanding of subsurface and highlight the need for further geotechnical and geophysical investigations to validate and refine cavity detection and hazard assessments.

We present a 3D joint inversion method to estimate two physical parameters, density and magnetization of subsurface materials using field intensity measurements. In the method, we introduce the fuzzy c-means (FCM) clustering technique to relate gravity with magnetic data. In the approach, the subsurface structure is discretized to a set of rectangular prisms. For estimating the density and magnetization of each prism, we minimize the quadratic norm of the residuals between the observed data and the calculated responses from inversion model. Two regularization terms, i.e. the roughness and the similarity of the two physical parameters, are introduced in our joint inversion to control the degree of model roughness and similarity. We determine their regularization parameters using the L-curve criterion. We apply our method to a numerical model which represents submarine massive sulphides (SMS). The joint inversion results, which have the advantages of both gravity and magnetic inversion, show better accuracy and resolution than the individual ones.

Abstract. We introduce a workflow integrating geological modelling uncertainty information to constrain gravity inversions. We test and apply this approach to the Yerrida Basin (Western Australia), where we focus on prospective greenstone belts beneath sedimentary cover. Geological uncertainty information is extracted from the results of a probabilistic geological modelling process using geological field data and their inferred accuracy as inputs. The uncertainty information is utilized to locally adjust the weights of a minimum-structure gradient-based regularization function constraining geophysical inversion. Our results demonstrate that this technique allows geophysical inversion to update the model preferentially in geologically less certain areas. It also indicates that inverted models are consistent with both the probabilistic geological model and geophysical data of the area, reducing interpretation uncertainty. The interpretation of inverted models reveals that the recovered greenstone belts may be shallower and thinner than previously thought.

Horizontal-to-Vertical Spectral Ratios (HVSR) and Rayleigh group velocity dispersion curves (DC) can be used to estimate the shallow S-wave velocity () structure. Knowing the structure is important for geophysical data interpretation either in order to better constrain data inversions for P-wave velocity () structures such as travel time tomography or full waveform inversions or to directly study the structure for geo-engineering purposes (e.g., ground motion prediction). The joint inversion of HVSR and dispersion data for 1D structure allows characterising the uppermost crust and near surface, where the HVSR data ( to ) are most sensitive while the dispersion data (1 to ) constrain the deeper model which would, otherwise, add complexity to the HVSR data inversion and adversely affect its convergence. During a large-scale experiment, 197 three-component short-period stations, 41 broad band instruments and 190 geophones were continuously operated for 6 months (April to October 2017) covering an area of approximately with a site spacing of approximately 1 to . Joint inversion of HVSR and DC allowed estimating and, to some extent density, down to depths of around . Broadband and short period instruments performed statistically better than geophone nodes due to the latter’s gap in sensitivity between HVSR and DC. It may be possible to use HVSR data in a joint inversion with DC, increasing resolution for the shallower layers and/or alleviating the absence of short period DC data, which may be harder to obtain. By including HVSR to DC inversions, confidence improvements of two to three times for layers above were achieved. Furthermore, HVSR/DC joint inversion may be useful to generate initial models for 3D tomographic inversions in large scale deployments. Lastly, the joint inversion of HVSR and DC data can be sensitive to density but this sensitivity is situational and depends strongly on the other inversion parameters, namely and . Density estimates from a HVSR/DC joint inversion should be treated with care, while some subsurface structures may be sensitive, others are clearly not. Inclusion of gravity inversion to HVSR/DC joint inversion may be possible and prove useful.

Imaging Pleistocene volcanic edifices along the Egyptian Red Sea margin: Insights from reflection seismics and 3D constrained inversion of gravity and magnetic data

A gravity inversion technique using Deep Neural Networks (DNN) was developed to construct the 2D basement topography in offshore Abu Dhabi, UAE. Forward model parameters are set based on the geological features in the study area. Hundreds of thousands of synthetic forward models of the basement and their corresponding gravity anomalies are generated in a relatively short time by applying parallel computing. The simulated data are input to our DNN model which conducts the nonlinear inverse mapping of gravity anomalies to basement topography. To assess the model's robustness against noises, DNN models are retrained using datasets with noise-contaminated gravity data whose performances are evaluated by making predictions on unseen synthetic anomalies. Finally, we employed the DNN inversion model to estimate the basement topography using pseudo gravity anomalies over a profile in offshore UAE.