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The drift rate of relative gravimeters differs from time to time and from meter to meter. Furthermore, it is inefficient to estimate the drift rate by returning them frequently to the base station or stations with known gravity values during gravity survey campaigns for a large region. Unlike the conventional gravity adjustment procedure, which employs a linear drift model, we assumed that the variation of drift rate is a smooth function of lapsed time. Using this assumption, we proposed a new gravity data adjustment method by means of objective Bayesian statistical inference. Some hyper-parameters were used as trade-offs to balance the fitted residuals of gravity differences between station pairs and the smoothness of the temporal variation of the drift rate. We employed Akaike’s Bayesian information criterion (ABIC) to estimate these hyper-parameters. A comparison between results from applying the classical and the Bayesian adjustment methods to some simulated datasets showed that the new method is more robust and adaptive for solving problems caused by irregular nonlinear meter drift. The new adjustment method is capable of determining the time-varying drift rate function of any specific gravimeter and optimizing the weight constraints for every gravimeter used in a gravity survey. We also carried out an error analysis for the inverted gravity value at each station based on the marginal distribution. Finally, we used this approach to process actual gravity survey campaign data from an observation network in North China.

This study investigates the subsurface rooting of the Merija anticline in the Missour Basin, Morocco, with a focus on copper mineralization exploration. A sequential geophysical workflow was implemented, combining gravity surveys, electrical resistivity (ER), and induced polarization (IP) methods. The gravity data, acquired along spaced profiles extending from outcropping areas to Quaternary-covered zones, clearly delineated the structural continuity of the anticline beneath the cover. The application of trend filtering in covered areas allowed the removal of regional effects, successfully isolating residual anomalies associated with the buried continuation of the anticline. Interpolated Bouguer anomaly maps highlighted a major regional fault, interpreted as controlling the deep rooting of the anticline. A resistivity profile was then deployed perpendicular to this fault, providing detailed imaging of the anticline’s geometry and lithological contrasts. Complementary IP profiles conducted near the mine site targeted the detection of chargeability anomalies associated with copper mineralization dominated by malachite, confirming the electrical signature of copper mineralization, particularly within the sandstone and conglomerate formations of the Lower Cretaceous. To validate the geophysical interpretations, a drilling campaign was conducted, which confirmed the presence of the identified lithological units and the anticline rooting, as revealed by geophysical data. This approach provides a robust framework for copper exploration in the Merija area and can be adapted to similar geological contexts elsewhere.

The Ghare-Aghaj potash resource is one of the largest halokinetic-type potash-bearing salt deposits in the northern part of the central Iran zone, within the Zanjan Province. This near-surface deposit originates in a sedimentary environment rich in siliciclastic sediments during the relaxation phase between tectonic activities. To explore such massive evaporates, because of their low density contrast compared to the surrounding rocks, a ground-based gravity survey proves to be an ideal strategy. In this study, we use a mixed L p –L q  norm regularization in a constrained inversion process to generate compact and smooth models in three dimensions. This approach leads to the accurate detection of deposit boundaries and also localizes the prospect zone. First, we employ this method in a synthetic case, representing a simple vertical prism shape resembling evaporates' general geometry, such as salt domes and potash mineralization. This assessment examines how well it can recover these geometric structures with lower density contrast at varying depths. Subsequently, we apply this method in a real-case scenario. The findings detect potash mineralization within a salt wall structure at about 10–20 m below the surface, considering the challenging topographical conditions. Smooth reconstructed models, while accurately recovering the background properties, fail to recover the boundaries of the target ore. Conversely, blocky reconstructed models sharply recover the geometry of the potash mineralization but with less precision in the recovery of the background properties. The inversion results illustrate a salt dome within an anticline, showing a vertical expansion of around 110 m and a lateral expansion of 160 m. These results are in good concordance with the well data, indicating a salt distribution with a vertical expansion ranging from 11 to 115 m below the surface.

Ceboruco volcano (−104°30′, 21°7′, 2150 m asl) is located in the western portion of the trans-Mexican volcanic belt and NW extreme of the Tepic-Zacoalco rift zone, a structure composed of a series of NNW-trending en echelon fault-bounded basins constituting the NE boundary between the north-American plate and the Jalisco block (JB). Ceboruco experimented a Plinian eruption about 1000 years ago and several more of different styles afterward; the last one in 1870 CE. This volcano poses a significant risk because of the relatively large population in its surroundings. Ceboruco has been studied by mostly from the point of view of petrology, geochemistry, and physical volcanology; however, no geophysical studies about its internal structure have been published. In this paper, we present the results of a gravimetric survey carried out in its surroundings and a model of the internal structure obtained from inversion of the data. The Ceboruco area is characterized by a negative Bouguer anomaly spanning the volcanic structure. The probable causative body modeled with the data of the survey is located about 1 km below mean sea level and has a volume of 163 km 3 . We propose that this body is the magma chamber from where the products of its eruptions in the last 1000 years ensued.

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

Southeastern (SE) Tibet on the Chinese mainland is geologically active and plays an important role in subsurface deformation and mass transfers. Hybrid gravimetry using both absolute and relative gravimeters is an efficient tool for monitoring surface and underground mass transfers. But for hybrid gravity network surveys, uncertainties are influenced by measurement errors, while sparseness of the network and environmental artifacts must be identified and minimized prior to studying gravity change. In this study, Bayesian gravity adjustment (BGA) was applied for the first time to the hybrid gravity network in SE Tibet during 2014–2016, which effectively reduced measurement uncertainties via its estimated scale factors and drift rates, thereby demonstrating its suitability for the large and complex gravity network in SE Tibet. To estimate the field source resolution, reduce environmental artifacts, and invert mass redistributions on the deep crust, an equivalent source inversion (ESI) based on the spatiotemporal smoothness regularization constraint model and the Akaike Bayesian information criterion parameter estimation method was applied to datasets processed by BGA. With respect to processing synthetic gravity data with spatiotemporal noises, the ESI was an effective algorithm, with the optimal field source resolution in SE Tibet being 0.75° × 0.75°. The apparent density change at a depth of 20 km was then inverted, with an average rate of −0.6 to 0.6 kg/m3/year, which was approximately 0.22‰ of the average crustal density. In addition, its spatial distribution showed close consistency with active tectonic block boundaries and low-velocity/high-conductivity zones. Comprehensively considering the hydrological effects, GPS observation studies, and geophysical and petrological evidence in the region, this study suggests that the crustal mass redistributions in SE Tibet are possibly controlled by active tectonic block boundaries and fluids distributed in the deep crust.

Urban underground space exploration is an important way to solve urban underground problems, Identifying the geological structure of urban underground space is of great significance for urban development. Gravity exploration is one of the most commonly used and effective geophysical exploration methods, using a high-precision gravimeter to collect small changes in the gravity field within the survey area and obtain underground spatial anomalies through data analysis. This paper introduces the gravity exploration method, elaborates on the principle of microgravity measurement, constructs a cavity anomaly model, and conducts simulations to provide theoretical support for carrying out gravity measurement. Finally, experimental tests of the cavity model were carried out at the test site to verify the feasibility of microgravity measurement in urban underground space detection.

Like many geophysical observations, relative gravity (RG) measurements are affected by random errors, systematic errors, and occasional blunders. When RG measurements are used to build large gravity networks in remote areas under adverse environmental or logistical conditions (such as extreme temperatures, heavy precipitation, rugged terrain, difficult or dangerous roads, and high altitudes), it is more likely for significant errors to occur and accumulate. Therefore, obtaining accurate gravity estimates at regional gravity networks largely depends on defensive data collection protocols and robust adjustment techniques. In this work, we present a measurement field protocol based on highly redundant observation patterns, and a two-step least squares adjustment scheme implemented as a MATLAB package. This software helps us identify blunders, mitigates the impact of random errors, and downweights or removes outlier observations. The methodology also guarantees that adjusted gravity values have well-constrained standard error estimates. We illustrate the capabilities of our approach through the case study of the Bolivian gravity network, where we determined the acceleration due to gravity at 2548 stations that spread over difficult and sometimes extreme environments, with a typical level of uncertainty of 0.10–0.15 mGal.

Sub-ice-shelf bathymetry strongly influences ice shelf stability by guiding melt-inducing water masses and through pinning points that resist the flow of the overriding ice. Collecting sub-ice-shelf bathymetry data using active source seismic surveying or direct observations is accurate but time-consuming and often impractical. Gravity methods provide a pragmatic, but more uncertain, alternative, by which observed variations in Earth's gravitational field are used to estimate the underlying bathymetry. We utilize a new open-source gravity inversion algorithm developed specifically for modeling sub-ice-shelf bathymetry and estimating the spatially variable uncertainty in the results. The inversion is tested on a suite of models created with high-resolution multibeam bathymetry data. These tests enable (1) determination of the best practices for conducting bathymetric inversions, (2) recognition of the limitations of the inversion and uncertainty quantification, and (3) identification of where community efforts should be focused for the future determination of Antarctica's sub-ice-shelf bathymetry. With an airborne gravity survey with 10 km spacing, 3 mGal of errors, a distribution of known bathymetry measurements, and a regional gravity field representative of the average Antarctic ice shelf, we achieve a root mean squared error of the inverted bathymetry of 23 m. We find that estimating and removing the regional component of gravity before the inversion is the largest source of error in the resulting bathymetry model, but this error can be greatly reduced with additional known bathymetry points. We analyzed Antarctic ice shelves and predict that, if high-resolution gravity data were available, gravity inversion could potentially improve bathymetry models for 95 % of them compared to interpolated products like Bedmap2.

We investigate using the GIRAFE cold-atom gravimeter during an airborne gravity survey for improving gravity field and quasigeoid modelling. The study is conducted over the Bay of Biscay, France. Geoid/quasigeoid determination is usually a major challenge over such coastal areas due to scarce and inconsistent gravity data. In a first step, the GIRAFE dataset is analysed and compared with available surface gravity data as well as with global altimetry models from UCSD and DTU. The comparisons indicate that the DTU model is better than the UCSD model within around 10 km from the coastline. Furthermore, recent satellite altimeter missions significantly improve the altimetry models in coastal areas. A significant bias (− 4.00 mGal) in shipborne data is also found from this comparison. In a second step, eight quasigeoid solutions are calculated to evaluate the contribution of GIRAFE data. This contribution reaches 3 cm in terms of height anomaly for DTU21 while being much larger for UCSDv31 and shipborne data. Finally, the quasigeoid solutions are validated using GNSS-levelling data. The results indicate that using GIRAFE data improves by approximately 50% the quality of quasigeoid models over land near the coast. The highest accuracy, around 1 cm, is achieved when GIRAFE data are merged with refined gravity data. Importantly, the standard deviation is just 1.2 cm when compared with GNSS-levelling points if using only GIRAFE data over marine areas, which is very close to the 1 cm goal of geoid/quasigeoid model determination in modern geodesy. This study thus confirms the benefits of performing airborne gravity survey using quantum sensors.