Potential Fields for Solid Earth and Exploration Geophysics

A sandwiched piezoelectric accelerometer is developed and optimized for acquiring low-frequency, wide-band seismic data. The proposed accelerometer addresses the challenges of capturing seismic signals in the low-frequency range while maintaining a broad frequency response through the design of multi-stage charge amplifiers and a sandwiched structure. The device’s design, fabrication process, and performance evaluation are discussed in detail. Experimental results demonstrate its performance in amplitude and phase response characteristics. Full article

We developed an improved depth of extreme point (DEXP) method, characterized as an effective and rapid imaging method that can estimate the depth and distribution of a source quickly. Its main purpose is to solve various challenges. The automatic calculation aspect of the traditional method is often limited; namely, there is a problem with achieving automatic and reliable processing when the observed surface presents undulating topography, and this problem cannot be ignored. Therefore, we propose the addition of the constant method and the hypothetical observed surface method to achieve improvements in the traditional method. Firstly, we test the improved method on the synthetic models to demonstrate its notable advantage: the achievement of a fully automatic calculation without requiring any other additional information such as structural index (SI) values and threshold values. Meanwhile, we also demonstrate its ability and reliability to handle undulating topography with acceptable accuracy for imaging results. Furthermore, we verify the robustness of the improved method by applying it to real gravity data from the potash salt deposit in the Sakhon Nakhon basin, Laos. In this case, the improved DEXP method effectively identified the location of the potash deposit. Moreover, combined with the optimal edge detection method, gravity prospecting for potash salt deposits exhibited significant advantages. Full article

Lunar seismology is a critical area of research, providing insights into the Moon’s internal structure, composition, and thermal history, as well as informing the design of safe and resilient habitats for future human settlements. This paper presents the development of a state-of-the-art, three-axis broadband seismometer with a low-frequency range of 0.001–1 Hz and a target sensitivity over one order of magnitude greater than previous Apollo-era instruments. The paper details the design, assembly, methodology, and test results. We compare the acceleration noise of our prototype and commercial seismometers across all three axes. Increasing the test mass and reducing its natural frequency may further improve performance. These advancements in seismometer technology hold promise for enhancing our understanding of the Moon’s and other celestial bodies’ internal structures and for informing the design of future landed missions to ocean worlds. Full article

Predicting the oil–gas-bearing distribution of unconventional reservoirs is challenging because of the complex seismic response relationship of these reservoirs. Artificial neural network (ANN) technology has been popular in seismic reservoir prediction because of its self-learning and nonlinear expression abilities. However, problems in the training process of ANNs, such as slow convergence speed and local minima, affect the prediction accuracy. Therefore, this study proposes a hybrid prediction method that combines mutation particle swarm optimization (MPSO) and ANN (MPSO-ANN). It uses the powerful search ability of MPSO to address local optimization problems during training and improve the performance of ANN models in gas-bearing distribution prediction. Furthermore, because the predictions of ANN models require good data sources, multicomponent seismic data that can provide rich gas reservoir information are used as input for MPSO-ANN learning. First, the hyperparameters of the ANN model were analyzed, and ANNs with different structures were constructed. The initial ANN model before optimization exhibited good predictive performance. Then, the parameter settings of MPSO were analyzed, and the MPSO-ANN model was obtained by using MPSO to optimize the weights and biases of the developed ANN model. Finally, the gas-bearing distribution was predicted using multicomponent seismic data. The results indicate that the developed MPSO-ANN model (MSE = 0.0058, RMSE = 0.0762, R² = 0.9761) has better predictive performance than the PSO-ANN (MSE = 0.0062, RMSE = 0.0786, R² = 0.9713) and unoptimized ANN models (MSE = 0.0069, RMSE = 0.0833, R² = 0.9625) on the test dataset. Additionally, the gas-bearing distribution prediction results were consistent overall with the actual drilling results, further verifying the feasibility of this method. The research results may contribute to the application of PSO and ANN in reservoir prediction and other fields. Full article

Downward continuation is a key technique for processing and interpreting gravity anomalies, as it has a major role in reducing values to horizontal planes and identifying small and shallow sources. However, it can be unstable and inaccurate, particularly when continuation depth increases. While the Milne and Adams–Bashforth methods based on numerical solutions of the mean-value theorem have partly addressed these problems, more accurate and realistic methods need to be presented to enhance results. To address these challenges, we present two new methods, Milne–Simpson and Adams–Bashforth–Moulton, based on implicit expressions and their predictor-correctors. We test the validity of the presented methods by applying them to synthetic models and real data, and we obtain stability, accuracy, and large depth (eight times depth intervals) downward continuation. To facilitate wider applications, we use calculated vertical derivatives (of the first order) by the integrated second vertical derivatives (ISVD) method to replace theoretical ones from forward calculations and real ones from observations, obtaining reasonable downward continuations. To further understand the effect of introduced calculation factors, we also compare previous and presented methods under different conditions, such as with purely theoretical gravity anomalies and their vertical derivatives at different heights from forward calculations, calculated gravity anomalies and their vertical derivatives at non-measurement heights above the observation by upward continuation, calculated vertical derivatives of gravity anomalies by the ISVD method at the measurement height, and noise. While the previous Adams–Bashforth method sometimes outperforms the newly presented methods, new methods of the Milne–Simpson predictor-corrector and Adams–Bashforth–Moulton predictor-corrector generally present better downward continuation results compared to previous methods. Full article

Salt interpretation using seismic data is essential for structural interpretation and oil and gas exploration. Although deep learning has made great progress in automatic salt image segmentation, it is often difficult to obtain satisfactory results in complex situations. Thus, interactive segmentation with human intervention can effectively replace the fully automatic method. However, the current interactive segmentation cannot be directly applied to 3D seismic data and requires a lot of human interaction. Because it is difficult to collect 3D seismic data containing salt, we propose a workflow to simulate salt data and use a large amount of 3D synthetic salt data for training and testing. We use a 3D U-net model with skip connections to improve the accuracy and efficiency of salt interpretation. This model takes 3D seismic data volume with a specific size as an input and generates a salt probability volume of the same size as an output. To obtain more detailed salt results, we utilize a 3D graph-cut to ameliorate the results predicted by the 3D U-net model. The experimental results indicate that our method can achieve more efficient and accurate segmentation of 3D salt bodies than fully automatic methods. Full article

In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds’ density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the “shape-of-anomaly” data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin. Full article

Electrical resistivity tomography (ERT) is a well-known geophysical method applied to geological, hydrogeological and geoenvironmental research. To date, 2D ERT is still used much more than 3D ERT, thanks to its greater immediacy, survey speed and lower complexity in processing and inversion. However, the assumption of two-dimensionality of the underground structures can mean that the effects of 3D structures on the 2D ERT can sometimes lead to gross errors in interpretation. This work aims to evaluate these effects by testing synthetic and experimental models. Numerical simulations are performed starting from different resistivity models, and from the results, 2D data sets are derived to study and quantify the effects of 2D inversion on 3D structures. Tests simulating prismatic resistive blocks with a vertical square section are presented. Prisms extend orthogonally to the survey line. Depending on their length, they range from a minimum equal to the length of the section (cubic resistive block) to infinity (2D prism). On these models, 2D and 3D electrical resistivity tomography (ERT) surveys are simulated. The results show that resistive blocks with a limited extension orthogonal to the profile are not effectively resolved by 2D ERT. Additionally, resistivity values obtained from a 2D ERT inversion on a 3D resistive prism are underestimated more than those obtained on the corresponding 2D prism when compared with the true value. This underestimation increases with the three-dimensional characteristics. Furthermore, resistive blocks located near the survey line but not crossed by it create artifacts that can lead to erroneous interpretations. A field test performed on a calcarenite quarry, of which the three-dimensional geophysical model was reconstructed, confirmed the results obtained by the synthetic tests, highlighting that the effects of three-dimensionality can lead to the identification of artifacts in the vertical section or produce strong errors in the estimation of depth and size, thus causing misleading statements. Full article

Phlegraean Fields is a large, active caldera located in the densely populated westernmost sector of Naples’s Bay (Southern Italy). Several Bouguer anomaly surveys are available for this area with different resolution and accuracy; gravity data derive from the integration of stations placed below and above the sea level as the caldera develops both onshore and offshore. The comparison of these maps with the Digital Elevation Model shows a still remaining Terrain Effect hiding the shallower and deep caldera structure’s signal. This effect has an impact on the modelling of the gravity source’s depth and geometry. In this research, we apply a geologically constrained terrain correction method to the higher resolution Free Air dataset available for the study area to enhance the complete Bouguer reduction. The correlation analysis between the residual and the topography allows us to assess the quality of the outcomes. The results represent an improvement in the anomalies’ isolation and clearly show a continuous circular-like clustering of maxima related to the geometry of the caldera rim. The minima are associated with volcano-tectonic depression filled with pyroclastic and sediment. Furthermore, features alignments overlap the fault systems, along which the volcanic activity occurred. Full article

Since its invention in 1955, the Galperin symmetric triaxial seismometer has been widely used for seismic detection on Earth, and most recently on the planet Mars. In this paper, we present detailed physics of such seismometers, which has not yet been published in open literature. We extended Galperin’s original work, which is based on idealized geometry and assumptions, to include more practical cases, including (1) non-idealized tilt angles of its component seismometers; (2) component seismometers that are not exactly oriented 120° apart; (3) distributed mass on the boom; and (4) the case of operations at lower frequencies. Full article

Aeromagnetic compensation is a crucial step in the processing of aeromagnetic data. The aeromagnetic compensation method based on the linear regression model has poorer fitting capacity than the neural network aeromagnetic compensation algorithm. The existing gradient updating neural network-based aeromagnetic compensation algorithm is subject to the problem that the gradient disappears during the backpropagation process, resulting in poor fitting ability and affecting aeromagnetic compensation accuracy. In this paper, we propose a neural network compensation algorithm with strong fitting ability: residual backpropagation neural network (Res-bp). The algorithm realizes the cross-layer propagation of the gradient through a residual connection so that the network not only preserves the original information but also acquires additional information during training, successfully solving the problem of gradient disappearance and boosting the network’s fitting capacity. The algorithm is applied to the data collected by unmanned aerial vehicles (UAVs) to verify its effectiveness. The results show that the improvement ratio is improved compared with the traditional neural network, demonstrating that the algorithm has a significant compensation effect on aeromagnetic interference and improves the quality of aeromagnetic data. Full article

The detection depth of current borehole acoustic reflection imaging is only tens of meters without high resolution. This considerably limits its wide application in the identification and fine description of unconventional reservoirs and in the optimization of drilling trajectories. Increasing the directional energy from the transmitter to a geological structure is an excellent way to solve this issue. In this study, a plasma source with a parabolic reflector was introduced during borehole acoustic reflection imaging. First, an experimental system was built for testing the plasma source. Next, the acoustic-electrical characteristics and directional radiation of the source were studied using experiments and a numerical simulation. Finally, the advantages, disadvantages, and feasibility of the plasma-transmitting source were analyzed; some suggestions for further work on the source and its logging application were proposed. The experimental and simulation results show that the use of a plasma source with a parabolic reflector can increase the detection depth of borehole acoustic reflection imaging to hundreds of meters with high resolution. This is crucial in imaging the geological structures near boreholes and enhancing oil–gas exploration and development. Full article

Potential fields methods, based on the exploitation of gravity and magnetic fields, are among the most important methods to recover fundamental information on the Earth crust structure at global, regional and local scales. The bottleneck for this kind of geophysical methods is often represented by the development of ad-hoc techniques to fully exploit the available data. In fact, each different technique can observe the effect of a single property of the subsurface and when we want to estimate this property from the observed field (the so-called inverse problem), several problems such as non-uniqueness and instability arise. A possible solution to these problems consists in jointly inverting, in a consistent way, different observed fields, possibly also incorporating all the available geological constraints. In the current work, we present an innovative Bayesian algorithm aimed at performing a full 3D joint inversion of gravity and magnetic fields constrained by geological a-priori qualitative information. The algorithm is tested on a real-case scenario, namely, a local study to estimate a complete 3D model of the Oka carbonatite complex. This complex is a composite pluton in Quebec (Canada), important for mining operations related to critical raw material such as Niobium and other rare earth. This example shows the reliability of the developed inversion algorithm and gives hints on the fundamental role that potential fields can play in mining activities. Full article

We propose a processing workflow to enhance the information content of aeromagnetic data. Our workflow is based on the downward continuation and subsequent L-transform of magnetic data. This workflow returns a map showing single highs, which correspond to the location of magnetic bodies, and does not need any a priori information about the source magnetization. We validated our workflow using the aeromagnetic anomalies of the Tyrrhenian Sea (Italy), by a comparison of the reprocessed aeromagnetic anomalies with high-resolution shipborne magnetic data in three selected areas. Through this comparison, we show that the proposed processing workflow of aeromagnetic data leads to more accurate interpretative results. Our results indicate that, in areas where higher resolution data are lacking, the reprocessing of aeromagnetic data according to our workflow may be as decisive as to suggest changes to their previous interpretations or, at least, useful for highlighting areas of special interest, deserving to be magnetically explored by a dedicated high-resolution shipborne survey. Full article

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal. Full article