SCEC Award Number 22111 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Using 3D seismic data to study damage zones
Name Organization
Emily Brodsky University of California, Santa Cruz
Other Participants Danny Brothers, Jared Kluesner (USGS)
Travis Alongi (UCSC, Graduate Student)
SCEC Priorities 2d, 3a, 3e SCEC Groups FARM, Seismology, Geology
Report Due Date 03/15/2023 Date Report Submitted 12/05/2023
Project Abstract
Fault damage zones provide a window into the non-elastic processes and products of an earthquake, but geological and seismic tomography methods have been unable to measure damage zones at depth with sufficient spatial sampling to evaluate the relative influence of depth, distance, and lithological variations. Here, we identify and analyze the damage zone of the Palos Verdes Fault offshore southern California using two 3D seismic reflection datasets. We apply a novel algorithm to identify discontinuities attributed to faults and fractures in large seismic volumes and examine the spatial distribution of fault damage in surrounding sedimentary rock. Our results show that damage through fracturing is most concentrated around mapped faults and decays exponentially to a distance of 2.2 km, where fracturing reaches a clearly defined and relatively undamaged background for all examined depths and lithologies. This decrease in fracturing with distance from the central fault strand exhibits similar functional form to outcrop studies. The older and deeper units have higher levels of background fracturing and shallower decays of fracturing with distance from the fault. Surprisingly, these differences in damage decay and background level trade-off result in a consistent damage zone width regardless of lithology or depth. We find that the damage zone has similar decay trends on both the east and west side of the fault, and when examining the damage zone at shorter along strike distances that the damage zone has a more complex decay trend and at least two strands are resolvable.
Intellectual Merit Earthquakes release accumulated elastic strain energy and consume part of that energy in the fracturing of rock both on the fault and in the surrounding damage zone. The damage zone is broadly defined as the area where fracture density is higher than the surrounding background fracture density (Chester and Logan, 1986; Kim et al., 2004; Mitchell and Faulkner, 2009; Faulkner et al., 2010) and forms a halo of increased fracturing around the highly localized principal slip surface (Chester et al., 1986; Caine et al., 1996; Choi et al., 2016). Damage zones are significant in earthquake physics for at least four reasons. First, the damage process itself is potentially a sink of energy during earthquake rupture (Wong, 1982; Martel and Pollard, 1989 Chester et al., 2005; Wilson et al., 2005; Abercrombie and Rice, 2005; Brodsky et al., 2020). Constraining the extent of the damage zone at depth is important for evaluating the energy budget of the fault system. Secondly, damage zones are highly permeable and particularly important in controlling the distribution and mobility of fluids around faults (Caine et al., 1996). Since fluid pressure can be a major factor in earthquake nucleation (Hubbert & Ruby, 1959), understanding the structure of the damage zone is a prerequisite for modeling how fluid flow can contribute to initiating and propagating earthquakes. Thirdly, the rheology of the damage zone is distinct from the surrounding media and plastic deformation in the damage zone can alter the rupture dynamics of an earthquake (Dunham, 2011; Thakur et al., 2020). Finally, the extent of the damage zone reflects the aggregate seismic deformation across a particular fault and thus could potentially be used to guide hazard investigations. ed . Offset features are typically measured over a relatively narrow width on either side of the fault and may not capture all of the coseismic deformation and thus damage provides a potentially important alternative window into the seismic history. Establishing the extent of the damage zone in 3D space is therefore an important goal for both fundamental science and pragmatic reasons.
Despite its importance, basic knowledge of the systematics of the 3D damage zones is limited, and much of what is known is from geologic outcrop exposures and related observations (Scholz et al., 1993; Wilson et al., 2003; Shipton et al., 2006; Mitchell & Faulkner, 2009; Savage & Brodsky, 2011 Keren & Kirkpatrick, 2016). In these studies, the damage zone width has been shown to scale nonlinearly with various fault parameters such as length, displacement or throw, and number of strands. (Childs et al., 1997; Cowie and Shipton, 1998; Savage and Brodsky, 2011; Torabi and Berg, 2011). Outcrop studies are limited to surficial measurements of fracture density and lack the means to quantify the damage in situ at depth. Limited availability of fault exposure has made it difficult to disentangle the contributions of lithology, depth, and distance from the fault. Undoubtedly all three factors play a role in controlling the relative damage, but they are seldom separable in outcrop studies.
Passive seismic data have provided some insights on the in-situ fault damage zones. Some studies have shown a reduction in seismic body wave velocities near faults and were interpreted to be due to reduced elastic moduli, a proxy for damage (Ben-Zion et al., 2003; Vidale & Li, 2003; Cochran et al., 2009;). However, these studies are limited by access and deployment logistics on land as well as sparse and often clustered earthquake sources to sample the fault zones. Extent and velocity changes often trade-off in inversion methods, thus establishing variations with depth, distance and lithology are again challenging by these methods.
Active source marine seismic data may provide a means to improve the situation and provide a higher-resolution view of the faulted damage zone and do not rely on proxies to infer elastic moduli, and instead provide a more direct approach for detecting faults and fractures. 3D Seismic reflection techniques, typically used in hydrocarbon exploration, have long been used to infer faulting in-situ through offsets in reflectors. Recently similarity attributes have been used to improve and guide the interpretation of faults in seismic data (Bahorich and Farmer, 1995; Marfurt et al., 1998; Chopra and Marfurt, 2005). These methods use measures of multi-trace similarity over a moving window, and these methods have been tested and agree with forward modeled synthetic faults in seismic volumes (Botter et al., 2016). Faults have been identified using similarity attribute methods (Iacopini et al., 2016) and subsequently used to study the fault damage zone on fault perpendicular cross sections in seismic volumes (Alaei and Torabi, 2017; Liao et al., 2019; Ma et al., 2019). Additionally, machine learning approaches have implemented supervised neural networks to set the weights of ensembles of discontinuity detecting attributes to highlight faults and possible fluid pathways associated with fault junctions (Kluesner and Brothers, 2016).
Here we use existing 3D and 2D marine seismic data along the Palos Verdes Fault (Figure 1) and a modern fault detection and localization algorithm to extract a 3D fault network from the data. Prior studies have suggested that fracture density follows well-defined statistical distributions that need to be well-sampled in order to be quantified (Mitchell & Faulkner, 2009; Savage and Brodsky, 2011). Active seismics provide a powerful way to define these distributions using averaging in large volumes to seek generalizable behavior. We follow this approach by detecting fractures, measuring systematics, averaging volumes and then pursuing the spatial and lithological controls on damage.
Broader Impacts This work supported the PhD of Travis Alongi, who has recently graduated and taken an Mendenhall position at the USGS. The work stimulated a new collaboration between the Coastal Science Center of the USGS and UC Santa Cruz.
Exemplary Figure Figure 3 The left side of each figure shows a representative example of dip-steered diffusion filtered seismic inline 1340 data in black and white color scale (line location shown in Figure 1). The overlain vertical purple line represents the manually mapped central strand. The transparent to red is the thinned fault likelihood fault detections and transparency indicated. The multi-color horizontal lines are mapped 3D horizon-surfaces and mark lithological contacts or unconformities that have been tied to well logs (PL - Pico lower, RU - Repetto upper, RL - Repetto lower, MD - Monterey Delmontian, MM - Monterey Mohnian). These horizons are used as upper and lower bounds to constrain the fracture probability as a function of distance away from the fault for each lithology in full 3D space, not just at the example inline. (A) are the Chevron volume results, which are shown in the semi-log plots on the right of the seismic line, colors match the lithology units indicated on the seismic line, and data are averaged over - 17 km along strike. (B) shows the results for the Shell volume, and are averaged over 4.5 km. Note the different exponential fit slopes and background for each geologic unit (horizontal portion).