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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Evaluating multistation phase picking algorithm phase neural operator (PhaseNO) on local seismic networks

Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the phase neural operator (PhaseNO) shows promising results on regional data sets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on four different local data sets and compare the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. By manually reviewing the newly detected events, we find that the PhaseNO model outperforms the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO’s behaviours when only using one station, as well as gradually increasing the number of stations in the seismic network to better understand its behaviour. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluation of Station Performance of the Idaho National Laboratory Seismic Monitoring Network Using Network Detection Thresholds

The Idaho National Laboratory (INL) Seismic Monitoring Network is located in eastern Idaho and monitors a portion of the intermountain seismic belt. It has been in place for 50 yr and has undergone several major changes, the most recent of which has been the transition to the Antelope real‐time acquisition system and the implementation of automatic phase picking algorithms to aid in analysis. This study discusses the efforts to evaluate the performance of the INL seismic monitoring network (and other surrounding stations) using the new real‐time acquisition system. The method outlined by Wilson et al. (2021) is used to develop an empirical relationship between the observability of local earthquakes as a function of magnitude and distance. This relationship is used to produce detection thresholds for Pwaves for all stations of interest. The INL seismic network has two main goals: monitor tectonic‐and volcanic‐related events and measure ground motions for input into seismic hazard analysis. Because of these two overall objectives, several seismic stations have been installed near critical facilities and, therefore, are not as quiet as stations that are used primarily for earthquake detection. This is reflected in their detection thresholds, which are much smaller for stations away from facilities. This study shows that the INL Seismic Monitoring Network is able to detect earthquakes near INL facilities with M L > 1.2, with redundancies built in to ensure this sensitivity even if data became unavailable from some stations. This study also shows “holes” in the monitoring network where the detection of smaller earthquakes is highly dependent on sparsely placed seismic stations. In conclusion, the results of this study will be used to govern plans for expansion of earthquake monitoring in Idaho and the surrounding region and to fine‐tune the detection thresholds for individual stations.

58 - GEOSCIENCES↗
DOE OSTI2025

Exploring Data Set Bias and Decision Support with Predictive Uncertainty Through Bayesian Approximations and Convolutional Neural Networks

Individual seismic catalogs can contain multiscale observations from fault level to global scales and associated waveforms from discrete events reflect crustal structure across many different scales and locations. Seismic network aperture, geographic location, and observation distance may not provide informative guidance or intuition on how different catalogs will behave across models trained under different conditions. We rely on uncertainty to provide guardrails for when to trust model decisions, but understanding when our uncertainty is trustworthy is an open challenge. Here, in this work, we explore Bayesian approximation methods for assigning predictive uncertainty in seismic event classification problems. We find that computationally expensive Bayesian approximations do not outperform simple ensemble methods. We also find that when exploiting multiple seismic event catalogs, joint training with data from all the catalogs combined with Bayesian approximations and supervised training for classification can obscure bias and result in less robust uncertainty while also not providing substantial performance benefits compared to training individual models for each catalog.

58 GEOSCIENCES↗
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗
DOE OSTI2025

Seafloor Seismic Noise Patterns Across the Pacific Basin

Seismic hazard monitoring and global tomography efforts are improved by recording signals at a variety of distances and azimuths to maximize subsurface sampling. Although seismic networks provide good to excellent coverage on land, seafloor stations are still sparse. Inclusion of ocean-based data would greatly improve the global coverage of seismic networks, but the use of seafloor seismic data to complement land-based detection and characterization of events is complicated by the generally much higher ambient noise level in the ocean compared to that observed on land. This noise is driven primarily by sea surface waves and tides, but how seismic noise levels vary with location in the oceans is not well described. Here, in this work, we analyze the relationship between ocean surface wave height and seismic noise in the 0.4–4 Hz frequency band at ocean-bottom seismometer deployments across the Pacific basin. We find that a noise-to-responsiveness ratio (NRR)—the median noise level at a station divided by its sea surface wave height responsiveness—correlates negatively with detection success for large teleseismic earthquakes. Stations that are close to land, with relatively shallow ocean and low wind speed, often have lower NRR than open-ocean stations, but the connection between geographic location and earthquake detection success is imperfect.

58 GEOSCIENCES↗
DOE OSTI2026

Variability in Performance of a Machine Learning Seismicity Catalog: Central Italy, 2016–2017

Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We develop station‐level detection probabilities using logistic regression and combine them across a seismic network to compute spatial magnitude‐of‐completeness fields. We apply this approach to two catalogs from the 2016–2017 Central Italy sequence that were constructed from the same seismic network, one routine and one ML‐based. At the station level, the ML picker increases detection sensitivity by identifying smaller magnitude events and detecting earthquakes at greater distances. Spatially, the magnitude of completeness decreases substantially, with median values shifting from 1.6 to 0.5 for P waves and from 1.7 to 0.5 for S waves. However, the ML catalog also shows greater variability in station‐level performance than the routine catalog. These results demonstrate that ML‐based improvements in detectability are widespread but spatially nonuniform, highlighting their benefits, their limitations, and the potential for further improvements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Effect of Time Window and Spectral Measurement Options on Empirical Green’s Function Analysis Using DAS Array and Seismic Stations

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green’s function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. Here, we focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multitaper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

58 GEOSCIENCES↗
NASA NTRS1977

HFT events - Shallow moonquakes

A few large distant seismic events of distinctly high signal frequency, designated HFT (high-frequency teleseismic) events, are observed yearly by the Apollo lunar seismic network. Their sources are located on or near the surface of the moon, leaving a large gap in seismic activity between the zones of HFT sources and deep moonquakes. No strong regularities are found in either their spatial or temporal distributions. Several working hypotheses for the identity of these sources have advanced, but many characteristics of the events seem to favor a hypothesis that they are shallow moonquakes. Simultaneous observations of other lunar phenomena may eventually enable the determination of their true identity.

Nakamura, Y.↗
NASA NTRS1973

New seismic data on the state of the deep lunar interior.

Direct shear-wave arrivals from seismic events originating on the far side of the moon are not observed at some of the stations of the Apollo seismic network. These data suggest that the material in the lunar interior at a depth of 1000 to 1100 kilometers is more dissipative for seismic shear waves than the lithosphere above, and possibly exists in a partially molten state akin to the earth's asthenosphere.

Nakamura, Y.↗
DOE OSTI2026

Constructing a High‐Resolution Aftershock Catalog for the 2017 Mw 8.2 Tehuantepec Earthquake Sequence Using a Machine Learning–Based Workflow

The 8 September 2017 Mw 8.2 Tehuantepec earthquake was the largest instrumentally recorded normal‐faulting earthquake in Mexico. The mainshock occurred offshore within the Tehuantepec seismic gap, generating >30,000 aftershocks in the following year. We applied an open‐source, machine learning (ML)–assisted workflow to construct a high‐resolution aftershock catalog using data from temporary and permanent seismic networks in southern Mexico. The workflow integrates PhaseNet for phase detection; GaMMA for phase association; and VELEST, HypoInverse, and HypoDD for velocity modeling and relocation. We processed seven months of continuous waveform data from 29 broadband stations, including a temporary rapid‐response deployment that improved station coverage of the offshore rupture zone. To evaluate performance, we compared our results against analyst‐reviewed picks and event locations from the Servicio Sismológico Nacional catalog. The resulting catalog contains 11,374 relocated earthquakes and represents the most comprehensive published dataset for this sequence, incorporating the first full use of the temporary network. Relocated hypocenters show improved depth control and align well with the Slab2.0 subduction geometry, revealing clearer separation between offshore slab events and onshore crustal seismicity. This study demonstrates that combining ML‐based detection with established methods provides a scalable and reproducible approach for constructing high‐quality earthquake catalogs in tectonically complex environments and offers practical guidance for adapting similar workflows to other earthquake sequences.

Garcia, Marc [The University of Texas at El Paso, ↗
NASA NTRS1979

The Apollo passive seismic experiment

The completed data set obtained from the 4-station Apollo seismic network includes signals from approximately 11,800 events of various types. Four data sets for use by other investigators, through the NSSDC, are in preparation. Some refinement of the lunar model based on seismic data can be expected, but its gross features remain as presented two years ago. The existence of a small, molten core remains dependent upon the analysis of signals from a single, far-side impact. Analysis of secondary arrivals from other sources may eventually resolve this issue, as well as continued refinement of the magnetic field measurements. Evidence of considerable lateral heterogeneity within the moon continues to build. The mystery of the much meteoroid flux estimate derived from lunar seismic measurements, as compared with earth-based estimates, remains; although, significant correlations between terrestrial and lunar observations are beginning to emerge.

Latham, G. V.↗
NASA NTRS1973

Moonquakes, meteoroids, and the state of the lunar interior

Analysis of data returned from the four stations of the Apollo Seismic Network has revealed that the lunar interior can be divided into two major zones: a rigid, dynamically inactive outer shell, about 1000 km thick (the lunar lithosphere); and a relatively weak central zone (the lunar asthenosphere) in which partial melting is probable. The transition between these two zones is gradual. Seismic activity within the moon is far below that of the earth. The small moonquakes that do occur originate near the base of the lithosphere, and appear to fall within two major belts. Tidal energy appears to be an important, if not the dominant, source of energy released as moonquakes. A secular component of moonquake energy release may result from slight thermal expansion or contraction of the moon, weak convection in the asthenosphere, or secular recession of the moon from the earth. Lack of shallow moonquake activity implies that the moon is neither expanding nor contracting at an appreciable rate at present.

Latham, G.↗
DOE OSTI2024

Relocation of the 8 September 2023 High Atlas, Morocco, Earthquake Aftershock Sequence

The earthquake that occurred on 8 September 2023, with a magnitude of 6.8, was the most destructive earthquake event in Morocco in the past decade. This earthquake took place in the Al Haouz region, located in the western part of the High Atlas Mountain range. To better understand what caused and triggered this earthquake, the earthquake catalogs including P and S arrival times were collected from the Moroccan seismic network and combined with regional data from the International Seismological Centre. The mainshock and aftershocks were relocated by using iLoc, a state-of-the-art single-event location algorithm, and then by the multiple event location double-difference algorithm, hypoDD. The improved earthquake relocations using iLoc and the double-difference methods provide sharper lineation of seismicity and agree well with tomographic images of the earthquake zone. Finally, the seismicity distribution and the focal mechanism of the mainshock indicate that the earthquake sequence has occurred along the South Atlas fault system.

58 GEOSCIENCES↗
NASA NTRS1974

Structure of the moon

Seismic data fron the four stations of the Apollo passive seismic network have been analyzed to obtain the velocity structure of the moon. Analysis of body wave phases from artificial impacts of known impact time and position yields a crustal section. In the Mare Cognitum region the crust is about 60 km thick and is layered. In the 20-km-thick upper layer, velocity gradients are high and microcracks may play an important role. The 40-km-thick lower layer has a nearly constant 6.8-km/sec velocity. There may be a thin high-velocity layer present beneath the crust. The determination of seismic velocities in the lunar mantle is attempted by using natural impacts and deep moonquakes. The simplest model that can be proposed for the mantle consists of a 'lithosphere' overlying an 'asthenosphere'.

Toksoz, M. N.↗
NASA NTRS1978

Passive seismic experiment - A summary of current status

The data set obtained from the four-station Apollo seismic network including signals from approximately 11,800 events, is surveyed. Some refinement of the lunar model will result, but its gross features remain the same. Attention is given to the question of a small, molten lunar core, the answer to which remains dependent on analysis of signals from a far side impact. Seventy three sources of repeating, deep moonquakes have been identified, thirty nine of which have been accurately located. Concentrated at depths from 800 to 1000 km, the periodicities of these events have led to the hypothesis that they are generated by tidal stresses. Lunar seismic data has also indicated that the meteoroid population is ten times lower than originally determined from earth based observations. Lunar seismic activity is much lower and mountainous masses show no sign of sinking, in contrast to earth, as a result of the lunar crust being four times thicker. While much work remains to be done, significant correlation between terrestrial and lunar observations can be seen.

Latham, G. V.↗
NASA NTRS1974

Results from the Apollo passive seismic experiment

Recent results from the Apollo seismic network suggest that primitive differentiation occurred in the outer shell of the moon to a depth of approximately 300 km; and the central region of the moon is presently molten to a radius of between 200 and 300 km. If early melting to a depth of 300 to 400 km was a consequence of accretional energy, very short accretion times are required. The best model for the zone of original differentiation appears to be a crust 40 to 80 km thick, ranging in composition from anorthositic gabbro to gabbro; overlying an ultramafic cumulate (olivine-pyroxene) about 250 km thick. The best candidate for the molten core appears to be iron or iron sulphide. A new class of seismic signals has recently been identified that may correspond to shallow moonquakes. These are rare, but much more energetic than the more numerous, deep moonquakes.

Lathum, G.↗
NASA NTRS1977

Results from the Apollo passive seismic experiment

Recent results from the Apollo Seismic Network suggest that primitive differentiation occurred in the outer shell of the moon to a depth of approximately 300 km and the central region of the moon is presently molten to a radius of between 200 and 300 km. If early melting to a depth of 300 to 400 km was a consequence of accretional energy, very short accretion times are required. It was shown that the best model for the zone of original differentiation is a crust 40 to 80 km thick, ranging in composition from anorthositic gabbro to gabbro, and overlying an ultramafic cumulate about 250 km thick. The best candidate for the molten core appears to be iron or iron sulphide. A new class of seismic signals recently were identified that may correspond to shallow moonquakes. These are rare, but much more energetic than the more numerous, deep moonquakes.

Latham, G.↗