Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering

We propose a new pattern recognition method that is able to reconstruct the 3D structure of the active part of a fault network using the spatial location of earthquakes. The method is a generalization of the so-called dynamic clustering method, that originally partitions a set of datapoints into clusters, using a global minimization criterion over the spatial inertia of those clusters. The new method improves on it by taking into account the full spatial inertia tensor of each cluster, in order to partition the dataset into fault-like, anisotropic clusters. Given a catalog of seismic events, the output is the optimal set of plane segments that fits the spatial structure of the data. Each plane segment is fully characterized by its location, size and orientation. The main tunable parameter is the accuracy of the earthquake localizations, which fixes the resolution, i.e. the residual variance of the fit. The resolution determines the number of fault segments needed to describe the earthquake catalog, the better the resolution, the finer the structure of the reconstructed fault segments. The algorithm reconstructs successfully the fault segments of synthetic earthquake catalogs. Applied to the real catalog constituted of a subset of the aftershocks sequence of the 28th June 1992 Landers earthquake in Southern California, the reconstructed plane segments fully agree with faults already known on geological maps, or with blind faults that appear quite obvious on longer-term catalogs. Future improvements of the method are discussed, as well as its potential use in the multi-scale study of the inner structure of fault zones

Enhancing coherency analysis for fault detection and mapping using 3D diffraction imaging

Automatic detection of geological discontinuities such as small throw faults, and pinch-outs is an important problem in the interpretation of 3D seismic data. This is commonly done using coherency analysis. However coherency may be affected by noise, which may create false anomalies. We propose a new interpretation workflow for the detection and mapping of faults, which enhances the coherency-type analysis with identification and detection of diffractions produced by the discontinuities. The algorithm utilizes migrated and unmigrated stacked seismic volumes and the cube of stacking (NMO) velocities. Tests on a simple 2.5 D model show that the method is capable in detecting and mapping of faults below seismic resolution

Structural Evolution of the Arabian Basin Based on 3D Seismic Interpretation

The Arabian basin was subject to several tectonic events, including Lower Cambrian Najd rifting, the Carboniferous Hercynian Orogeny, Triassic Zagros rifting, and the Early/Cretaceous and Late/Tertiary Alpine orogenic events. These events reactivated Precambrian basement structures and affected the structural configuration of the overlying Paleozoic cover succession. In addition to a 2D seismic array and several drill well logs, a newly acquired, processed 3D seismic image of the subsurface in part of the basin covering an area of approximately 1051 km2 has been provided to improve the understanding of the regional tectonic evolution associated with these deformation events. In this study, a manual interpretation is presented of six main horizons from the Late Ordovician to the Middle Triassic. Faults and folds were also mapped to further constrain the stratigraphic and structural framework. Collectively, this data is used to build a geological model of the region and develop a timeline of geological events. Results show that a lower Paleozoic sedimentary succession between the Late Silurian to the Early Permian was subject to localised tilting, uplift, and erosion during the Carboniferous Hercynian Orogeny, forming a regional unconformity. Subsequent deposition occurred from the Paleozoic to the Mesozoic, producing a relatively thick, conformable, upper succession. The juxtaposition of the Silurian rocks and Permian formations allows a direct fluid flow between the two intervals. Seismic analysis also indicated two major fault generations. A younger NNW-striking fault set with a component of reverse, east-side-up displacement affected the Lower Triassic succession and is most likely related to the Cretaceous and Tertiary Alpine Events that reactivated the Najd fault system. These fault structures allow vertical migration that could act as conduits to form structural traps. Manual mapping of fault structures in the study area required significant time and effort. To simplify and accelerate the manual faults interpretation in the study area, a fault segmentation method was developed using a Convolutional Neural Network. This method was implemented using the 3D seismic data acquired from the Arabian Basin. The network was trained, validated, and tested with samples that included a seismic cube and fault images that were labelled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training dataset. To achieve a more robust model, the prediction results were further enhanced using postprocessing by linking discontinued segments of the same fault and thus, reducing the number of detected faults. This method improved the accuracy of the prediction results of the proposed model using the test dataset by 77.5%. Additionally, an efficient framework was introduced to correlate the predictions and the ground truth by measuring their average distance value. This technique was also applied to the F3 Netherlands survey, which showed promising results in another region with complex fault geometries. As a result of the automated technique developed here, fault detection and diagnosis were achieved efficiently with structures similar to the trained dataset and has a huge potential in improving exploration targets that are structurally controlled by faults

Master of Science

thesisThe abundance of data from the Earthscope U.S. Transportable Array (TA) eliminates observational barriers such as data paucity and station sampling bias that have in the past hindered our understanding of the processes involved in dynamic triggering. The price of data abundance is that strategies must be developed to automate the systematic recovery of earthquake information. Optimized amplitude threshold detectors in the time-domain used to automate the process of earthquake detection with the TA data result in databases dominated by site-specific noise contributions. To increase the accuracy of detection databases, we develop a frequency-domain detection algorithm that employs spectral characteristics to distinguish earthquakes from other band-limited noise sources. This spectral filtering algorithm doubles the accuracy rate compared to timedomain methods. Despite the improvements in detection accuracy, we find that false detections in single-station pick databases still comprise a majority of all detections from the TA data. Leveraging frequency-domain processing techniques to develop array visualizations enables robust earthquake detection to magnitudes at or below M2. We use this array method to explore 18 global mainshocks (M>7) exhibiting the highest surface wave amplitudes during the TA deployment. Of the 18 mainshocks studied, none show strong evidence of instantaneous dynamic triggering and only one offers limited evidence for delayed dynamic triggering. These results suggest that prolific triggering in the U.S. is a rare phenomenon, requiring amplitudes outside the range observed here and/or that additional conditions (fluids, tectonic environment, frequency, or duration of shaking) within the amplitude ranges explored here play a primary role in dynamic triggering