Abrupt, local frictional fault failure comprises a displacement that is normally accompanied by acoustic emission (AE)-an impulsive elastic wave broadcast with an amplitude proportional to particle velocity. The aggregate of these displacements is the basic fault motion. In laboratory shear experiments, the examination of a sequence of laboratory earthquakes includes continuous measurements of fault motion and the associated AE that is broadcast. From these measurements, connections between the fault motion and cumulative sum of the AE amplitude can be identified. The composition of the AE broadcasts reveals inhomogeneity in the fault mechanical structure from which they arise. This inhomogeneity can be decomposed into a time invariant AE component and an articulated AE component. The articulated AE component serves as a "state of the fault diagnostic" that follows a distinctive pattern to fault failure. Thus, the articulated AE component can be used directly to monitor the state of the fault.

Characterizing fluid flow in a porous and permeable material is fundamental to energy and hydrological applications, yet direct measurements of permeability are very difficult to conduct in situ. However, attending fluid flow through a material are various mechanical responses, e.g., strain fields and acoustic emissions, and these mechanical responses may hold important clues to the fluid flow in the material, specifically the permeability. Here we report results from a numerical study of fluid flow through a channel, defined by confining side blocks, that contains a particle bed. For a range of inlet velocities, we study the strain and acoustic emission in the confining side blocks. The simulations are repeated for different configurations of the particle bed. We find a one-to-one correspondence between strain and acoustic emission and the quantities that determine the permeability. Thus, strain and acoustic emission may serve as ingredients in an unconventional scheme for remote monitoring to learn the permeability.

Characterizing fluid flow in a porous material with permeability is fundamental to energy and hydrological applications, yet direct measurements of permeability are very difficult to conduct in situ. However, attending fluid flow through a material are various mechanical responses, e.g., strain fields, acoustic emission. These mechanical responses may hold important clues to the fluid flow in the material, to the permeability. Here we report results from a numerical study of fluid flow in a channel, defined by confining side blocks, that contains a particle bed. For a range of inlet velocities, we study the strain and acoustic emission in the side blocks. Simulations are repeated for different configurations of the particle bed. We find that the observed mechanical response accords with an analytic model of this system, providing promising evidence for using mechanical measurements, particularly strain and acoustic emission, as surrogates for direct measurement of permeability.

Rocks are heterogeneous materials that exhibit nonlinear elastic (anelastic) behavior at scales ranging from the laboratory to Earth. In the laboratory, typical, complex relationships exist between stress and strain that include hysteresis, finite relaxation times, strain rate, and history dependence. These behaviors are linked to important characteristics such as stress, porosity, permeability, material integrity, and material failure. We adopted a “pump‐probe” type experiment common in laboratory studies, using solid earth tides as the low‐frequency pump and empirical Green's function as the high‐frequency probe. By probing the velocity at different points in the pump cycle, we constrained important information about the strain‐modulus relationship. Near the San Andreas Fault, we observed strongly nonlinear elastic behavior that characterizes the damage zone. We also constrained important aspects of hysteretic behavior that are related to damage properties and possibly pore pressure. Away from the fault, the nonlinear behavior is diminished.

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults monitor earthquakes and augment current early warning systems.

The stress tensor is an important property for upper crustal studies such as those that involve pore fluids and earthquake hazards. At tectonic plate scale, plate boundary forces and mantle convection are the primary drivers of the stress field. In many local settings (10–100 s of km and <10 km depth) in tectonic plate interiors, we can simplify by assuming a constant background stress field that is perturbed by local heterogeneity in density and elasticity. Local stress orientation and sometimes magnitude can be estimated from earthquake and borehole‐based observations when available. Modeling of the local stress field often involves interpolating sparse observations. We present a new method to estimate the 3D stress field in the upper crust and demonstrate it for Oklahoma. We created a 3D material model by inverting multiple types of geophysical observations simultaneously. Integrating surface‐wave dispersion, local travel times and gravity observations produces a model of P‐wave velocity, S‐wave velocity, and density. The stress field can then be modeled using finite element simulations. The simulations are performed using our simplified view of the local stress field as the sum of a constant background stress field that is perturbed by local density and elasticity heterogeneity and gravitational body forces. An orientation of N82°E, for the maximum compressive tectonic force, best agrees with previously observed stress orientations and faulting types in Oklahoma. The gravitational contribution of the horizontal stress field has a magnitude comparable to the tectonic contribution for the upper 5 km of the subsurface.

Tracking the state of stress evolution within geothermal reservoirs is important for optimizing energy production and characterizing seismic hazard. Borehole image analysis and hydraulic fracture tests can constrain principal stress directions and magnitudes at discrete locations, but cannot provide a full description of the evolving stress tensor throughout the reservoir. Interferometric synthetic aperture radar (InSAR) observations can reveal high-resolution, reservoir-scale, time-dependent surface displacements that can be used in inversion models to infer subsurface stress sources and changes in the stress tensor. However, such stress inversions are subject to various a priori assumptions, including source type, location, number, or magnitude. We develop a novel method to obtain a description of the evolving stress field that utilizes machine learning (ML) to quantify subsurface stress tensor changes from InSAR observations. We train a convolutional neural network (CNN) with data produced using analytical realizations of subsurface strain and surface displacement from sources representing volumetric and double couple deformation. From the strain values we calculate subsurface stress in the geothermal reservoir. We establish the range of earthquake parameters and fluid pressure sources that produce InSAR-measurable surface displacements and train the CNN to characterize the stress field from surface displacements. In parallel with our CNN solution to the relationship between ground deformation and interior stress, we develop an independent solution employing a variational procedure. These two problem solutions are used interactively to inform the development of each. We demonstrate the application of our ML-based solution for the Coso Geothermal field. The success of our ML-based approach suggests that with generalized training, our method could be employed broadly to geothermal fields to provide field operators with a near real-time model of stress field evolution.

We present experimental and modeling results and a downhole logging tool concept resulting from a research collaboration between Chevron Energy Technology Company and Los Alamos National Laboratory investigating using nonlinear acoustics applications for natural fracture characterization and assessing near-wellbore mechanical integrity or drilling-induced damage. The generation of a scattered wave by noncollinear mixing of two acoustic plane waves in an acoustically nonlinear medium was first documented several decades ago. If the frequency ratio and convergence angle of the two waves and the compressional-to-shear velocity ratio of the medium where they intersect meet certain conditions, their interaction creates a scattered third wave, propagating in a predictable direction, with a frequency equal to the sum or difference between the two primary wave frequencies and an amplitude dependent on the nonlinearity at the intersection location. The conditions resulting in this scattering and the properties of the scattered wave are thus governed by the physics of the interaction, resulting in a set of "selection rules" that are the key to the measurement principle introduced here. If the two transmitted plane waves are oriented such that the third wave returns to the borehole, the phenomenon may be used as the basis for a logging tool measuring acoustic nonlinearity around the wellbore circumference, with a secondary measurement of the compressional-to-shear velocity ratio. Laboratory measurements supported by finite-difference and analytical modeling confirmed that the mixing of two plane compressional waves generated a shear wave as predicted by the selection rules in a large Berea sandstone block, confirming the potential for a downhole tool with a depth of investigation in the range 15 to 20 cm. Historical data show that nonlinearity in core samples is primarily caused by a lack of mechanical integrity. In the oil field, this may be microfractures in tight rock unconventional reservoirs or incipient near-wellbore failure while drilling. This prompts applications to fracture characterization and calibration of mechanical earth models. The main practical challenge for a downhole logging tool is injecting powerful directional acoustic energy into the formation. We envisage an openhole tool making sequential station measurements using transmitters built into hydraulically controlled pads contacting the borehole wall. Noncollinear mixing may be activated by maintaining the frequency of one transmitter constant while sweeping the other through the range of frequency ratios predicted by the selection rules, resulting in a received sum or difference frequency signal that rises to a peak and then falls. Alternatively, the frequency ratio may be maintained while steering one of the acoustic beams. The peak signal amplitude indicates the coefficient of nonlinearity, which is sensitive to lack of mechanical integrity caused by natural fractures or mechanical disaggregation. The frequency ratio at which it occurs is an indicator of the shear-to-compressional velocity at the location where the two beams cross. In this manner, a record of nonlinearity along or around the borehole can be envisaged. The physics of acoustic nonlinearity is well established, and our laboratory measurements have determined that the phenomenon of interest should occur and be measurable in the subsurface. Overcoming the engineering challenges would bring new formation evaluation insights unique to this measurement principle.

Sorption hysteresis in nanoporous polymer is an intriguing phenomenon that involves coupling between sorption and deformation. Based on the mechanism revealed at the microscopic level by use of molecular simulation, a poromechanical model is developed capturing all relevant physics and yielding a quantitative description. In this model, the coupling between sorption and deformation is described by a poromechanics framework. More in detail, an upscaling process from the molecular mechanism is implemented to model the hysteresis through the state change of each element upon deformation. We provide two solutions of the model: a numerical one based on the finite element method and an analytical one based on uniform strain assumption. The results from both solutions agree well with the molecular simulation and experimental results, therefore capturing and describing adequately sorption hysteresis. The developed model illustrates that water forms different structural distributions upon adsorption and desorption. A parametric study shows that sorption hysteresis is influenced by material properties. We find that a softer material with stronger adsorbent-adsorbate interaction tends to exhibit more profound sorption hysteresis. The developed model, which relies on the concepts of sorption-deformation coupling and multiscale modeling from atomistic simulations to domain dependent theory, paves the way for a new direction of modeling sorption hysteresis.

Heat and moisture are known to have important mechanical effects on polymers such as hygric swelling, thermal expansion, and mechanical weakening. A common approach when investigating such effects is to assume the effects of heat and moisture to be similar-the so-called time-temperature-moisture superposition. Through molecular dynamics simulation, this study evaluates the extent of the similarity of the effects of moisture and heat on the hygric swelling, thermal expansion, and mechanical weakening of a biopolymer: an uncondensed type of lignin, one of the most abundant polymers in the plant regime. We introduce as a microscopic metric the local stiffness (T/< u(2)>, temperature divided by the amplitude of segmental motion < u(2)>), to analyze the mechanisms of mechanical effects of heat and moisture. The local stiffness of polymer skeleton and the overall stiffness of the composite material are shown to be strongly correlated, with a Pearson correlation coefficient of 0.96. Under the assumptions of harmonic vibration and isotropy, an explicit equation relating bulk moduli and the local stiffness can be derived, and the theoretically predicted moduli are in good agreement with measurement. The thermal expansion and weakening are shown to be related to each other and both dependent on the local stiffness. The analysis of the potential energy further points out that heating weakens both primary and secondary bonds of the polymer skeleton, while hydration only affects the secondary bonds. This major difference is thought to be the origin of the different impacts of heat and moisture on biopolymer mechanics, offering a different view of the time-temperature-moisture superposition principle.

Plate motion near the fault gouge layer, and the elastic interplay between the gouge layer and the plate under stick-slip conditions, is key to understanding the dynamics of sheared granular fault systems. Here, a two-dimensional implementation of the combined finite-discrete element method (FDEM), which merges the finite element method (FEM) and the discrete element method (DEM), is used to explicitly to simulate a sheared granular fault system. We focus on investigating the influence of normal load, driving shear velocity and plate stiffness on the velocities and displacements measured at locations on the upper and lower plates just adjacent to the gouge in the direction parallel to the shear direction (x direction). The simulations show that at slips the plate velocities are proportional to the normal load and may be inversely proportional to the square root of the plate's Young's modulus; whereas the driving shear velocity does not show distinct influence on the plate velocities. During stick phases, the velocities of the upper and lower plates are respectively slightly greater and slightly smaller than the half of the driving shear velocity, and are both in the same direction of shear. The shear strain rate of the gouge is calculated from this velocity difference between the upper and lower plate during stick phases and thus the gouge effective shear modulus can be calculated. The results show that the gouge effective shear modulus increases proportionally with normal load, while the influence of shear velocity and plate stiffness on gouge effective shear modulus is minor. The simulations address the dynamics of a laboratory scale fault gouge system and may help reveal the complexities of earthquake frictional dynamics.

Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick-slip cycles. These results illustrate that even within a single, well-constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.

An atomistic slit pore model is built to study the sorption-induced deformation of nanoporous materials with the help of molecular simulation. Both sorption and strain isotherms are determined to probe the anisotropic deformation behavior induced upon molecular adsorption. A detailed analysis shows that the driving microscopic mechanisms at different sorption stages are different. At high relative pressure, as expected from the classical macroscopic picture, the pore deformation is governed by the Laplace pressure as the pore gets filled with liquid because of capillary condensation. In such situation, the strain in normal and longitudinal directions can be predicted from the stiffness modulus in the corresponding direction. At low pressure, when liquid films are adsorbed at the pore surfaces and separated by the vapor phase, the strain is driven by the attractive solid fluid forces and in-plane pressure within the film, and the deformation is confined in the direction parallel to the film solid interface. Because of the interplay of the two factors, the strain changes from shrinkage to expansion upon increase of pressure. Analysis of isosteric heat of adsorption shows that the contribution arising from the deformation is small compared to the sorption contribution, which indicates that the influence of deformation on the sorption process is limited.

The use of natural sustainable resources such as wood in green industrial processes is currently limited by our poor understanding of the impact of moisture on their thermodynamic and mechanical behaviors. Here, a molecular dynamics approach is used to investigate the physical response of a typical hydrophilic biopolymer in softwood hemicellulose-xylan-when subjected to moisture adsorption. A unique moisture-induced crossover is found in the thermodynamic and mechanical properties of this prototypical biopolymer with many quantities such as the heat of adsorption, heat capacity, thermal expansion and elastic moduli exhibiting a marked evolution change for a moisture content about 30 wt%. By investigating the microscopic structure of the confined water molecules and the polymer-water interfacial area, the molecular mechanism responsible for this crossover is shown to correspond to the formation of a double-layer adsorbed film along the amorphous polymeric chains. In addition to this moisture-induced crossover, many properties of the hydrated biopolymer are found to obey simple material models.

A numerical scheme using the combined finite-discrete element method is employed to study a model of an earthquake system comprising a granular layer embedded in a formation. When the formation is driven so as to shear the granular layer, a system of stress chains emerges. The stress chains endow the layer with resistance to shear and on failure launch broadcasts into the formation. These broadcasts, received as acoustic emission, provide a remote monitor of the state of the granular layer of the earthquake system.

The seismogenic plate boundaries are presumed to behave similarly to a densely packed granular medium, where fault and blocks systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. We use machine learning and show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model, and discuss the physical basis behind decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. Our work provides novel insights into the applications of machine learning in studying frictional processes that take place in geophysical systems.