Geology topics

Robert Skoumal

Publications and source records attributed to Robert Skoumal.

12 recordsLinked to original sources

USGS2025

Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa

From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.

American Somoa↗

USGS2024

SKHASH: A python package for computing earthquake focal mechanisms

We introduce a Python package for computing focal mechanism solutions. This algorithm, which we refer to as SKHASH, is largely based on the HASH algorithm originally written in Fortran over 20 yr ago. HASH innovated the use of suites of solutions, spanning the expected errors in polarities and takeoff angles, to estimate focal mechanism uncertainty. SKHASH benefits from new features with flexible input formats and allows users to take advantage of recent advances in constraining focal mechanisms for small magnitude or poorly recorded earthquakes. The 3D locations of earthquakes and the velocity models used are varied when finding acceptable solutions. As a result, source–receiver azimuths are reflective of errors from the earthquake locations and velocity models, in addition to the takeoff angles. Users can consider weighted P ‐wave first‐motion polarities derived from traditional or machine‐learning picks, cross‐correlation consensus, and/or imputation techniques using SKHASH. Focal mechanism solutions can also be further constrained using traditional, machine learning, and/or cross‐correlation consensus S / P amplitude ratios. With improved reporting of individual and collective P polarity and S / P amplitude misfits, users can better evaluate the success of the solutions and the quality of the measurements. The reporting also makes it easier to identify potential issues with metadata, including incorrectly reported station polarity reversals. In addition, by leveraging vectorized operations, taking advantage of an efficient backend Python C Application Programming Interface, and the use of a parallel environment, the Python SKHASH routine may compute mechanisms quicker than the HASH routine.

Seismological Research Letters↗

USGS2023

Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California

We utilized relative polarity measurements and machine learning techniques to better resolve focal mechanisms and stress orientations considering a catalog of ∼29,000 relocated earthquakes that occurred during 1984–2021 in the southeastern San Francisco Bay Area. Earthquake focal mechanisms are commonly produced using P wave first motion polarities, which traditionally requires events to be well-recorded across a seismic network with good focal sphere coverage. We adapted recently developed approaches that are less dependent on high signal-to-noise records and exploit similar waveforms to produce relative polarity and amplitude measurements between earthquake pairs. These techniques were previously only applied on localized earthquake sequences, and we further developed these approaches so that they can be utilized for regional catalogs. We validated or corrected manually identified polarities by performing polarity consensuses using earthquake pairs. Missing and unreliable polarity measurements were imputed using iterative random forests, an unsupervised ensemble machine learning method. Relative P and S wave amplitude measurements were made between earthquakes, constraining S / P ratios for low signal-to-noise waveforms. Using these techniques, we were able to reduce focal mechanism uncertainties by an average of ∼13° and produced well-constrained focal mechanisms for ∼6 times as many earthquakes than those produced using only the traditionally derived polarities. We performed stress inversions using the focal mechanisms by grouping the focal mechanism results into a quadtree structure. Our stress results are consistent with previous work, albeit at a higher spatial resolution, and demonstrate these techniques can aid our understanding of fault structures and kinematics in more detail than was previously possible.

California↗

USGS2022

Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination

Earthquake focal mechanisms are traditionally produced using P ‐wave first‐motion polarities and commonly require well‐recorded seismicity. A recent approach that is less dependent on high signal‐to‐noise exploits similar waveforms to produce relative polarity measurements between earthquake pairs. Utilizing these relative polarity measurements, it is possible to produce composite focal mechanisms for clusters within microseismic sequences using regional networks. However, missing or low‐confidence polarity measurements still limit our ability to calculate high‐quality composite focal mechanisms. Here, we replaced unreliable polarity measurements with estimates using iterative random forests, an unsupervised ensemble machine learning method. Using the imputed (“replaced”) polarity data, we then categorically clustered the events into families. As a case study, we applied this modified composite mechanism workflow to a multistation template matched catalog of an earthquake swarm that occurred during 2020 near the Maacama fault in northern California. We found that our modified methodology produced higher‐quality earthquake families and improved composite focal mechanisms, with fault‐plane uncertainties <35° for 94% of the families compared with 34% of families using the previous methodology.

Seismological Research Letters↗

USGS2021

Wastewater disposal has not significantly altered the regional stress state in southern Kansas

Wastewater disposal is primarily responsible for the increased seismicity rate since ~2013 in southern Kansas. Previous work that used shear wave splitting (SWS) in southern Kansas interpreted a ~90º temporal rotation in the fast polarization direction and attributed it to increased pore pressures resulting from fluid injection. However, this interpreted rotation coincided with a change in the stations used to make the SWS measurements. We investigate the temporal variability of fast azimuths in southern Kansas by making SWS measurements on earthquake families with similar source-receiver paths recorded on a stable local seismic network. We select high-quality SWS measurements by investigating the stability of results across 65 different frequency bands between 0.5-15 Hz. We find that the fast polarization direction in southern Kansas is relatively constant with an average ENE (~N79ºE) orientation between 2014-2017. Our fast polarization measurements are primarily a reflection of the maximum principal horizontal stress direction (SHmax). We observe a slight spatial change in SHmax to the NE (~N55°E) near the Nemaha Ridge in Oklahoma. However, we do not observe any significant temporal rotation of SHmax or variation in delay time (i.e., crack density) in southern Kansas, contrary to the earlier study. The previously interpreted ~90º rotation may either be a reflection of a very local stress change or a misinterpretation of SWS results potentially due to the use of inconsistent source-receiver paths. Our SWS measurements cover the period of peak wastewater disposal and seismicity rates and suggest an absence of significant temporal rotations in the local anisotropy and stress orientations associated with wastewater disposal.

Kansas↗

USGS2021

The proliferation of induced seismicity in the Permian Basin, Texas

The Permian Basin has a long history of induced earthquakes, but the seismicity rates have increased dramatically over the past two decades and included a M W 5.0 likely induced by wastewater disposal (WD) in March 2020. A detailed characterization of the proliferation of seismicity in the Permian Basin throughout this time period is needed for improving the scientific understanding of the mechanisms responsible and for mitigating future seismic hazard. Due to a sparse regional seismic network before the advent of Texas Seismological Network in 2017, we characterize seismicity using the 10-station TXAR array that is 100s of km away from most of the seismicity, with the objective of improving upon the substantial contributions from previous work. By exploiting the nature of waveform similarity, we detect events with template matching, performing a quantitative analysis of spatially varying detection capabilities throughout the study area. From an initial catalog of 10,753 events, we identify 45,009 earthquakes and 10,208 quarry blasts. Using our catalog of earthquakes, we improve epicentral locations, compare relative magnitude techniques, and associate earthquakes to WD or hydraulic stimulations. We further use our earthquake catalog to investigate the relationship between seismicity and human activities near the city of Pecos, Texas. Through a comparison of our earthquake catalog with industrial records, we determine that the vast majority seismicity near Pecos, Texas, since 2000 is likely induced by an increase of WD at wells injecting at depths greater than 1.5 km.

Texas↗

USGS2021

Characterizing stress orientations in southern Kansas

Induced seismicity predominantly occurs along faults that are optimally oriented to the local principal compressive stress direction, and the characterization of these stress orientations is an important component of understanding seismic hazards. The seismicity rate in southern Kansas rapidly increased in 2013 primarily due to the disposal of large volumes of wastewater into the Arbuckle Group. Previously, local stress orientations in this area were poorly constrained, which limited our understanding of the complex faulting and diverse earthquake mechanisms in this region. We use shear‐wave splitting and focal mechanism inversion techniques to create multiple, independent estimates of maximum horizontal stress directions ( ⁠ S H max ">

Kansas↗

USGS2020

The induced Mw 5.0 March 2020 west Texas seismic sequence

On March 26, 2020, a M 5.0 earthquake occurred in the Delaware Basin, Texas, near the border between Reeves and Culberson Counties. This was the third largest earthquake recorded in Texas and the largest earthquake in the Central and Eastern United States since the three M 5.0–5.8 induced events in Oklahoma during 2016. Using multistation waveform template matching, we detect 3,940 earthquakes in the sequence with the first event in the area occurring in May 2018. The M 5.0 earthquake sequence occurred on a ENE (∼082°) normal fault dipping ∼37° toward the south. The earthquake caused 6 mm of oblique surface deformation, and geodetic slip inversion suggests slip was isolated above 6 km depth. We find that the sequence was most likely induced by nearby wastewater disposal operations, and seismicity rates in the region surrounding the M 5.0 will likely continue to increase in the future if disposal operations continue unaltered.

Texas↗

USGS2020

Hydraulic fracturing induced seismicity

Hydraulic fracturing (HF) is a technique that is used for extracting petroleum resources from impermeable host rocks. In this process, fluid injected under high pressure causes fractures to propagate. This technique has been transformative for the hydrocarbon industry, unlocking otherwise stranded resources; however, environmental concerns make HF controversial. One concern is HF‐induced seismicity, since fluids driven under high pressure also have the potential to reactivate faults. Controversy has inevitably followed these HF‐induced earthquakes, with economic and human losses from ground shaking at one extreme and moratoriums on resource development at the other. Here, we review the state of knowledge of this category of induced seismicity. We first cover essential background information on HF along with an overview of published induced earthquake cases to date. Expanding on this, we synthesize the common themes and interpret the origin of these commonalities, which include recurrent earthquake swarms, proximity to well bore, rapid response to stimulation, and a paucity of reported cases. Next, we discuss the unanswered questions that naturally arise from these commonalities, leading to potential research themes: consistent recognition of cases, proposed triggering mechanisms, geologically susceptible conditions, identification of operational controls, effective mitigation efforts, and science‐informed regulatory management. HF‐induced seismicity provides a unique opportunity to better understand and manage earthquake rupture processes; overall, understanding HF‐induced earthquakes is important in order to avoid extreme reactions in either direction.

Reviews of Geophysics↗

USGS2020

Factors influencing the probability of hydraulic fracturing induced seismicity in Oklahoma

Injection‐induced seismicity became an important issue over the past decade, and although much of the rise in seismicity is attributed to wastewater disposal, a growing number of cases have identified hydraulic fracturing (HF) as the cause. A recent study identified regions in Oklahoma where ≥75% of seismicity from 2010 to 2016 correlated with nearly 300 HF wells. To identify factors associated with increased probability of induced seismicity, we gathered publicly available information about the HF operations in these regions including: injected volume, number of wells on a pad, injected fluid (gel vs. slickwater), vertical depth of the well, proximity of the well to basement rock, and the formation into which the injection occurred. To determine the statistical strength of the trends, we applied logistic regression, bootstrapping, and odds ratios. We see no trend with total injected volume in our Oklahoma dataset, in contrast to strong trends observed in Alberta and Texas, but we note those regions have many more multiwell pads leading to larger cumulative volumes within a localized area. We found a > ∼50% lower probability of seismicity with the use of gel compared to slickwater. We found that HF wells targeting older formations had a higher probability of seismicity; however, these wells also tend to be deeper, and we found the trend with well depth to be stronger than the trend with age of formation. When isolated to the Woodford formation, well depth produced the strongest relationship, increasing from ∼5%> to ∼50% probability from 1.5 to 5.5 km. However, no trend was seen in the proximity to basement parameter. Based on previously measured pore pressure gradients, we interpret the strong absolute depth relationship to be a result of the increasing formation overpressure measured in deeper portions of the basin that lower the stress change needed to induce seismicity.

Oklahoma↗

USGS2020

Activation of optimally and unfavourably oriented faults in a uniform local stress field during the 2011 Prague, Oklahoma, sequence

The orientations of faults activated relative to the local principal stress directions can provide insights into the role of pore pressure changes in induced earthquake sequences. Here, we examine the 2011 M 5.7 Prague earthquake sequence that was induced by nearby wastewater disposal. We estimate the local principal compressive stress direction near the rupture as inferred from shear wave splitting measurements at spatial resolutions as small as 750 m. We find that the dominant azimuth observed is parallel to previous estimates of the regional compressive stress with some secondary azimuths oriented subparallel to the strike of the major fault structures. From an extended catalogue, we map ten distinct fault segments activated during the sequence that exhibit a wide array of orientations. We assess whether the five near-vertical fault planes are optimally oriented to fail in the determined stress field. We find that only two of the fault planes, including the M 5.7 main shock fault, are optimally oriented. Both the M 4.8 foreshock and M 4.8 aftershock occur on fault planes that deviate 20–29° from the optimal orientation for slip. Our results confirm that induced event sequences can occur on faults not optimally oriented for failure in the local stress field. The results suggest elevated pore fluid pressures likely induced failure along several of the faults activated in the 2011 Prague sequence.

Oklahoma↗

USGS2019

Characterizing seismogenic fault structures in Oklahoma using a relocated template matched catalog

Oklahoma is one of the most seismically active places in the United States as a result of industry activities. In order to characterize the fault networks responsible for these earthquakes in Oklahoma, we relocated a large-scale template matching catalog between 2010-2016 using the GrowClust algorithm . This relocated catalog is currently the most complete statewide catalog for Oklahoma during this seven-year window. Using this relocated catalog, we identified seismogenic fault segments by developing an algorithm that clusters earthquakes and then identifies linear trends within each cluster. Considering the large number of earthquakes in Oklahoma, this algorithm made the process of identifying previously unmapped seismogenic faults more approachable and objective. We identify approximately 2500 seismogenic fault segments which are in general agreement with focal mechanisms and optimally oriented relative to maximum principle stress measurements. We demonstrate that these fault orientations can be used to approximate the maximum principle stress orientations.

Oklahoma↗