Abstract
This paper is devoted to the statistical analysis of dependence between fault length (L) and displacement (D). The main purpose of this work is to study the scaling relations between fault length and displacement using a database that includes datasets of 21 faults with geometric data extracted from 3D seismic coherence cubes of the Norwegian Barents Sea. Multiple linear regression and Bayesian and Akaike information criterions are applied to obtain optimal regression parameters. Our dataset is unique since it includes segment lengths of individual faults, unlike the previously published datasets. Hence, we studied both the dependence of fault segment length and accumulated fault length on displacement. The latter relation (accumulated fault length versus displacement) shows a general agreement (positive correlation and power-law relation) with the previously published results that are mainly obtained from outcrop studies, although the slopes vary for different lithologies. The differences could be attributed to the unique characteristics of our dataset that includes data of all segment lengths of individual faults.
Highlights
Fault geometric attributes such as fault displacement (D) and length (L) and their relations have been previously studied through statistical analysis of data coming from different data sources such as geological field studies, mines, interpreted seismic data, satellite images, and cores [1,2,3,4,5,6,7]
We present results from the statistical analysis of the relation between fault length and displacement for the database that includes datasets of 21 normal faults, which encompasses many fault segments as part of the same fault at each depth (Figure 2b), and we can investigate the effect of segmentation on the L-D relation
We consider the relationship between fault segment length and maximum displacement values (D) for all studied faults
Summary
Fault geometric attributes such as fault displacement (D) and length (L) and their relations have been previously studied through statistical analysis of data coming from different data sources such as geological field studies, mines, interpreted seismic data, satellite images, and cores [1,2,3,4,5,6,7]. There are limitations inherent in almost all measuring methods These limitations affect the statistical distribution of such data [4,6]. A fault is usually segmented, which means that the length of each single segment and the variation in displacement along the segment should be honored and contribute to the total length of the fault and its accumulated displacement. These details can be studied for seismic-scale faults using fault obtained from high-quality seismic cubes [9,10]
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