The impact of weathering upon the roughness characteristics of a splay of the active fault system responsible for the massive 2016 seismic sequence of the Central Apennines, Italy

Abstract

Fault roughness constitutes a key element in the understanding of earthquake nucleation, and surficial asperities on the fault plane play a critical role in slip dynamics and frictional behavior during the seismic cycle. Since it is not generally feasible to recover fault roughness profiles or maps directly at the seismogenic sources, faults at the Earth’s surface are typically used as analogues. However, these analogue fault surfaces are often subjected to weathering and erosion, which in turn, reduces their representativeness as seismogenic faults. Rupture along active faults episodically exposes “fresh” fault planes at the Earth’s surface, which represent the best available targets for the evaluation of fault roughness generated at seismogenic depths. Here we present a study conducted on a splay of the Mt. Vettore fault system in the Central Apennines, Italy, along a vertical transect that includes both a weathered and freshly exposed portion of the fault. The latter was exposed after the dramatic Mw 6.5 shock that hit the area on 30 October 2016. We have produced a highly detailed model (i.e., point cloud) of a section of the fault using structure from motion-multiview stereo photogrammetry to assess its roughness parameters (i.e., the Hurst fractal parameter) and to determine the extent to which these parameters are affected by weathering assuming that they had similar fractal characteristics when reaching the surface. Our results show that weathering can modify the value of the fractal parameters. In particular, by independently analyzing different patches of the fault, we have observed that the smoother and recently exposed portions have an average Hurst exponent of 0.52 while the average Hurst exponent of zones with more prolonged exposure times is 0.64. Accordingly, we conclude that by using high-resolution point clouds, it is possible to recognize patches of faults having a similar intensity of deterioration attributable to weathering.

‘Conjugate’ coseismic surface faulting related with the 29 December 2020, Mw 6.4, Petrinja earthquake (Sisak-Moslavina, Croatia)

“…As observations of coseismic surface effects are of considerable scientific importance, it is necessary to carry out the surveys as soon as possible. As a matter of fact, surface effects may be erased by degradation of fault scarps or by road/infrastructure repair, as well as overprinted by postseismic afterslip [21][22][23] . The engineers of Croatian Water Management Department started the survey of surface effects immediately after the main shock, while a working group represented by eight researchers of different Italian institutions (University of Camerino, ISPRA, CNR, DPC) began surveying the ground coseismic effects on 10 January 2021, working 8 hours a day per person for the following 8 days.…”

Virtual Outcrops in a Pocket: The Smartphone as a Fully Equipped Photogrammetric Data Acquisition Tool

“…4C). In this regard, the same fault was mapped in 2016 (Corradetti et al, 2021), using 640 images (4272 × 2848 pixels) taken with a Canon EOS 450D reflex mounted on a tripod to suppress motion blur. The reconstructed area for the Reflex Model was ~2.67 m 2 , and the point cloud included ~2.7 × 10 8 points.…”

“…The survey method proposed herein was performed on an outcrop of an active normal fault located within the Apennines, central Italy. A high-resolution 3D surface reconstruction of the outcrop is already available (Corradetti et al, 2021), thus allowing us to compare our results with a ground-truth model. The area contains outcropping Mesozoic rocks affected by active normal faulting.…”

Quantification of Fracture Roughness by Change Probabilities and Hurst Exponents

“…Various methods are applied for determining fractal parameters, including Fourier power spectrum (e.g., Sagy et al 2007;Bistacci et al 2011;Candela et al 2012;Renard et al 2013;Corradetti et al 2017Corradetti et al , 2020, variogram analysis (e.g., Huang et al 1992;McClean and Evans 2002), structure function (e.g., Poon et al 1992;Odling 1994), or the box counting method (e.g., Malinverno 1990). Regardless of the methodology used, it should be noted that a correct interpretation of fractal parameters requires self-affinity, or at least some kind of scale invariance.…”