Master of Science

thesisTwo-dimensional (2-D) and three-dimensional (3-D) seismic surveys are conducted across the Washington fault zone of northern Arizona, with the purpose of imaging the fault-related structures to a depth of 30 m by 3-D traveltime tomography and 2-D poststack migration. The scientific objective is to use the seismic methods instead of a trenching log to deduce the paleoseismic characters of this fault zone, and to guide paleoseismologists in the optimal placement of a future trenching survey. The first-arrival traveltimes of the data are picked and inverted for the P-wave velocity distribution. Tomographic results delineate two large low-velocity zones (LVZ), which are interpreted as two colluvial wedge packages. To detect the fault structures, which have more observable reflection energy than the 3-D data, the 2-D seismic data are migrated. Four faults are recovered in the migration image, including the main fault, and possible antithetic fault. The fault location is identical to that in the tomogram and raypath density image. The main fault in the tomogram is also consistent with the results from the geomorphology survey. These results demonstrate that seismic imaging methods (3-D traveltime tomography and 2-D reflection imaging) can delineate the shape and depth of LVZs associated with colluvial wedges. Although these LVZ images cannot unambiguously delineate different rupture events in a colluvial package, they can be used to optimally design a follow-on trenching survey. Combining the paleoseismic data with the fault slip inferred by tomography, the age of the fault is speculatively estimated to be younger than 16 kyr. Future work should compare my interpreted tomogram with the trench log soon to be excavated by personnel of the Utah Geological Survey (UGS), and analyze the validity of my geological interpretation. This trench was designed using the results of this survey, which is partial justification for seismic surveys over normal fault scarps

A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets