Computational electromagnetic geophysics for groundwater system studies: A review of established practices and recent advances - Paula Rulff; Octavio Castillo-Reyes; Wouter Deleersnyder; Maria Carrizo Mascarell; Burke J. Minsley; Jude King (Journal of Hydrology, 2026)

Recent Advances in 2D Frequency-Domain Marine Controlled-Source EM Inversion Using Adjoint Approximate Sensitivities - Gang Li; Yutao Liu; Octavio Castillo-Reyes (Surveys in Geophysics, 2026)

Electromagnetic Subsurface Imaging in the Presence of Metallic Structures: A Review of Numerical Strategies - Octavio Castillo-Reyes; Pilar Queralt; Perla Piñas-Varas; Juanjo Ledo; Otilio Rojas (Surveys in Geophysics, 2024)
Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts.

Meshing strategies for 3d geo-electromagnetic modeling in the presence of metallic infrastructure - Octavio Castillo-Reyes; Paula Rulff; Evan Schankee Um; Adrian Amor-Martin (Computational Geosciences, 2023)
In 3D geo-electromagnetic modeling, an adequate discretisation of the modeling domain is crucial to obtain accurate forward responses and reliable inversion results while reducing the computational cost. This paper investigates the mesh design for subsurface models, including steel-cased wells, which is relevant for many exploration settings but still remains a numerically challenging task. Applying a goal-oriented mesh refinement technique and subsequent calculations with the high-order edge finite element method, simulations of 3D controlled-source electromagnetic models in the presence of metallic infrastructure are performed. Two test models are considered, each needing a distinct version of approximation methods to incorporate the conductive steel casings of the included wells. The influence of mesh quality, goal-oriented meshing, and high-order approximations on problem sizes, computational cost, and accuracy of electromagnetic responses is investigated. The main insights of our work are: (a) the applied numerical schemes can mitigate the computational burden of geo-electromagnetic modeling in the presence of steel artifacts; (b) investigating the processes driving the meshing of models with embedded metallic infrastructures can lead to adequate strategies to deal with the inversion of such electromagnetic data sets. Based on the modeling results and analyses conducted, general recommendations for modeling strategies are proposed when performing simulations for challenging steel infrastructure scenarios.

Assessment of photo electro-Fenton and solar photo electro-Fenton processes for the efficient degradation of asulam herbicide - Héctor H. Vigil-Castillo; Edgar J. Ruiz-Ruiz; Khirbet López-Velázquez; Laura Hinojosa-Reyes; Octavio Gaspar-Ramírez; Jorge L. Guzmán-Mar (Chemosphere, 2023)

Machine Learning as a Seismic Prior Velocity Model Building Method for Full-Waveform Inversion: A Case Study from Colombia - Ursula Iturrarán-Viveros; Andrés M. Muñoz-García; Octavio Castillo-Reyes; Khemraj Shukla (Pure and Applied Geophysics, 2021)
We use machine learning algorithms (artificial neural networks, ANNs) to estimate petrophysical models at seismic scale combining well-log information, seismic data and seismic attributes. The resulting petrophysical images are the prior inputs in the process of full-waveform inversion (FWI). We calculate seismic attributes from a stacked reflected 2-D seismic section and then train ANNs to approximate the following petrophysical parameters: P-wave velocity ( $$V_\mathrm{{p}}$$ V p ), density ( $$ρ $$ ρ ) and volume of clay ( $$V_\mathrm{{clay}}$$ V clay ). We extend the use of the $$V_\mathrm{{clay}}$$ V clay by constraining it with the well lithology and we establish two classes: sands and shales. Consequently, machine learning allows us to build an initial estimate of the earth property model ( $$V_\mathrm{{p}}$$ V p ), which is iteratively refined to produce a synthetic seismogram that matches the observed seismic data. We apply the 1-D Kennett method as a forward modeling tool to create synthetic data with the images of $$V_\mathrm{{p}}$$ V p , $$ρ $$ ρ and the thickness of layers (sands or shales) obtained with the ANNs. A nonlinear least-squares inversion algorithm minimizes the residual (or misfit) between observed and synthetic full-waveform data, which improves the $$V_\mathrm{{p}}$$ V p resolution. In order to show the advantage of using the ANN velocity model as the initial velocity model for the inversion, we compare the results obtained with the ANNs and two other initial velocity models. One of these alternative initial velocity models is computed via P-wave impedance, and the other is achieved by velocity semblance analysis: root-mean-square velocity (RMS). The results are in good agreement when we use $$ρ $$ ρ and $$V_\mathrm{{p}}$$ V p obtained by ANNs. However, the results are poor and the synthetic data do not match the real acquired data when using the semblance velocity model and the $$ρ $$ ρ from the well log (constant for the entire 2-D section). Nevertheless, the results improve when including $$ρ $$ ρ , the layered structure driven by the $$V_\mathrm{{clay}}$$ V clay (both obtained with ANNs) and the semblance velocity model. When doing inversion starting with the initial $$V_\mathrm{{p}}$$ V p model estimated using the P-wave impedance, there is some gain of the final $$V_\mathrm{{p}}$$ V p with respect to the RMS initial $$V_\mathrm{{p}}$$ V p . To assess the quality of the inversion of $$V_\mathrm{{p}}$$ V p , we use the information for two available wells and compare the final $$V_\mathrm{{p}}$$ V p obtained with ANNs and the final $$V_\mathrm{{p}}$$ V p computed with the P-wave impedance. This shows the benefit of employing ANNs estimations as prior models during the inversion process to obtain a final $$V_\mathrm{{p}}$$ V p that is in agreement with the geology and with the seismic and well-log data. To illustrate the computation of the final velocity model via FWI, we provide an algorithm with the detailed steps and its corresponding GitHub code.

Ecological guidelines for designing networks of marine reserves in the unique biophysical environment of the Gulf of California - Adrian Munguia-Vega; Alison L. Green; Alvin N. Suarez-Castillo; Maria Jose Espinosa-Romero; Octavio Aburto-Oropeza; Andrés M. Cisneros-Montemayor; Gabriela Cruz-Piñón; Gustavo Danemann; Alfredo Giron-Nava; Ollin Gonzalez-Cuellar; Cristina Lasch; Maria del Mar Mancha-Cisneros; et al. (Reviews in Fish Biology and Fisheries, 2018)

Areas of endemism persist through time: A palaeoclimatic analysis in the Mexican Transition Zone - Gonzalo E. Pinilla‐Buitrago; Tania Escalante; Ana Gutiérrez‐Velázquez; Pedro Reyes‐Castillo; Octavio R. Rojas‐Soto (Journal of Biogeography, 2018)
Aim The Mexican Transition Zone ( MTZ ) is an area where the Nearctic and Neotropical biogeographical regions overlap, generating high species richness and endemism. The objective of this study was to analyse if potential changes in the composition and the geographical location of areas of endemism (AEs) for beetles and mammals during the Last Glacial Maximum ( LGM ), the mid‐Holocene ( MH ) or the Last Interglacial ( LIG ) have influenced the definition of the MTZ in the present. Location Mexico and Central America. Methods Ecological niche models ( ENM ) were generated describing the current distribution of 218 species associated with the MTZ and then transferred to three periods into the past. A parsimony analysis of endemicity ( PAE ) was run to identify current AE s. The transferred models of each set of species that form the current AE s were used to assess if the geographical ranges of the species’ ecological niches changed over time, or whether they remained stable supporting the validation of the AE s during three past recent periods ( LGM , MH and LIG ). Results Two current AEs were detected that persisted geographically during the three past periods ( LGM , MH and LIG ). Main conclusions The results show that some AEs change through time as a response of climate, whereas others remained stable. Thus, the MTZ could be considered as a dynamic zone at least over the last 130,000 years. The climate analysis of the AEs allows them to be recognized either as true spatio‐temporal units, or as temporarily restricted patterns of co‐distribution resulting from changes in climate over time.