Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual earthquakes and play an important role in earthquake monitoring. Dense networks improved picking methods produce massive data sets, particularly for swarms aftershocks occurring closely time space, making challenging problem. We present new method, the Gaussian Mixture Model Association (GaMMA), that combines mixture model measurements (both amplitude), with location, origin...

Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective must determine the number of discrete events, location and origin times, it differentiate real arrivals from measurement artifacts. The advent deep learning pickers, which provide high rates picks closely overlapping small magnitude earthquakes, motivates revisiting problem approaching using methods learning. We have developed a Graph Neural Network associator that simultaneously...

Research Article| January 09, 2019 Pairwise Association of Seismic Arrivals with Convolutional Neural Networks Ian W. McBrearty; McBrearty aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Search for other works by this author on: GSW Google Scholar Andrew A. Delorey; Delorey Paul Johnson Author and Article Information Publisher: Seismological Society America First Online: 09 Jan Online Issn: 1938-2057 Print 0895-0695...

Slow earthquakes may trigger failure on neighboring locked faults that are stressed enough to break, and slow slip patterns evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, associated tremor have only been observed intermittent discrete bursts. By training convolutional neural network detect known single seismic station we isolate identify preceding following larger events. The deep can be used for detection of quasi-continuous tremor, providing...

Reliable seismicity catalogs are essential for seismology. Following phase picking, association groups arrivals into sets with consistent origins (i.e., events), determines event counts, and identifies outlier picks. To handle the substantial increase in quantity of seismic picks from improved picking methods larger deployments, several novel associators have recently been proposed. This study presents a detailed benchmark analysis five associators, including classical machine learning-based...

Abstract The association of phase picks to form events is one the fundamental components seismology. Large and dense sensor networks, such as >1000 geophone arrays (and distributed acoustic sensing), offer unique challenges in due vast numbers observations high likelihood errant picks. In addition, large number stations can greatly increase time it takes perform association. For this reason, machine learning (ML) methods might provide a more optimal method for networks. work, we...

Measurements of volcano deformation are increasingly routine, but constraining complex magma reservoir geometries via inversions surface measurements remains challenging. This is partly due to modeling being limited one two approaches: computationally efficient semi-analytical elastic solutions for simple (point sources, spheroids, and cracks) expensive numerical 3D geometries. Here, we introduce a pair Graph Neural Network (GNN) based elasto-static emulators capable making fast reasonably...

Abstract Laboratory earthquake experiments provide important observational constraints for our understanding of physics. Here we leverage continuous waveform data from a network piezoceramic sensors to study the spatial and temporal evolution microslip activity during shear experiment with synthetic fault gouge. We combine machine learning techniques ray theoretical seismology detect, associate, locate tens thousands events within gouge layer. Microslip is concentrated near center system but...

The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become sensitive, and particularly when earthquake rates are high. For instance, waves arriving across a monitoring network from several sources may overlap in time, false be detected, some unknown phase (e.g., P- or S-waves). We propose an automated method to associate obtain source locations applicable such situations. To do so we use pattern metric based...

Abstract Seismic studies of glaciers yield insights into spatio-temporal processes within and beneath on scales relevant to flow deformation the ice. These methods enable direct monitoring bed in ways that complement other geophysical techniques, such as geodetic or ground penetrating radar observations. In this work, we report analysis passive seismic data collected from interior North East Greenland Ice Stream, ice sheet's largest outlet glacier. We record thousands basal earthquakes, many...

We solve the traditional problems of earthquake location and magnitude estimation through a supervised learning approach, where we train Graph Neural Network to predict estimates directly from input pick data, each allows distinct seismic network with variable number stations positions. model using synthetic simulations assumed travel-time amplitude-distance attenuation models. The architecture uses one graph represent station set, another space. includes theoretical predictions given...

Marine turbidite paleoseismology relies on the assumption of synchronous triggering turbidity currents by earthquake shaking to infer rupture extent and recurrence. Such inference commonly depends age dating correlation physical stratigraphy deposits carried (i.e., turbidites) across great distances. Along Cascadia subduction zone, which lies offshore Pacific Northwest, USA, facies in core photographs, X-ray computed tomography images, magnetic susceptibility (MS) data exhibit differences...

SUMMARY The retrieval of earthquake finite-fault kinematic parameters after the occurrence an is a crucial task in observational seismology. Routinely used source inversion techniques are challenged by limited data coverage and computational effort, subject to variety assumptions constraints that restrict range possible solutions. Back-projection (BP) imaging do not need prior knowledge rupture extent propagation, can track high-frequency (HF) radiation emitted during process. While classic...

In seismology, accurately associating seismic phases to their respective events is crucial for constructing reliable seismicity catalogs. This study presents a comprehensive benchmark analysis of five phase associators, including machine learning based solutions, employing synthetic datasets tailored replicate the characteristics real data in crustal and subduction zone scenario. The were generated using NonLinLoc raytracer, station distributions velocity models simulating large range across...

Eruptions of Piton de la Fournaise volcano (Reunion Island, France) are preceded by intense pre-eruptive seismicity swarms characterized hundreds, or even thousands micro earthquakes (magnitude < 2). These volcano-tectonic events triggered the upward migration magma toward surface and their location provides important information regarding future eruption location. Yet, large number earthquakes, it is difficult to locate them all during swarms. Hence, we have implemented an approach at...

The seismic sequence off the coast of Mayotte island, in Comoros archipelago, preceded and accompanied large submarine volcanic eruption birth volcano Fani Maoré. While this has slowed down stopped, it is still active captures deep complex system new its evolution. seismicity separated two clusters. distal cluster located about 25 km South-East been associated with magma propagation towards surface. proximal cluster, 10 East Mayotte, suggests presence several magmatic reservoirs...

During the three month long eruption of K\=ilauea volcano, Hawaii in 2018, pre-existing summit caldera collapsed over 60 quasi-periodic failure events. The last 40 these events, which generated Mw >5 very period (VLP) earthquakes, had inter-event times between 0.8 - 2.2 days. These events offer a unique dataset for testing methods predicting earthquake recurrence based on locally recorded GPS, tilt, and seismicity data. In this work, we train deep learning graph neural network (GNN) to...

ABSTRACT On 6 February 2023, a devastating earthquake doublet consisting of Mw 7.8 and 7.6 events separated by about 9 hr struck the southeastern part Türkiye. The developing aftershock sequence contained thousands during first few days overwhelmed routine algorithms handling their detection location. In addition, several stations temporarily lost real-time contact came online again later. At same time Omori decay event rate reduced frequency allowed for inclusion progressively...

Abstract During the 3 month long eruption of Kı̄lauea volcano, Hawaii in 2018, pre‐existing summit caldera collapsed over 60 quasi‐periodic failure events. The last 40 these events, which generated Mw > 5 very period (VLP) earthquakes, had inter‐event times between 0.8 and 2.2 days. These events offer a unique data set for testing methods predicting earthquake recurrence based on locally recorded GPS, tilt, seismicity data. In this work, we train deep learning graph neural network (GNN)...

Double difference earthquake relocation is an essential component of many catalog development workflows. This technique produces high-resolution relative relocations between events by minimizing differential measurements the arrival times waves from nearby sources, which highlights resolution faults and improves interpretation seismic activity. The inverse problem typically solved iteratively using conjugate-gradient minimization, however cost scales significantly with total number sources...