Determining Grain Boundary Position and Geometry from EBSD Data: Limits of Accuracy

As the feature size of crystalline materials gets smaller, the ability to correctly interpret geometrical sample information from electron backscatter diffraction (EBSD) data becomes more important. This paper uses the notion of transition curves, associated with line scans across grain boundaries (GBs), to correctly account for the finite size of the excitation volume (EV) in the determination of the geometry of the boundary. Various metrics arising from the EBSD data are compared to determine the best experimental proxy for actual numbers of backscattered electrons that are tracked in a Monte Carlo simulation. Consideration of the resultant curves provides an accurate method of determining GB position (at the sample surface) and indicates a significant potential for error in determining GB position using standard EBSD software. Subsequently, simple criteria for comparing experimental and simulated transition curves are derived. Finally, it is shown that the EV is too shallow for the curves to reveal subsurface geometry of the GB (i.e., GB inclination angle) for most values of GB inclination.

Recent faulting along Gorontalo fault based on seismicity data analysis and lineament mapping

We focused our study to characterize the geometry and activity of Gorontalo fault. We analysed reviewed the ISC seismic catalogue and the BMKG relocated earthquake events available for the time period of 1960 to 2021, located along the expected location of this fault. In addition, we analysed continuous record from local seismic observatory available for the same period. Further, we mapped the lineaments using 8.3-m resolution DEMNAS data. Tens on shallow earthquakes occurred in the vicinity of this fault with a range magnitude of M 2 to 3. Our lineament analysis however does not reveal distinctive pattern that may indicate the fault manifestation at the surface. The NW-SE trending lineaments are coincidence with the mapped trace of Gorontalo Fault. The weak surface manifestation of the fault scarp may be related to the tropical climatic condition of the area which may obliterate the faulting topography. However, we observed alignment of the seismicity distribution with the mapped NW-SE lineament, indicating that the lineament is likely representing active fault and these earthquakes are associated with faulting along this fault. Our study provide indication that the Gorontalo Fault is active and further study is necessary to investigate subsurface geometry and mitigate its seismic hazards.

Joint inversion of full-waveform ground-penetrating radar and electrical resistivity data: Part 1

We have developed an algorithm for joint inversion of full-waveform ground-penetrating radar (GPR) and electrical resistivity (ER) data. The GPR data are sensitive to electrical permittivity through reflectivity and velocity, and electrical conductivity through reflectivity and attenuation. The ER data are directly sensitive to the electrical conductivity. The two types of data are inherently linked through Maxwell’s equations, and we jointly invert them. Our results show that the two types of data work cooperatively to effectively regularize each other while honoring the physics of the geophysical methods. We first compute sensitivity updates separately for the GPR and ER data using the adjoint method, and then we sum these updates to account for both types of sensitivities. The sensitivities are added with the paradigm of letting both data types always contribute to our inversion in proportion to how well their respective objective functions are being resolved in each iteration. Our algorithm makes no assumptions of the subsurface geometry nor the structural similarities between the parameters with the caveat of needing a good initial model. We find that our joint inversion outperforms the GPR and ER separate inversions, and we determine that GPR effectively supports ER in regions of low conductivity, whereas ER supports GPR in regions with strong attenuation.

3D Characterization of a Coastal Freshwater Aquifer in SE Malta (Mediterranean Sea) by Time-Domain Electromagnetics

Electromagnetic (EM) geophysical methods are well equipped to distinguish electrical resistivity contrasts between freshwater-saturated and seawater-saturated formations. Beneath the semi-arid, rapidly urbanizing island of Malta, offshore groundwater is an important potential resource but it is not known whether the regional mean sea-level aquifer (MSLA) extends offshore. To address this uncertainty, land-based alongshore and across-shore time-domain electromagnetic (TDEM) responses were acquired with the G-TEM instrument (Geonics Ltd., Mississauga, ON, Canada) and used to map the onshore structure of the aquifer. 1-D inversion results suggest that the onshore freshwater aquifer resides at 4–24 m depth, underlain by seawater-saturated formations. The freshwater aquifer thickens with distance from the coastline. We present 2D and 3D electromagnetic forward modeling based on finite-element (FE) analysis to further constrain the subsurface geometry of the onshore freshwater body. We interpret the high resistivity zones that as brackish water-saturated bodies are associated with the mean sea-level aquifer. Generally, time-domain electromagnetic (TDEM) results provide valuable onshore hydrogeological information, which can be augmented with marine and coastal transition-zone measurements to assess potential hydraulic continuity of terrestrial aquifers extending offshore.

Extracting horizon surfaces from 3D seismic data using deep learning

Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.