Holocene Fault Reactivation in the Eastern Cascades, Washington

2018 ◽  
Vol 108 (5A) ◽  
pp. 2614-2633
Author(s):  
Benjamin M. Carlson ◽  
Elizabeth R. Schermer ◽  
Colin B. Amos ◽  
William J. Stephenson ◽  
Brian L. Sherrod ◽  
...  
Keyword(s):  
Fault Reactivation ◽  
Eastern Cascades

Stress perturbation caused by multistage hydraulic fracturing: Implications for deep fault reactivation

2021 ◽  
Vol 141 ◽  
pp. 104704
Author(s):  
Mengke An ◽  
Fengshou Zhang ◽  
Egor Dontsov ◽  
Derek Elsworth ◽  
Hehua Zhu ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Fault Reactivation ◽  
Deep Fault ◽  
Multistage Hydraulic Fracturing

Normal fault reactivation during multiphase extension: Analogue models and application to the Turkana depression, East Africa

2021 ◽  
pp. 228870
Author(s):  
Liang Wang ◽  
Daniele Maestrelli ◽  
Giacomo Corti ◽  
Yaoyao Zou ◽  
Chuanbo Shen
Keyword(s):  
East Africa ◽  
Normal Fault ◽  
Fault Reactivation ◽  
Analogue Models

scholarly journals Fault reactivation and strain partitioning across the brittle-ductile transition

Geology ◽  
2019 ◽  
Vol 47 (12) ◽  
pp. 1127-1130 ◽  
Author(s):  
Gabriel G. Meyer ◽  
Nicolas Brantut ◽  
Thomas M. Mitchell ◽  
Philip G. Meredith
Keyword(s):  
Fault Slip ◽  
Fault Reactivation ◽  
Ductile Deformation ◽  
Tectonic Deformation ◽  
Gradual Change ◽  
Bulk Rock ◽  
Total Deformation ◽  
Bulk Strain ◽  
Brittle Ductile Transition ◽  
Ductile Flow

Abstract The so-called “brittle-ductile transition” is thought to be the strongest part of the lithosphere, and defines the lower limit of the seismogenic zone. It is characterized not only by a transition from localized to distributed (ductile) deformation, but also by a gradual change in microscale deformation mechanism, from microcracking to crystal plasticity. These two transitions can occur separately under different conditions. The threshold conditions bounding the transitions are expected to control how deformation is partitioned between localized fault slip and bulk ductile deformation. Here, we report results from triaxial deformation experiments on pre-faulted cores of Carrara marble over a range of confining pressures, and determine the relative partitioning of the total deformation between bulk strain and on-fault slip. We find that the transition initiates when fault strength (σf) exceeds the yield stress (σy) of the bulk rock, and terminates when it exceeds its ductile flow stress (σflow). In this domain, yield in the bulk rock occurs first, and fault slip is reactivated as a result of bulk strain hardening. The contribution of fault slip to the total deformation is proportional to the ratio (σf − σy)/(σflow − σy). We propose an updated crustal strength profile extending the localized-ductile transition toward shallower regions where the strength of the crust would be limited by fault friction, but significant proportions of tectonic deformation could be accommodated simultaneously by distributed ductile flow.

Assessing the Accuracy of Fault Interpretation using Machine Learning Techniques when Risking Faults for CO2 Storage Site Assessment

2021 ◽  
pp. 1-55
Author(s):  
Emma A. H. Michie ◽  
Behzad Alaei ◽  
Alvar Braathen
Keyword(s):  
Deep Learning ◽  
New Technologies ◽  
Co2 Storage ◽  
Fault Reactivation ◽  
Machine Learning Techniques ◽  
Close Similarity ◽  
Storage Site ◽  
Site Assessment ◽  
Learning Techniques ◽  
Fault Interpretation

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.

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