With at least 60 processing cores, the Xeon-Phi coprocessor is a truly multicore architecture, which consists of an interconnection speed among cores of 240 GB/s, two levels of cache memory, a theoretical performance of 1.01 Tflops, and programming flexibility, all making the Xeon-Phi an excellent coprocessor for parallelizing applications that seek to reduce computational times. The objective of this work is to migrate a geophysical application designed to directly calculate the gravimetric tensor components and their derivatives and in this way research the performance of one and two Xeon-Phi coprocessors integrated on the same node and distributed in various nodes. This application allows the analysis of the design factors that drive good performance and compare the results against a conventional multicore CPU. This research shows an efficient strategy based on nested parallelism using OpenMP, a design that in its outer structure acts as a controller of interconnected Xeon-Phi coprocessors while its interior is used for parallelyzing the loops. MPI is subsequently used to reduce the information among the nodes of the cluster.

Potential-field-data imaging of complex geological features in deepwater salt-tectonic regions in the Gulf of Mexico remains an open active research field. There is still a lack of resolution in seismic imaging methods below and in the surroundings of allochthonous salt bodies. In this work, we present a novel three-dimensional potential-field-data simultaneous inversion method for imaging of salt features. This new approach incorporates a growth algorithm for source estimation, which progressively recovers geological structures by exploring a constrained parameter space; restrictions are posed from a priori geological knowledge of the study area. The algorithm is tested with synthetic data corresponding to a real complex salt-tectonic geological setting commonly found in exploration areas of deepwater Gulf of Mexico. Due to the huge amount of data involved in three-dimensional inversion of potential field data, the use of parallel computing techniques becomes mandatory. In this sense, to alleviate computational burden, an easy to implement parallelization strategy for the inversion scheme through OpenMP directives is presented. The methodology was applied to invert and integrate gravity, magnetic and full tensor gradient data of the study area.

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.

Within the geophysical exploration utilising seismic methods, it is well known that if the explored distances are much greater than the wavelength of the seismic waves with which the exploration is carried out, the ray approach of the wave theory can be used. In this way, when the rays travel through an inhomogeneous medium, they follow curved trajectories, which is imperative to determine the geological features that produce reflection and refraction phenomena. In this paper, a simple algorithm for the calculation of the trajectory of a seismic beam through an inhomogeneous stratum is presented. For this, the construction of a pseudo-Riemannian metric is required from the function of P-wave velocities of the geological stratum. Thus, the problem is inverted because instead of finding the curved trajectory of the seismic beam in a background with a Euclidean metric, it is proposed that the beam follows a geodesic of a curved space-time specific to each stratum, becoming a simple and automatic process using the differential geometry apparatus. For the reader to gain insight into this tool, different geological setups from idealised ones up to a salt dome are presented.