Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs

Clustering of earthquake magnitudes is still actively debated, compared to well-established spatial and temporal clustering. Magnitude clustering is not currently implemented in earthquake forecasting but would be important if larger magnitude events are more likely to be followed by similar sized events. Here we show statistically significant magnitude clustering present in many different field and laboratory catalogs at a wide range of spatial scales (mm to 1000 km). It is universal in field catalogs across fault types and tectonic/induced settings, while laboratory results are unaffected by loading protocol or rock types and show temporal stability. The absence of clustering can be imposed by a global tensile stress, although clustering still occurs when isolating to triggered event pairs or spatial patches where shear stress dominates. Magnitude clustering is most prominent at short time and distance scales and modeling indicates >20% repeating magnitudes in some cases, implying it can help to narrow physical mechanisms for seismogenesis.

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EGS Permeability/Flow Imaging and Optimization

Permeability/Flow imaging is needed as a first step to mitigate the impacts of ubiquitous flow heterogeneity/channeling that exists within geothermal systems. Initial focus is on imaging flow and permeability fields using large specimen sizes (> 30 cm) and realistic yet variable true-triaxial stresses (external stresses up to 30 MPa) and temperatures (up to 200°C). These experiments will also focus on thermal performance prediction and dynamic optimization of circulation using graph theory and operational surface controls.

Finding Seismicity Within the Noise of Fiber Optic DAS

Earthquake (EQ) signal detection and phase picking are challenging problems in seismology, where detection refers to identifying the EQ signals within a dataset that consists of noise and non-EQ signals and phase picking refers to the identification of distinct seismic phases, such as P-wave and S-wave, within an EQ signal. Deep Learning (DL) can be used for detection/picking because of learning based on high-level representations of the available data streams. In large datasets such as DAS, current computing restrictions require high-level representations of the data to extract usable information on seismicity, where specific regions of the seismic signals will be given more focus than others. This can be accomplished with replicating the way in which humans interpret their visual field of view. Specifically, attention mechanisms can be used in DL to replicate the efficient manner in which humans can identify changes in space and time.

Linking Geophysical Microcrack Observations with Rock Damage Evolution

The goal of the proposed project is to identify and link critical microcrack parameters to macroscale physical property behavior to provide a method for which to interpret and predict the evolution of rock/concrete behavior under various loading conditions (i.e., stress and stress path). Laboratory investigations to separate the macro-scale contributions of microcrack parameters will be used to analyze the dependency of damage on stress and stress path. The deconvolution of microcrack parameter controlling mechanisms to macro-scale behavior will help put site-specific observations of geologic/civil structure damage into perspective, thereby providing a context for rock/concrete behavior prediction due to microcracking.

Non-local Triggering in Rock Fracture

Combining multiphysics observations in the laboratory, we present novel phenomena for the analogue between rock fracture and seismicity. We show, for the first time in a laboratory setting, how a large‐scale flaw in rock can facilitate “non‐local triggering”—remotely triggered damage in rock mass. Results prove analogues of rock fracture evolution to natural seismicity including several lab‐analogues on seismicity model predictions beyond power law. We observe under specimen‐scale criticality a relatively small AE perturbation occurring at a seemingly random location can trigger cascading events whose spatial span can traverse the entire rock fracture system. The inclination angle of the non‐local triggering pairs shows dependence on the large‐scale flaw, and the subsequent generations of triggered AEs by these non‐local triggers decline significantly slower than that of the general triggers. The rock fracture system evolves to a state where the resistance to non‐local triggering continuously reduces, bridging the gap between an idealized lab setting and a natural fault. The non‐locally triggered events can also be reactivated as the rock fracture system evolves towards increased complexity. These lab‐observations as well as the understanding on the analogue relationship between rock fracture and seismicity may shed new light on the insight of the periodical, dynamic, and unpredictable nature of the multiscale rock fracture process.

Microcrack Damage Observations via Acoustic Emission

Hydraulic fracturing is a common practice in several industries and environments, including energy production using enhanced geothermal systems, hydrocarbon extraction from unconventional oil and/or gas reservoirs, and mining and civil engineering excavation methods. Understanding damage related to the coalesced fractures induced by hydraulic fracturing and the surrounding material is fundamental when efforts to predict material and system behavior is sought. Inducing fracture networks in rock can create large amounts of microcracking in surrounding regions that are not connected to the wellbore. The degree of microcracking can vary depending on fluid, rock type, stress/temperature boundary conditions, as well as inherent material properties and heterogeneities. Regions of rock containing microcracks near coalesced macro-scale fractures can behave differently than the original matrix material due to the permanent structural changes. Understanding of how the coalesced fractures can interact with the surrounding rock containing microcracks requires the characterization of damage in terms of physical property evolution.

CT images of a laboratory hydraulic fracture (Hampton et al, 2018).

Linking Physical Measurements with Imaging

The use and integration of many physical measurements is crucial for understanding the impact of changing boundary conditions. For example, acoustic emissions observed throughout a laboratory fracture test can provide an immense amount of information regarding the microcracking processes and damage, but the nature of the coalesced fracture, it’s roughness, conductivity, or opening width is not readily captured with the AE method. Other geophysical or imaging techniques must be integrated to provide a full characterization of the deformation. Conversely, using CT images alone will not describe the progressive nature of the fracturing process. This research studies the integration of many physical measurements and imaging techniques for an enhanced understanding of fracture development and interaction with existing discontinuities and matrix material.

Acoustic emission density and measured permeability as a function of distance from a coalesced fracture (Hampton et al, 2019).

Influence of Damage on Permeability

Understanding the role of rock damage in the form of microfractures, compaction and dilation bands, and alteration of the geochemical make-up of rocks is fundamental for subsurface engineering and safe energy extraction and sustainable reservoir development. Currently, knowledge is lacking in the characterization of damage across multiple length scales and its influence on material behavior. In subsurface engineering of oil/gas extraction, rock damage is purposely induced to achieve specific goals, but the role of micro-scale damage on the performance of these systems is not well understood. Consequences of poorly characterized rock damage and the subsequent rock mass physical property changes can lead to poor reservoir development practices, lower production, and lost time and investment.