The northern Yucatán Peninsula hosts a complex karstic environment shaped by carbonate platform development and the Chicxulub impact event, making subsurface characterization crucial for geological and hydrogeological studies. This work aimed to resolve the shallow crustal structure and identify major tectonic features that influence karst processes and groundwater dynamics. We applied a rapid 3D gravity inversion method, linear back projection (LBP), to Bouguer anomaly data, combined with Euler deconvolution to map shallow and deep fault systems. The inversion produced a high-resolution density model down to 12.8 km depth, revealing key geological structures. Multilevel thresholding delineated significant low-density basins, notably the Chicxulub crater, as well as buried sedimentary basins. Euler solutions identified fault networks that coincide with areas of intense karstification, particularly in the eastern peninsula. Results highlight the interplay between impact-related tectonics and karst evolution, influencing groundwater flow paths and recharge zones. This study demonstrates the effectiveness of gravity inversion and Euler deconvolution for regional crustal imaging in carbonate platforms and emphasizes the need for further local-scale surveys to investigate coastal aquifer vulnerability and saltwater intrusion processes.

The sonic log is a key tool for assessing the mechanical properties of rocks, identifying structural features, calibrating seismic data, and monitoring well integrity. However, sonic data are often incomplete due to time and cost constraints, tool failures, or unreliable measurements. Traditional approaches to generate synthetic sonic logs usually rely on empirical relationships or statistical methods. In this study, we applied an artificial intelligence approach in which a deep neural network was trained with real data from an oilfield in Mexico to reconstruct sonic logs based on their relationships with other geophysical well logs. Three models, each using different input logs, were trained to predict the sonic response. The models were validated on wells excluded from training, and performance was evaluated using the root mean square error (RMSE) and mean absolute percentage error (MAPE), showing satisfactory accuracy. The models achieved RMSE values between 1.4 and 1.7 [$\mathsf{\mu }$s/ft] and MAPE values between 2.1 and 2.6% on independent test wells, confirming robust predictive performance. We also generated synthetic sonic logs for wells where no sonic data were originally acquired, demonstrating the practical value of the proposed method. This work integrates convolutional (CNN) and recurrent (GRU) layers in a single deep-learning architecture, trained under strict well-level validation. The workflow is demonstrated on wells from the Tabasco Basin, representing a field-scale deployment not previously reported in similar studies.

In this study, we performed three-dimensional gravity modeling and inversion of the Vinton Dome, a well-known onshore salt structure located in southwestern Louisiana, USA, by simultaneously integrating Full Tensor Gravity (FTG) and conventional gravity data using a simulated-annealing approach. The process begins with the application of Tensor Euler Deconvolution (ETD), which provides a physically consistent initial model for inversion. This method extends the traditional Euler deconvolution by incorporating both the three diagonal components of the gravity-gradient tensor and the three components of the gravity field, thereby enhancing the frequency content and stability of the solutions. By combining FTG gradients and gravity data, the proposed workflow improves the precision of subsurface modeling, particularly in delineating salt-body boundaries and estimating their depth. The integration of ETD-derived initialization with joint inversion results in a more accurate reconstruction of density contrasts, offering a powerful approach for characterizing complex geologic structures such as the Vinton Dome.

Reverse time migration (RTM) exploits time-reversal symmetry and adjoint duality to focus wavefields and reconstruct subsurface reflectivity, but large surveys remain limited by the cost of forward and backward propagation. We present a Graphics Processing Unit (GPU)-accelerated adjoint RTM workflow for depth imaging of the Chicxulub impact structure using the marine A0/A1 composite profile (1996). The processed stacked section contains 14,172 traces with 6.25 m Common Depth Point (CDP) spacing, 1 ms sampling, and 18 s record length. Forward and adjoint wavefields are computed with a staggered-grid finite-difference scheme (fourth order in space, second in time) and Convolutional Perfectly Matched Layers (CPMLs), which provide stable finite-domain simulations while introducing controlled symmetry breaking through absorption. The solver is verified with the Lamb half-space analytical benchmark and applied through five interpretation-guided velocity/density updates. The final depth image improves reflector continuity and interpretability of crater-scale elements, including post-impact sedimentary fill, melt and breccia units, terrace fault blocks, and deep uplift-related structure. Compute Unified Device Architecture (CUDA) acceleration reduces runtime from ∼32.36 h on a CPU baseline to ∼34.10 min on an RTX 3070 (≈56.9×), enabling practical, reproducible iterative RTM on accessible hardware.