Gaspard Farge, University of California, Santa Cruz

Tectonic tremor tracks the repeated slow rupture of certain major plate boundary faults. One of the most perplexing aspects of tremor activity is that some fault segments produce strongly periodic, spatially extensive tremor episodes, while others have more disorganized, asynchronous activity. Here we measure the size of segments that activate synchronously during tremor episodes and the relationship to regional earthquake rate on major plate boundaries. Tremor synchronization in space seems to be limited by the activity of small, nearby crustal and intraslab earthquakes. This observation can be explained by a competition between the self-synchronization of fault segments and perturbation by regional earthquakes. Our results imply previously unrecognized interactions across subduction systems, in which earthquake activity far from the fault influences whether it breaks in small or large segments.

Tina Dura, Virgina Tech

Climate-driven sea-level rise is increasing flood risks worldwide, but sudden land subsidence from great (>M8) earthquakes remains an overlooked factor. Along the Washington, Oregon, and northern California coasts, the next Cascadia subduction zone (CSZ) earthquake could cause 0.5-2 m of rapid subsidence, dramatically expanding floodplains and exposing communities to heightened flooding hazards.

This talk explores the coastal geologic methods used to estimate coseismic subsidence along the CSZ, and then quantifies potential floodplain expansion across 24 Cascadia estuaries under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios—both today and by 2100, when compounded by climate-driven sea-level rise. We will also explore the implications for residents, infrastructure, and decision-makers preparing for the intersection of seismic and climate hazards.

Rick Aster, Colorado State University

The long-period seismic background microseism wavefield is a globally visible signal that is generated by the incessant forces of ocean waves upon the solid Earth and is excited via two distinct source processes. Extensive continuous digital seismic data archives enable the analysis of this signal across nearly four decades to assess trends and other features in global ocean wave energy. This seminar considers primary and secondary microseism intensity between 4 and 20 s period between 1988 and late 2024. 73 stations from 82.5 deg. N to 89.9 deg. S latitude with >20 years of data and >75% data completeness from the NSF/USGS Global Seismographic, GEOSCOPE, and New China Digital Networks. The primary microseism wavefield is excited at ocean wave periods through seafloor tractions induced by the dynamic pressures of traveling waves where bathymetric depths are less than about 300 m. The much stronger secondary wavefield is excited at half the ocean wave period through seafloor pressure variations generated by crossing seas. It is not restricted to shallower depths but is sensitive to acoustic resonance periods in the ocean water column. Acceleration power spectral densities are estimated using 50%-overlapping, 1-hr moving windows and are integrated in 2-s wide period bands to produce band-passed seismic amplitude and energy time series. Nonphysical outliers, earthquake signals, and Fourier series seasonal variations (with a fundamental period of 365.2422 d) are removed. Secular period-dependent trends are then estimated using L1 norm residual-minimizing regression. Increasing microseism amplitude is observed across most of the Earth for both the primary and secondary microseism bands, with average median-normalized trends of +0.15 and +0.10 %/yr, respectively. Primary and secondary band microseism secular change rates relative to station medians correlate across global seismic stations at R=0.65 and have a regression slope of 1.04 with secondary trends being systematically lower by about 0.05 %/yr. Multiyear and geographically extensive seismic intensity variations show globally observable interannual climate index (e.g., El Niño–Southern Oscillation) influence on large-scale storm and ocean wave energy. Microseism intensity histories in 2-s period bands exhibit regional to global correlations that reflect ocean-basin-scale teleconnected ocean swell, long-range Rayleigh wave propagation, and the large-scale reach of climate variation. Global secular intensity increases in recent decades occur across the entire 4 – 20 s microseism band and progressively greater intensification at longer periods, consistent with more frequent large-scale storm systems that generate ocean swell at the longest periods.

Chris Johnson, Los Alamos National Lab

Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Applying these machine learning models typically require task-specific labeled data for training and tuning for experimental results or a region of interest, thus limiting the generalization and robustness when broadly applied. Foundation models diverge from labeled data training procedures and are widely used in natural language processing and computer vision. The primary different is these models learn a generalized representation of the data, thus allowing several downstream tasks performed in a unified framework. Here we apply the Wav2Vec 2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kilauea volcano on the island of Hawai'i. We pre-train the Wav2Vec 2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

Betsy Madden, San Jose State University

Seismic hazard assessments currently depend on fault slip rates, the cumulative offset over many earthquakes along individual faults, to determine the probability of earthquakes of a certain magnitude over a certain time period and potential ground motions. Geologic fault slip rates are estimated by a combination of field and laboratory techniques. Such data can be generated synthetically with mechanical models that capture slip rate variations along complex, three-dimensional fault networks. I will discuss opportunities provided by these synthetic data, as well as integration of the results with dynamic rupture models of individual earthquakes.

James Atterholt, USGS

Observations of broad-scale lithospheric structure and large earthquakes are often made with sparse measurements and are low resolution. This makes interpretations of the processes that shape the lithosphere fuzzy and nonunique. Distributed Acoustic Sensing (DAS) is an emergent technique that transforms fiber-optic cables into ultra-dense arrays of strainmeters, yielding meter-scale resolution over tens of kilometers for long recording periods. Recently, new techniques have made probing fiber-measured earthquake wavefields for signatures of large-scale deformation and dynamic behavior possible. With fibers in the Eastern California Shear Zone and near the Mendocino Triple Junction, I use DAS arrays to measure a diversity of tectonic-scale phenomena. These include the length scale over which the Garlock Fault penetrates the mantle, the plumbing system of the Coso Volcanic Field at the crust-mantle boundary, the topographic roughness of the Cascadia Megathrust, and the time-dependent rupture velocity of the 2024 M7 Cape Mendocino earthquake. Dense measurements vastly improve the clarity with which we can view these processes, offering new insights into how the lithosphere evolves and what drives the behavior of large earthquakes.

Doron Morad, University of California, Santa Cruz

In natural fault surfaces, stresses are not evenly distributed due to variations in the contact population within the medium, causing frictional variations that are not easy to anticipate. These variations are crucial for understanding the kinematics and dynamics of frictional motion and can be attributed to both the intact material and granular media accommodating the principal slip zone. Here, I explore the effects of heterogeneous frictional environments using two different approaches: fracture dynamics on non-mobilized surfaces and granular systems on mobilized ones.

First, I will present a quantitative analysis of laboratory earthquakes on heterogeneous surfaces, incorporating both laboratory-scale seismic measurements coupled with high-speed imaging of the controlled dynamic ruptures that generated them. We generated variations in the rupture properties by imposing sequences of controlled artificial barriers along the laboratory fault. We first demonstrate that direct measurements of imaged slip events correspond to established seismic analysis of acoustic signals; the seismograms correctly record the rupture moments and maximum moment rates. We then investigate the ruptures’ early growth by comparing their measured seismogram velocities to their final size. We investigate the laboratory conditions that allow final size predictability during the rupture early growth. Due to higher initial elastic energies imposed prior to nucleation, larger events accelerate more rapidly at the rupture onset for both heterogeneous and non-heterogeneous surfaces.

Second, I present a new Couette-style deformation cell designed to study stress localization in two-dimensional granular media under different flow regimes. This apparatus enables arbitrarily large deformations and spans four orders of magnitude in driving velocity, from sub-millimeter to meters per second. Using photoelasticity, we measure force distribution and localization

These two end-member cases of frictional sliding, one dominated by gouge, and the second by intact surfaces, highlight two fundamental aspects of friction dynamics. The spatial distribution of heterogeneity directly influences stress distribution and, consequently, the stability of the medium. With these experimental methods, we can now measure and even control these effects.

Cassie Hanagan, USGS

Advancing our understanding of earthquake processes inevitably pushes the bounds of data resolution in the spatial and temporal domains. This talk will step through a series of examples leveraging two relatively niche geodetic datasets for understanding portions of the earthquake cycle: (1) temporally dense and sensitive borehole strainmeter (BSM) data, and (2) spatially dense sub-pixel image correlation displacement data. More specifically, I will detail gap-filling benefits of these two datasets for different earthquakes.

BSMs respond to a frequency of deformation that bridges the capabilities of more common GNSS stations and seismometers. As such, they are typically installed to capture deformation signals such as slow slip or transient creep. In practice they are also useful for measuring dynamic and static coseismic strains. This portion of the talk will focus on enhanced network capabilities for detecting both coseismic and postseismic deformation with a relatively new BSM array in the extensional Apennines of Italy, with events spanning tens to thousands of kms away. Then, we will transition toward how these instruments can constrain spatiotemporally variable afterslip following the 2019 Mw7.1 Ridgecrest, California earthquake.

High spatial resolution displacements from sub-pixel image correlation serve as gap-filling datasets in another way – providing higher spatial resolution (~0.5 m) maps of the displacement fields than any other method to date, and patching areas where other methods fail to capture the full deformation magnitude or extent, such as where InSAR decorrelates. This portion of the talk will focus on new results that define expected displacement detection thresholds from high-resolution satellite optical imagery and, alternatively, from repeat lidar data. Examples will include synthetic and real case studies of discrete and diffuse deformation from earthquakes and fault creep.

Evan Hirakawa, USGS

Northern California, specifically the San Francisco Bay Area, is a great place to study earthquake hazards and risk, due to its dense population centers surrounded by active faults, as well as complex geology that strongly influences earthquake ground motions. Computer simulations of seismic wave propagation which can incorporate 3D models of the subsurface properties and complex faulting behavior are good tools for studying seismic hazard, but ultimately require more development before unlocking full potential; specifically, the 3D seismic velocity models need to be further developed in many places and the simulated motions need to be validated with real, recorded data.

In this talk, I will summarize a few different research projects on these topics. First I will review recent efforts to improve the USGS San Francisco Bay region 3D seismic velocity model (SFCVM), the leading community velocity model in the area, and describe some of its interesting features. This will be followed by a preview of ongoing work from collaborators and some other promising avenues to explore, in hopes of further improving the model and stoking more community involvement. In the second part of the talk, I will switch gears and move farther north, to the Humboldt County area, where a recent M7 earthquake occurred offshore. I will show some preliminary modeling results, discuss the datasets available from this event, and describe some of the local geology and efforts to better understand subsurface structure.

Omar Issa, ResiQuant (Co-Founder)/Stanford University

A study by FEMA suggests that 20-40% modern code-conforming buildings would be unfit for re-occupancy following a major earthquake (taking months or years to repair) and 15-20% would be rendered irreparable. The increasing human and economic exposure in seismically active regions emphasize the urgent need to bridge the gap between national seismic design provisions (which do not consider time to recovery) and community resilience goals.

Recovery-based design has emerged as a new paradigm to address this gap by explicitly designing buildings to regain their basic intended functions within an acceptable time following an earthquake.

This shift is driven by the recognition that minimizing downtime is critical for supporting community resilience and reducing the socioeconomic impacts of earthquakes. This seminar presents engineering modeling frameworks and methods to support scalable assessment and optimization of recovery-based design, including:

1. Procedures for selection and evaluation of recovery-based performance objectives and study the efficacy of user-defined checking procedures.

2. A framework to rapidly optimize recovery-based design strategies based on user-defined performance objectives.

3. Building technology to support utilization of these approaches across geographies and industrial verticals.

Together, these contributions provide the technical underpinnings and industry-facing data requirements to perform broad, national-scale benefit-cost analysis (BCA) studies that can accelerate decision-making and engineering intuition as resilient design progresses in the coming years.