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Article

Seven Thousand Felt Earthquakes in Oklahoma and Kansas Can Be Confidently Traced Back to Oil and Gas Activities

by
Iason Grigoratos
1,*,
Alexandros Savvaidis
2 and
Stefan Wiemer
1
1
Swiss Seismological Service, ETH Zurich, Sonneggstrasse 5, CH-8092 Zurich, Switzerland
2
Bureau of Economic Geology, University of Texas at Austin, 10611 Exploration Way, Austin, TX 78758, USA
*
Author to whom correspondence should be addressed.
GeoHazards 2025, 6(3), 36; https://doi.org/10.3390/geohazards6030036
Submission received: 7 May 2025 / Revised: 23 June 2025 / Accepted: 1 July 2025 / Published: 15 July 2025
(This article belongs to the Special Issue Seismological Research and Seismic Hazard & Risk Assessments)

Abstract

The seismicity levels in Oklahoma and southern Kansas have increased dramatically over the last 15 years. Past studies have identified the massive disposal of wastewater co-produced during oil and gas extraction as the driving force behind some earthquake clusters, with a small number of events directly linked to hydraulic fracturing (HF) stimulations. The present investigation is the first one to examine the role both of these activities played throughout the two states, under the same framework. Our findings confirm that wastewater disposal is the main causal factor, while also identifying several previously undocumented clusters of seismicity that were triggered by HF. We were able to identify areas where both causal factors spatially coincide, even though they act at distinct depth intervals. Overall, oil and gas operations are probabilistically linked at high confidence levels with more than 7000 felt earthquakes (M ≥ 2.5), including 46 events with M ≥ 4.0 and 4 events with M ≥ 5. Our analysis utilized newly compiled regional earthquake catalogs and established physics-based principles. It first hindcasts the seismicity rates after 2012 on a spatial grid using either real or randomized HF and wastewater data as the input, and then compares them against the null hypothesis of purely tectonic loading. In the end, each block is assigned a p-value, reflecting the statistical confidence in its causal association with either HF stimulations or wastewater disposal.

1. Introduction

Within Oklahoma and Kansas, the disposal of massive amounts of wastewater, co-produced during oil and gas production, started in the 1940s and began to scale significantly in the 1990s, and increased dramatically with the advent of hydraulic fracturing (HF) operations in the mid-2000s [1]. Between the year 2000 and 2021, 39.7 billion bbls (6.3 billion m3) of wastewater were disposed in the region. To put things into perspective, this is roughly equivalent to the total current volume of the Salton Sea lake. Wastewater disposal wells (SWD) inject into laterally extensive aquifers, which are at different shallow depths, with some (e.g., Arbuckle) directly overlying the Precambrian basement [2]. Notably, little to no wellhead pressure is needed during injection (gravity-feed disposal) [3,4], while the median disposal depth varies spatially but is around 1200 m. The HF activities occur at shallow low-permeability formations (e.g., Woodford Shale) and at much higher wellhead pressures. The median HF depth is around 2200 m, but some stimulations exceed 4000 m in depth. The most significant HF activities are taking place in the SCOOP/STACK plays, the Arkoma Basin and the Anadarko Platform. Kansas has hosted very limited HF operations.
Historically, the seismicity rates in Oklahoma (OK) and Kansas (KN) were very low, at about two to four seismic events above moment magnitude (Mw) 3 per year [5,6]. However, around 2009 the seismicity rates started to rise, accelerating rapidly around 2014, and peaking at the end of 2015 at unprecedented levels, orders of magnitude above the historical levels (Figure 1a). Within OK, there were four events larger than M 5 in total (one in 2011 and three in 2016), with the largest being a Mw 5.8 in Pawnee. The estimated statewide earthquake losses have exceeded US$ 85 million, mostly due to non-structural damages [7]. The frequency of felt seismicity caused unrest to the public as the regional infrastructure has been designed with little to no consideration of seismic demands. At the beginning, the epicenters were mostly in the center of OK, but later migrated further north and northwest. Since 2016, the seismicity rates have been on a steady decline and are now much closer to the historical levels. The majority of earthquake hypocenters are below the top of the basement [8,9,10], with a distinct overlap between the deepest disposal wells and the shallowest earthquakes [11].
Scientific studies attributed this seismicity surge in OK to SWD [12,13,14,15], including the four largest M5+ events (e.g., [16,17]). Most of these studies calibrated adhoc geomechanical models to past seismicity and injection data to argue that a triggering mechanism is plausible. Only McClure et al. [14] and Grigoratos et al. [15] applied robust hypothesis testing protocols to support the alleged association. The SWD rates were gradually rising after at least the year 2000, with accelerating growth between 2011 and 2015 (Figure 1a). Since then, they have been on a steady decline. Notably, although wastewater disposal activities continuously intensified between 2000 and 2015, the earthquake rates did not rise before 2009, accelerating noticeably around 2014. This indicates an apparent time lag of months to years between increased disposal rates and the onset of seismicity [13,18]. That said, we should highlight that a similar time lag was not observed in the seismicity response once disposal rates began decreasing.
SWD is recognized as the primary, but not the sole, contributor to elevated seismicity levels within OK. Holland [19] and Skoumal et al. [20] have shown that certain earthquake clusters within the state were induced by HF, especially within the SCOOP/STACK zone. Ries et al. [21] flagged a number of operational and geological factors that enhance the apparent likelihood of HF-induced seismicity within Oklahoma. Notably the SWD and HF operations in certain areas of the state overlap both in space and time (Figure 1b).
By 2013 the seismicity had expanded northward into southern KN, peaking in 2015, like in OK. Prior to this period, Kansas had also experienced minimal seismic activities, with only 15 earthquakes above M 3 since 1973 [6]. The largest recent event in KN was the Mw 4.9 Milan event in 2014, near the southern border [10]. SWD within the Arbuckle formation has been the suspected triggering factor in southern KN [6,22]. However, this conclusion has been reached deterministically, demonstrating that such a causal link is physically possible. The uniqueness and statistical significance of this association remains unquantified. Figure 1b and Figure S1 demonstrate that the link in space between SWD and earthquake occurrence is not straightforward. In some regions, the seismicity is collocated with SWD wells, but in other regions there is no spatial correlation. There are vast areas within KN where massive SWD volumes have caused no detectable seismicity.
In this study, we apply an established probabilistic protocol [14,15,23] to examine the extent to which SWD and HF have contributed to the unprecedented seismicity levels observed in OK and southern KN over the last 15 years. The present investigation is the first one to examine the role both of these activities played throughout the two states, under the same framework. It is also the first one to examine the effects of HF and SWD in central and northern KN. Our framework first hindcasts the seismicity rates on a spatial grid using either HF or SWD data as input, and then compares those seismicity rates against the null hypothesis of purely tectonic triggering. In the end, each block is assigned a p-value, indicating the statistical confidence of its causal relationship with each human activity. Our analysis used both declustered and non-declustered compilations of regional earthquake catalogs and established physics-based relationships. This robust framework has been previously successfully applied in Oklahoma [15], South Texas [24] and West Texas [23].

2. Data

First, we compiled a new unified earthquake catalog covering magnitudes above 1 for the Central and Eastern United States from January 2000 to February 2023, combining multiple different sources (Table S1). Duplicate events among catalogs were removed following certain hierarchy rules, whenever multiple location or magnitude solutions existed [25]. These rules, alongside the margins used during the duplicate-search can be found in Grigoratos and Wiemer [26]. Subsequently, the unified catalog was divided into subcatalogs to capture spatio-temporal variations in the magnitude completeness (Mc). The OK & KN area had its own subcatalog, containing 82 events above M 4 and 3298 above M 3. Most of our analyses focused on the bulk of the seismicity, below latitude 37.5. There, we estimate a magnitude of completeness (Mc) around 3 after 2006 in OK, around 2.5 after 2012 in OK, around 2.5 after mid-2014 in southern Kansas, and closer to 2 after 2016 for both. These estimates are based on the slope of the magnitude–frequency distributions and the history of the networks (see Supplementary Materials). Our methods are not sensitive to the b-values or the Mc [23]; therefore, a precise Mc calculation was not needed. For this reason, magnitudes were not homogenized into Mw, allowing various magnitude scales to coexist. Generally, most magnitudes below 3.5 are local magnitudes, while those above 3.5 are predominantly moment magnitudes.
Sources of SWD data were the B3 database (for OK), the Oklahoma Corporation Commission (OCC; for OK), the KGS (for KN), the Environmental Protection Agency (EPA; for a few counties in OK), Kyle Murray ([5]; for OK) and the studies by Weingarten et al. [3] (for all states), Barbour et al. [27] (for Pawnee county in OK) and Norbeck and Rubinsten [18] (for OK and KN). At the time of our analysis, the B3 wastewater disposal data were assumed complete till 2021 (with the exception of Pawnee county). The (monthly) OCC datasets were assumed complete through 2021, with just the daily Arbuckle-only data being available for 2022 and early 2023. The KGS disposal data were available only through 2021. The available EPA data, which are relevant only for a few counties mostly within OK, were assumed largely incomplete after 2016.
Our SWD analyses utilized monthly disposal rates, the coordinates of the wells and target-formations for some basins. True vertical depths were also collected, although not used explicitly, since all our analyses were conducted in 2D. We could not use the public pressure data because they are largely incomplete and not reliable [28]. Only SWD wells were included in our calculations; Enhanced Oil Recovery (EOR) wells were omitted, since EOR operations aim to stabilize, rather than increase, the pore pressure. Given the spatial extent of our investigation zone, we did not limit our analysis to the Arbuckle aquifer, but rather included all the disposal wells in the region, regardless of their target formation. This is a significant limitation of our study. That said, experience from the Delaware basin in West Texas has shown that SWD in shallow formations can also cause felt seismicity [23], with the interplay between shallow and deeper formations yet to be resolved. In the future, if precise focal depths become widely available, the analyses can be repeated with depth-specific filters applied to the seismicity and the injection data. That said, the reported target formations are not always reliable, since the reported injection depths often lead to contradictory labels.
Little to no wellhead pressure is needed for SWD, even for high-rate wells [3]. This indicates that the large-scale, bulk permeability of the Arbuckle Group is expected to be towards the upper end of the reported interval [4], which is around 0.1 to 2 m2/s [5]. The permeability certainly varies across locations, and might increase over time due to secondary porosity from fractures (seismicity).
Sources of HF stimulation datasets were the FracFocus Chemical Disclosure Registry (in both states) and the OCC FracNotices (in OK). FracFocus reporting began in 2011 and became mandatory after January 2012. The OCC mandated filing a Hydraulic Fracture Notice Form (FracNotice) 48 h prior to any HF operations within OK. FracNotices were first required to an OCC district office in 2012. Electronic filing was available after July of 2016 and became mandatory after December of 2016. In the FracNotices the operators state the scheduled start and end dates of operation, well surface and bottom-hole location, average fluid volume per stage, anticipated number of stages, and initiation date of well flowback. In summary, for OK, we used HF data through December 2022 (leveraging the FracNotices), while for KN we used data through September 2022 (~5 month reporting lags are typical within FracFocus).
The processing of the HF and SWD data followed Grigoratos et al. [5,23] and is detailed in the Supplementary Materials. The median duration of an HF stimulation was 4 days (Figure S2a), and the median daily volume of an HF well was 16890 bbls (Figure S2b). Between 2014 and 2022, the total injected HF volume was 4.5 billion bbls (715 million m3) and the total number of stimulation-days was 15700 (Figure 2). Gridded cumulative HF and SWD volumes alongside earthquake epicenters are presented in Figure 3a and Figure S3.

3. Methods

3.1. Declustering

The utilized earthquake recurrence models can exclusively model mainshocks. Thus, foreshocks and aftershocks were eliminated through the declustering method of Aden-Antoniow et al. [29], which employs the nearest-neighbor clustering framework of Zaliapin et al. [30]. This approach demonstrates robustness with limited sample sizes and remains stable regarding Mc, though it shows sensitivity to the user-specified d- and w-parameters (set to 1.5 and 0.5, respectively, following sensitivity analyses). The clusters were subsequently processed to retain the highest-magnitude event as the mainshock (instead of the earliest one).

3.2. Hypotheses Testing for Causal Factors of Induced Seismicity

Following Grigoratos et al. [15,23], the recorded earthquake activity is hindcast with a set of competing statistical models to derive likelihood ratios, which are then converted to p-values via reshuffling tests [14]. First, both injection and earthquake data are aggregated into spatial blocks (cells) of 5 to 10km at regular time intervals (monthly for SWD and daily for HF). Only blocks with positive volume (for a given operation) and with at least 3 earthquakes above Mc are designated as “active”. Two block-specific hypotheses are formulated: (i) a null hypothesis that assumes no relationship between injection and seismicity (total likelihood L0), and (ii) an alternative model that does assume a relationship between injection and seismicity (total likelihood L1). The ratio of L1 to L0 is defined as R. Although R values larger than 1.0 (i.e., L1 > L0) signal that the alternative hypothesis is more likely than the null hypothesis, this criterion is insufficient to statistically reject the null hypothesis. To do that, we need a reference distribution for the ratio R, in which the null hypothesis is true (Rnull). This reference distribution enables the comparison of the two hypotheses in a robust manner, even if they differ in model-complexity. The Rnull distribution was empirically synthesized by reshuffling the injection data (with subsequent recalculation of the ratios L1 and L0). The reshuffling procedure is described in detail in Grigoratos et al. [15,23]. The Rnull distribution essentially indicates how likely it is for a seismicity model to find a purely coincidental correlation between the observed seismicity and random injection data. By comparing the generated Rnull values in each block with the single R value obtained from the real injection and seismicity time-series (Robs), we can evaluate how confident we are that the improved correlation of the alternative hypothesis is not coincidental. The metric used to estimate this confidence level is the statistical p-value, where p = ( η + 1 ) / κ , with κ being the total number of synthetic (reshuffled) datasets (in our case 200) and η being the number of synthetic datasets with Rnull larger than the Robs value (from the real data). As an example, a p-value of 0.05 reflects a confidence level C of 95% that the null hypothesis can be rejected (C = 1 − p). For the conditions and assumptions under which these confidence levels can be interpreted as probabilities of causality, the readers are referred to McClure et al. [14]. A schematic diagram of our hypothesis testing framework is shown in Figure 4
The smaller the p-value, the stronger the association between the earthquake activity and the specific oil and gas operation being examined. Very low p-values, that is, below 0.05, indicate that the investigated anthropogenic activity represents a key triggering factor of the seismicity in that block [23]. p-values ranging from 0.05 to 0.10 may identify blocks where a portion of the earthquake clusters is induced. Blocks displaying low p-values across two different activities are probably affected by both operations, with more complex statistical analysis being required to properly estimate the precise contribution ratio. Elevated p-values above 0.20, and particularly surpassing 0.50, suggest that very little to no seismicity there is attributable to the examined human activity.
In this analysis, we investigated two potential triggers associated with hydrocarbon extraction: HF stimulations and SWD. The p-value analysis was performed twice, considering one alternative triggering mechanism per iteration, with each tested against the null hypothesis that assumes all earthquakes are of tectonic origin. Implementation details regarding spatial oversampling can be found in Grigoratos et al. [23] and Grigoratos and Wiemer [26].
Very low p-values, lower than 0.05, indicate that the investigated oil and gas activity is a key triggering factor of the seismicity in that block. p-values ranging between 0.05 and 0.10 could highlight blocks where some of the seismicity clusters are induced. Blocks exhibiting low p-values from both SWD and HF are likely influenced by both operations, with more advanced statistical methods being required to precisely measure the exact contribution for each. p-values ranging between 0.10 and 0.20 are somewhat inconclusive, whereas p-values larger than 0.20, and particularly those larger than 0.50, signal that little to no seismicity there was triggered by the examined operations.

3.3. Generalized Seismogenic Index Model

To be able to hindcast the spatio-temporal changes in the seismicity rates, we have to employ a physics-based earthquake recurrence model that incorporates the external driving forces, i.e., the oil and gas operations. As most stress changes arise either directly or indirectly from injected volumes, the earthquake recurrence model should use HF or SWD rates as inputs. We adopted the approach of Grigoratos et al. [5,23], who extended the Seismogenic Index model [31] to large-scale HF operations and SWD. Their approach accounted for the background tectonic rate and the stressing-rate dependency of the time lag between injection and seismicity rate changes [18]. The original Seismogenic Index model is itself a modified formulation of the G-R relation and predicts that the number of induced earthquakes is proportional to the pore pressure change, which can be approximated by the injected volume. Initially, it was successfully employed to hindcast induced seismicity during hydraulic stimulations [32,33,34]. Details on the modeling of the time-lags and all the equations defining our earthquake recurrence model can be found in the Supplementary Materials.
Following Grigoratos et al. [5], we recognized that SWD in one block impacts the pore pressure and stress field in adjacent blocks. HF volumes are injected into much tighter formations and therefore this effect is highly localized relative to the dimensions of our blocks. The latter is 5 km for HF and 10 km for SWD [23]. Consequently, we distributed the SWD volumes of each well in space and time following the Theis [35] equation for transient, radial flow in nonleaky vertically confined aquifers of infinite areal extent (Equation (S4)). The sole parameter required to calculate the distribution factors is the large-scale diffusivity, D . We performed calculations for three different values of D, equal to 0.3, 1.0 and 2.0 m2/s (Figure 3b) and discussed the sensitivity of the results. Additional implementation information is given in Grigoratos et al. [5] and in the Supplementary Materials.

3.4. Hydraulic Fracturing Radar

Following Grigoratos et al. [23], we employed the Hydraulic Fracturing Radar (HFR) as an initial screening tool for HF within each region of interest. HFR gives a measure (per grid cell) of how many earthquakes happened close in space and time to HF operations, adjusted for how much HF activity actually took place there. This metric is utilized only as a sanity check to confirm or question results stemming from the p-value analyses. To create HFR, first we plotted a 5 km base-grid with the percentage of earthquakes above Mc that occurred during or within 3 days of a HF stimulation and within 5 km from it (this percentage is called E Q H F % ) . Then, E Q H F % is adjusted to account for the maximum total duration of stimulations within a block over the investigated study period. The reason for that adjustment is the observed asymmetry in the intensity of HF activities across blocks. HFR can be calculated using the declustered or non-declustered catalog. The distance threshold (5 km) is meant to cover the epicentral uncertainty of the earthquakes and the horizonal extension of the HF wells (toe-to-heel extension). The 3-day temporal buffer (after the stimulation end-date) is due to the uncertainty in the end-dates of the HF stimulations coming from the IHS database.

4. Results

Figure 5 shows the results of the declustering, for the time period between 2000 and 2022, for a magnitude cut off of M 2.5. In reality, we expect the Mc to be closer to 3 in early years, and reach 2.5 only after 2012 or so. The algorithm removed 34% of the events as foreshocks/aftershocks. The corresponding value for the Delaware basin (M ≥ 1.5) and the Midland basin (M ≥ 1.5) was much lower, at 14% and 19% respectively [26]. This could be due to the fact that a relatively larger percentage of seismicity in OK and southern KN occurs within the crystalline basement.

4.1. SWD

Regarding SWD, we chose to start the calibration in 2012 because the Mc decreased significantly in 2011 throughout OK (Supplementary Materials). Figure 6 and Figure S4 show the statistical p-values for SWD (pSWD) in the region for M ≥ 2.5 and with the large-scale diffusivity value D set to 2 m2/s. In particular, 46% of “active” blocks have pSWD ≤ 0.05, and 55% of “active” blocks have pSWD ≤ 0.10. Furthermore, 61% of earthquakes occurred within a block with pSWD ≤ 0.05 and 71% within a block with pSWD ≤ 0.10. Recall that a p-value of 0.05 translates to a 95% confidence interval for association. The two key calibrated parameters are shown in Figure S5. The results are insensitive to declustering (Figure S6) and only slightly sensitive to the selected D value (Figures S7 and S8). This demonstrates the stability of our statistical framework, which is based on physics-based principles.
Our observations are in agreement with previous studies that have identified SWD as the main driver of seismicity in central and northwestern OK and in southern KN. By not flagging SWD as a causal factor in the SCOOP/STACK play and in the Arkoma basin, we are also in agreement with Skoumal et al. [20] who argued that HF is the main driver there. Notably, all Mw 4.9+ ruptures (Prague, Cushing, Fairview, Pawnee; Milan) occurred in blocks with pSWD ≤ 0.05. Thus, we were able for the first time to confidently link all major ruptures to SWD, using a single model-formulation and parametrization, without having to employ complex poroelastic effects or earthquake-to-earthquake nucleations. The latter two mechanisms were required in previous sequence-specific deterministic studies (e.g., [16,17,27]). The generic nature of our framework makes it uniquely suitable for investigations that include multiple human activities at various distances and timeframes.
We should highlight that there are many zones in central and northern Kansas that hosted high-volume SWD operations (Figure 3) without significant levels of seismicity or low pSWD values (Figure S4). In fact, we did not find any compelling evidence of persistent induced seismicity in central and northern Kansas (Figures S4 and S6). That said, our framework assumes a consistent seismic response within each block across time, and could potentially miss some short-lived isolated sequences. In any case, we conclude that central and northern Kansas is not prone to cascading induced seismicity from SWD, allowing for potentially highly effective mitigation measures on a well-by-well basis.
It is unclear whether SWD has historically triggered noticeable induced seismicity in the northern part of Kansas. Even though detected seismicity in the 1980s near the northern state-border occurred close to oil field operations, Evans and Steeples [36] made a strong case for a natural tectonic origin. Furthermore, we believe that the evidence that another northern sequence between 1986 and 1992 is linked to SWD is inconclusive [37].
Next, we examined, on a block-by-block basis, the relationships between total diffused or injected disposal volume, pSWD, maximum magnitude and seismicity rate (Figure S15). We limited our investigation to blocks with a pSWD below 0.05. The maximum magnitude was negatively correlated with pSWD, indicating that larger magnitudes tend to occur in blocks where SWD is the key triggering force. A very interesting finding was that the seismicity rate (above Mc) and the maximum magnitude were positively corelated only with the diffused disposal volume, and not with the injected (non-diffused) ones. This highlights how important it is to distribute the SWD volumes in both space and time following pore-pressure diffusion principles.

4.2. HF

In this section we will analyze the potential link between the observed seismicity and HF. The largest volumes are in the southern and western part of OK (Figure 2 and Figure S3). There we also find the highest HFR values (Figure 7), in agreement with Skoumal et al. [20]. Most, if not all, of the seismicity within the SCOOP/STACK plays, the Arkoma Basin and the Anadarko Platform appears highly correlated with HF operations (Figure 7). According to our window-based associations, 7% of the declustered earthquakes are linked to only 3% of the HF stimulations. We should note that 83% of the HF stimulations that eventually induced seismicity (according to HFR) started doing so during the stimulation period, while 71% of “active” blocks have a fitted time-lag (Equation (S3); Figure S9a) of 3 days or less. Consequently, we advise against using larger time-lags in well-to-earthquake association filters. Lastly, the median ΣHF value was −2.6 (Figure S9b).
Figure 8 shows the statistical p-values for HF (pHF) in the region, for the period between 2014 and 2022. In particular, 22% of the “active” blocks have pHF ≤ 0.05, and 36% of “active” blocks have pHF ≤ 0.10. Furthermore, 8% of the declustered earthquakes (above Mc) occurred within a block with pHF ≤ 0.05. When we combine the HFR windows with pHF, 3% of the declustered earthquakes above M 2.5 are both flagged by HFR and occurred within a block with pHF ≤ 0.05 (Figure 9). These earthquakes are almost certainly induced by HF. Rare exceptions to this rule may be events that also have a low pSWD value, hosted in blocks with relatively stable seismicity rates through time (Figure S14).
The pHF values are stable over time and were able to detect zones where HF is a causal factor early on (Figure S10). They are also almost identical when we use the constant time-lag t l a g c function (Figure S11) instead of the time-dependent one (Equation (S3)).
Crucially, the vast majority of blocks with low HFR values also have low pSWD values, indirectly validating the consensus that the seismicity in central OK and sKN is mostly triggered by SWD. That said, there are several blocks in OK with HFR between 0.10 and 0.25 and a few with HFR above 0.25 within zones linked to SWD (Figure 6 and Figure 7). Those blocks also have low pHF values in Figure 8, since HFR and pHF values are highly correlated (Figure S12), as expected. Therefore, there are areas within central and northern OK where the seismicity is likely occurring at different depths, with shallow earthquakes triggered by HF and deeper ones (within the basement) triggered by SWD. This is a novel finding of this analysis. The poor hindcasting performance of the earthquake recurrence model for HF (Figure S13) within blocks with pHF ≤ 0.05 indicates that in blocks where both causal factors are in play, the majority of the seismicity is linked to SWD.
Regarding the HF wells, only 1% of the stimulations are both linked to seismicity and occurred within a block with pHF ≤ 0.05. Conversely, 92% of the stimulations show no link with earthquake activity and are situated in blocks with pHF ≥ 0.05.
We additionally examined, on a block-by-block basis, whether the maximum magnitude showed correlation with either the p-value or with the total volume (provided that pHF ≤ 0.05) (Figure S16). The maximum magnitude was weakly correlated with pHF (Figure S16a), indicating that larger magnitudes tend to be linked to SWD, and not HF. Surprisingly, the maximum magnitude was not positively correlated with the total HF volume per block, implying that geologic conditions are more important than the injected volume as far as the size of the ruptures is concerned.

5. Conclusions

The seismicity levels in Oklahoma and southern Kansas have increased dramatically over the last 15 years. Several past studies have identified the disposal of wastewater co-produced during oil and gas extraction as the driving force behind this surge, with a small percentage being attributed directly to hydraulic fracturing (HF) stimulations. While the present investigation validates that SWD is the main causal factor, it identifies several previously undocumented clusters of seismicity within Oklahoma that appear triggered by HF stimulations. We did not find evidence of induced seismicity in central and northern Kansas.
When we expand our findings from both examined human activities (Figure 5 and Figure 7) to the entire catalog between 2009 and 2022, oil and gas operations are probabilistically linked at the 95% confidence level with 65% of the felt (M ≥ 2.5) earthquakes (7731 events), including 46 events above magnitude 4 and all 4 events above magnitude 5. Our results are essentially grid-independent and are not particularly sensitive to the declustering of the earthquake catalog, to the magnitude of completeness, to the investigation period and to the large-scale hydraulic diffusivity of the aquifers.
Notably, the seismicity is often unevenly correlated with the two oil and gas activities, with vast areas that have undergone intense operations having hosted no detectable seismicity. Thus, geomechanical factors dominate over operational ones, both for HF and SWD. This was the first time a study examined the role both of these activities played throughout the two states, under the same physical and statistical framework.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/geohazards6030036/s1: Text S1–S4; Figures S1–S16; Table S1. Extra supplemental material concerning the input data and results can be found as csv files from https://zenodo.org/ under the DOI: 10.0076/FY2022EHPg. They include earthquake catalogs (declustered and non-declustered), gridded monthly SWD data, and resulting gridded p-values. Each file-category is accompanied by its readme so that the end user has the appropriate documentation to understand how the data is structured.

Author Contributions

Conceptualization, I.G. and A.S.; methodology, I.G. and A.S.; software, I.G.; validation, I.G., A.S.; formal analysis, I.G.; investigation, I.G. and A.S.; resources, S.W. and A.S.; data curation, I.G.; writing—original draft preparation, I.G.; writing—review and editing, I.G., A.S. and S.W.; visualization, I.G.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This material is based upon work supported by the U.S. Geological Survey under Grant No. G22AP00243 (FY2022) of the call G22AS00006. The Texas Seismological Network and Seismology Research (TexNet) and the State of Texas provided financial support under the University of Texas at Austin Award Number 201503664.

Data Availability Statement

Sources of SWD data were the B3 database (https://www.b3insight.com/; last accessed on 11 December 2022), the Oklahoma Corporation Commission [OCC; https://oklahoma.gov/occ/divisions/oil-gas/oil-gas-data.html (last accessed on 2 March 2023); http://imaging.occeweb.com/imaging/UIC1012_1075.aspx (last accessed on 1 May 2019)], the KGS (https://www.kgs.ku.edu/Magellan/Qualified/class2_db.html; last accessed on 23 May 2023), the Environmental Protection Agency (EPA; last update on March 2018), Kyle Murray (OGS; last update on 1 May 2019) and the studies by Weingarten et al. [3], Barbour et al. [27] and Norbeck and Rubinstein [18]. Sources of HF data were the FracFocus Chemical Disclosure Registry (last accessed on 19 February 2023), IHS Markit (last accessed on 29 March 2023), and only for Oklahoma the OCC FracNotices (https://www.oklahoma.gov/content/dam/ok/en/occ/documents/og/isd_automated/All_Notices.csv; last accessed on 19 February 2023). The following earthquake catalogs can be found online: ANSS ComCat https://earthquake.usgs.gov/earthquakes/search/ (last accessed on 20 February 2023), OGS https://ogsweb.ou.edu/eq_catalog/ (last accessed on 19 February 2023), KGS (https://www.kgs.ku.edu/Geophysics/Earthquakes/data.html; last accessed on 19 February 2023). Publications that were used as sources of earthquake catalogs are listed in Table S1. The declustering code is based on scripts available at https://zenodo.org/record/5838353. The python scripts for the duplicate-removal process are available at https://github.com/klunk386/CatalogueTool-Lite/tree/master/OQCatk. All the figures, apart from Figure 4, were generated using Matlab (http://www.mathworks.com, last accessed on 1 March 2023).

Acknowledgments

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. The mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. We thank Justin Rubinstein for providing information about the seismic networks operating in southern Kansas, and Florent Aden-Antoniow for addressing inquiries related to his declustering code. The analyses greatly benefited from the high-performance computing resources available at SED.

Conflicts of Interest

The authors state that they have no identified competing financial interests or personal affiliations that might be perceived to have influenced the research presented in this manuscript.

References

  1. Hough, S.E.; Page, M. A century of induced earthquakes in Oklahoma? Bull. Seism. Soc. Am. 2015, 105, 2863–2870. [Google Scholar] [CrossRef]
  2. Pollyea, R.M.; Chapman, M.C.; Jayne, R.S.; Wu, H. High density oilfield wastewater disposal causes deeper, stronger, and more persistent earthquakes. Nat. Commun. 2019, 10, 3077. [Google Scholar] [CrossRef]
  3. Weingarten, M.; Ge, S.; Godt, J.W.; Bekins, B.A.; Rubinstein, J.L. High-rate injection is associated with the increase in US mid-continent seismicity. Science 2015, 348, 1336–1340. [Google Scholar] [CrossRef]
  4. Langenbruch, C.; Weingarten, M.; Zoback, M.D. Physics-based forecasting of man-made earthquake hazards in Oklahoma and Kansas. Nat. Commun. 2018, 9, 3946. [Google Scholar] [CrossRef]
  5. Grigoratos, I.; Rathje, E.; Bazzurro, P.; Savvaidis, A. Earthquakes Induced by Wastewater Injection, Part I: Model Development and Hindcasting. Bull. Seism. Soc. Am. 2020, 110, 2466–2482. [Google Scholar] [CrossRef]
  6. Rubinstein, J.L.; Ellsworth, W.L.; Dougherty, S.L. The 2013–2016 Induced Earthquakes in Harper and Sumner Counties, Southern Kansas. Bull. Seism. Soc. Am. 2018, 108, 674–689. [Google Scholar] [CrossRef]
  7. Grigoratos, I.; Bazzurro, P.; Rathje, E.; Savvaidis, A. Time-dependent seismic hazard and risk due to wastewater injection in Oklahoma. Earthq. Spectra 2021, 37, 2084–2106. [Google Scholar] [CrossRef]
  8. Pollitz, F.F.; Wicks, C.; Schoenball, M.; Ellsworth, W.; Murray, M. Geodetic Slip Model of the 3 September 2016 Mw 5.8 Pawnee, Oklahoma, Earthquake: Evidence for Fault-Zone Collapse. Seism. Res. Lett. 2017, 88, 983–993. [Google Scholar] [CrossRef]
  9. Schoenball, M.; Ellsworth, W.L. A systematic assessment of the spatiotemporal evolution of fault activation through induced seismicity in Oklahoma and southern Kansas. J. Geophys. Res. Solid Earth 2017, 122, 10.189–10.206. [Google Scholar] [CrossRef]
  10. Choy, G.L.; Rubinstein, J.L.; Yeck, W.L.; McNamara, D.E.; Mueller, C.S.; Boyd, O.S. A Rare Moderate-Sized (Mw 4.9) Earthquake in Kansas: Rupture Process of the Milan, Kansas, Earthquake of 12 November 2014 and Its Relationship to Fluid Injection. Seism. Res. Lett. 2016, 87, 1433–1441. [Google Scholar] [CrossRef]
  11. Cochran, E.S.; Wickham-Piotrowski, A.; Kemna, K.B.; Harrington, R.M.; Dougherty, S.L.; Castro, A.F.P. Minimal Clustering of Injection-Induced Earthquakes Observed with a Large-n Seismic Array. Bull. Seism. Soc. Am. 2020, 110, 2005–2017. [Google Scholar] [CrossRef]
  12. Keranen, K.M.; Weingarten, M.; Abers, G.A.; Bekins, B.A.; Ge, S. Induced earthquakes: Sharp increase in central Oklahoma seismicity since 2008 induced by massive wastewater injection. Science 2014, 345, 448–451. [Google Scholar] [CrossRef]
  13. Keranen, K.M.; Weingarten, M. Induced Seismicity. Annu. Rev. Earth Planet. Sci. 2018, 46, 149–174. [Google Scholar] [CrossRef]
  14. McClure, M.; Gibson, R.; Chiu, K.; Ranganath, R. Identifying potentially induced seismicity and assessing statistical significance in Oklahoma and California. Res. Solid Earth 2017, 122, 2153–2172. [Google Scholar] [CrossRef]
  15. Grigoratos, I.; Rathje, E.; Bazzurro, P.; Savvaidis, A. Earthquakes Induced by Wastewater Injection, Part II: Statistical Evaluation of Causal Factors and Seismicity Rate Forecasting. Bull. Seism. Soc. Am. 2020, 110, 2483–2497. [Google Scholar] [CrossRef]
  16. Goebel, T.; Weingarten, M.; Chen, X.; Haffener, J.; Brodsky, E. The 2016 Mw 5.1 Fairview, Oklahoma earthquakes: Evidence for long-range poroelastic triggering at >40 km from fluid disposal wells. Earth Planet. Sci. Lett. 2017, 472, 50–61. [Google Scholar] [CrossRef]
  17. Chen, X.; Nakata, N.; Pennington, C.; Haffener, J.; Chang, J.C.; He, X.; Zhan, Z.; Ni, S.; Walter, J.I. The Pawnee earthquake as a result of the interplay among injection, faults and foreshocks. Sci. Rep. 2017, 7. [Google Scholar] [CrossRef]
  18. Norbeck, J.H.; Rubinstein, J.L. Hydromechanical earthquake nucleation model forecasts onset, peak, and falling rates of induced seismicity in Oklahoma and Kansas. Geophys. Res. Lett. 2018, 45, 2963–2975. [Google Scholar] [CrossRef]
  19. Holland, A.A. Earthquakes Triggered by Hydraulic Fracturing in South-Central Oklahoma. Bull. Seism. Soc. Am. 2013, 103, 1784–1792. [Google Scholar] [CrossRef]
  20. Skoumal, R.J.; Ries, R.; Brudzinski, M.R.; Barbour, A.J.; Currie, B.S. Earthquakes induced by hydraulic fracturing are pervasive in Oklahoma. J. Geophys. Res. Solid Earth 2018, 123, 10.918–10.935. [Google Scholar] [CrossRef]
  21. Ries, R.; Brudzinski, M.R.; Skoumal, R.J.; Currie, B.S. Factors Influencing the Probability of Hydraulic Fracturing-Induced Seismicity in Oklahoma. Bull. Seism. Soc. Am. 2020, 110, 2272–2282. [Google Scholar] [CrossRef]
  22. Ansari, E.; Bidgoli, T.S. Reply to comment by Peterie et al. on “accelerated fill-up of the arbuckle group aquifer and links to US midcontinent seismicity”. J. Geophys. Res. Solid Earth 2020, 125. [Google Scholar] [CrossRef]
  23. Grigoratos, I.; Savvaidis, A.; Rathje, E. Distinguishing the Causal Factors of Induced Seismicity in the Delaware Basin: Hydraulic Fracturing or Wastewater Disposal? Seism. Res. Lett. 2022, 93, 2640–2658. [Google Scholar] [CrossRef]
  24. Grigoratos, I.; Savvaidis, A.; Wiemer, S. Revisiting the Seismicity in the Eagle Ford Shale: The Overlooked Role of Wastewater Disposal. Seism. Rec. 2025, 5, 145–154. [Google Scholar] [CrossRef]
  25. Grigoratos, I.; Poggi, V.; Danciu, L.; Monteiro, R. Homogenizing instrumental earthquake catalogs—A case study around the Dead Sea Transform Fault Zone. Seismica 2023, 2, 402. [Google Scholar] [CrossRef]
  26. Grigoratos, I.; Wiemer, S. Probabilistic identification of seismicity triggered by oil and gas activities and its effects on seismic hazard. Final Technical Report for the USGS Earthquake Hazards Program Grant #G22AP00243. 2023. Available online: https://earthquake.usgs.gov/cfusion/external_grants/reports/G22AP00243.pdf (accessed on 1 May 2025).
  27. Barbour, A.J.; Norbeck, J.H.; Rubinstein, J.L. The effects of varying injection rates in Osage County, Oklahoma, on the 2016 Mw 5.8 Pawnee earthquake. Seism. Res. Lett. 2017, 88, 1040–1053. [Google Scholar] [CrossRef]
  28. Murray, K. Class II saltwater disposal for 2009–2014 at the annual-, state-, and county-scales by geologic zones of completion, Oklahoma. In Open-File Report: OF5-2015; Oklahoma Geological Survey: Norman, OK, USA, 2015. [Google Scholar] [CrossRef]
  29. Aden-Antoniów, F.; Frank, W.B.; Seydoux, L. An Adaptable Random Forest Model for the Declustering of Earthquake Catalogs. J. Geophys. Res. Solid Earth 2022, 127. [Google Scholar] [CrossRef]
  30. Zaliapin, I.; Gabrielov, A.; Keilis-Borok, V.; Wong, H. Clustering Analysis of Seismicity and Aftershock Identification. Phys. Rev. Lett. 2008, 101. [Google Scholar] [CrossRef]
  31. Shapiro, S.A.; Dinske, C.; Langenbruch, C.; Wenzel, F. Seismogenic index and magnitude probability of earthquakes induced during reservoir fluid stimulations. Lead. Edge 2010, 29, 304–309. [Google Scholar] [CrossRef]
  32. Shapiro, S.A. Fluid-induced seismicity; Cambridge University Press: Cambridge, UK, 2015. [Google Scholar] [CrossRef]
  33. Dinske, C.; Shapiro, S. Performance test of the Seismogenic index model for forecasting magnitude distributions of fluid-injection-induced seismicity. In SEG Technical Program Expanded Abstracts 2016; Society of Exploration Geophysicists: Houston, TA, USA, 2016. [Google Scholar] [CrossRef]
  34. Kwiatek, G.; Grigoratos, I.; Wiemer, S. Variability of Seismicity Rates and Maximum Magnitude for Adjacent Hydraulic Stimulations. Seism. Res. Lett. 2024, 96, 920–932. [Google Scholar] [CrossRef]
  35. Theis, C.V. The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. Trans. Am. Geophys. Union 1935, 16, 519–524. [Google Scholar] [CrossRef]
  36. Evans, D.G.; Steeples, D.W. Microearthquakes near the sleepy hollow oil field, southwestern Nebraska. Bull. Seism. Soc. Am. 1987, 77, 132–140. [Google Scholar] [CrossRef]
  37. Armbruster, J.; Steeples, D.; Seeber, L. The 1989 earthquake sequences near Palco, Kansas: A possible example of induced seismicity. Seismol. Res. Lett. 1987, 60, 141. [Google Scholar]
Figure 1. (a) Time history of declustered seismicity rates above M 2.5 and monthly HF and SWD volumes, within the area of Figure 1a. The HF data are incomplete before 2012, the seismicity data are incomplete below M 3 before 2012 and the SWD data are incomplete for 2022. (b) Map with declustered seismicity (M ≥ 2.5; black dots), wells (yellow: HF; blue: SWD) and county/state borders. Data between 2006 and September 2022. The SWD database in Pawnee county is incomplete. See Figure S1 for a map of just the epicenters.
Figure 1. (a) Time history of declustered seismicity rates above M 2.5 and monthly HF and SWD volumes, within the area of Figure 1a. The HF data are incomplete before 2012, the seismicity data are incomplete below M 3 before 2012 and the SWD data are incomplete for 2022. (b) Map with declustered seismicity (M ≥ 2.5; black dots), wells (yellow: HF; blue: SWD) and county/state borders. Data between 2006 and September 2022. The SWD database in Pawnee county is incomplete. See Figure S1 for a map of just the epicenters.
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Figure 2. Map with declustered seismicity (M ≥ 2.5; black dots) and gridded (5 km × 5 km) total stimulation-days. Data between 2014 and 2022. The black diamonds label the four events above Mw 5. Country/state borders are also mapped.
Figure 2. Map with declustered seismicity (M ≥ 2.5; black dots) and gridded (5 km × 5 km) total stimulation-days. Data between 2014 and 2022. The black diamonds label the four events above Mw 5. Country/state borders are also mapped.
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Figure 3. Maps with declustered seismicity (M ≥ 2.5; black dots), county/state borders, gridded cumulative injected (a), or distributed, with D = 2 m2/s, (b) SWD volumes. Data between 2000 and 2021. The black diamonds label the four events above Mw 5. The seismicity data are incomplete below M 3 before 2012 in OK and before mid-2014 in sKN.
Figure 3. Maps with declustered seismicity (M ≥ 2.5; black dots), county/state borders, gridded cumulative injected (a), or distributed, with D = 2 m2/s, (b) SWD volumes. Data between 2000 and 2021. The black diamonds label the four events above Mw 5. The seismicity data are incomplete below M 3 before 2012 in OK and before mid-2014 in sKN.
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Figure 4. Schematic diagram of our hypothesis testing framework. Dashed lines indicate alternative paths.
Figure 4. Schematic diagram of our hypothesis testing framework. Dashed lines indicate alternative paths.
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Figure 5. Declustering results for OK and KN, earthquakes with M ≥ 2.5, between 2000 and 2022. (Left) The nearest-neighbor rescaled distance Rij and time Tij distribution. The color indicates if the events have been classified as background events or aftershocks. (Right) The two stacked nearest-neighbor distance ηij distributions. The dashed black lines correspond to the fit of a Weibull function to both distributions while the black line shows the resulting sum and fit to the overall ηij distribution.
Figure 5. Declustering results for OK and KN, earthquakes with M ≥ 2.5, between 2000 and 2022. (Left) The nearest-neighbor rescaled distance Rij and time Tij distribution. The color indicates if the events have been classified as background events or aftershocks. (Right) The two stacked nearest-neighbor distance ηij distributions. The dashed black lines correspond to the fit of a Weibull function to both distributions while the black line shows the resulting sum and fit to the overall ηij distribution.
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Figure 6. Statistical p-values for SWD between 2012 and 2021, for D equal to 2 m2/s, with declustered earthquakes above M 2.5 overlapped. Only “active” blocks are color-coded. The black diamonds indicate the four events above Mw 5. County/state borders are also mapped. Black rectangles are taken from Skoumal et al. [20] and represent areas with seismicity linked to hydraulic fracturing (HF). The grey polygon represents the SCOOP/STACK plays. See Figures S4 and S6 for an extended version of this map including northern Kansas and for the non-declustered catalog, respectively. See Figures S7 and S8 for D = 1 m2/s or 0.3 m2/s.
Figure 6. Statistical p-values for SWD between 2012 and 2021, for D equal to 2 m2/s, with declustered earthquakes above M 2.5 overlapped. Only “active” blocks are color-coded. The black diamonds indicate the four events above Mw 5. County/state borders are also mapped. Black rectangles are taken from Skoumal et al. [20] and represent areas with seismicity linked to hydraulic fracturing (HF). The grey polygon represents the SCOOP/STACK plays. See Figures S4 and S6 for an extended version of this map including northern Kansas and for the non-declustered catalog, respectively. See Figures S7 and S8 for D = 1 m2/s or 0.3 m2/s.
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Figure 7. HFR values mapped on a 5 km grid, with all declustered earthquakes between 2014 and 2022 above M 2.5 overlapped. Only blocks with at least three events are color-coded. The black diamonds indicate the four events above Mw 5. Country/state borders are also mapped. All not-shown blocks within northern Kansas had HFR values equal to 0.
Figure 7. HFR values mapped on a 5 km grid, with all declustered earthquakes between 2014 and 2022 above M 2.5 overlapped. Only blocks with at least three events are color-coded. The black diamonds indicate the four events above Mw 5. Country/state borders are also mapped. All not-shown blocks within northern Kansas had HFR values equal to 0.
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Figure 8. Statistical p-values for HF between 2014 and 2022, with all declustered earthquakes above M 2.5 overlapped. Only “active” blocks are color-coded. The black diamonds indicate the four events above Mw 5. County/state borders are also mapped. Black rectangles are taken from Skoumal et al. [20] and represent areas with seismicity linked to hydraulic fracturing (HF). The grey polygon represents the SCOOP/STACK plays. See Figure S11 for another version of this map using the constant time-lag.
Figure 8. Statistical p-values for HF between 2014 and 2022, with all declustered earthquakes above M 2.5 overlapped. Only “active” blocks are color-coded. The black diamonds indicate the four events above Mw 5. County/state borders are also mapped. Black rectangles are taken from Skoumal et al. [20] and represent areas with seismicity linked to hydraulic fracturing (HF). The grey polygon represents the SCOOP/STACK plays. See Figure S11 for another version of this map using the constant time-lag.
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Figure 9. Map of OK and sKN, showing all declustered earthquakes between 2014 and 2022 above M 2.5 (grey dots) and in red the subset of events that were in blocks with pHF ≤ 0.05 and fall within the HFR windows. Country/state borders are also mapped.
Figure 9. Map of OK and sKN, showing all declustered earthquakes between 2014 and 2022 above M 2.5 (grey dots) and in red the subset of events that were in blocks with pHF ≤ 0.05 and fall within the HFR windows. Country/state borders are also mapped.
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Grigoratos, I.; Savvaidis, A.; Wiemer, S. Seven Thousand Felt Earthquakes in Oklahoma and Kansas Can Be Confidently Traced Back to Oil and Gas Activities. GeoHazards 2025, 6, 36. https://doi.org/10.3390/geohazards6030036

AMA Style

Grigoratos I, Savvaidis A, Wiemer S. Seven Thousand Felt Earthquakes in Oklahoma and Kansas Can Be Confidently Traced Back to Oil and Gas Activities. GeoHazards. 2025; 6(3):36. https://doi.org/10.3390/geohazards6030036

Chicago/Turabian Style

Grigoratos, Iason, Alexandros Savvaidis, and Stefan Wiemer. 2025. "Seven Thousand Felt Earthquakes in Oklahoma and Kansas Can Be Confidently Traced Back to Oil and Gas Activities" GeoHazards 6, no. 3: 36. https://doi.org/10.3390/geohazards6030036

APA Style

Grigoratos, I., Savvaidis, A., & Wiemer, S. (2025). Seven Thousand Felt Earthquakes in Oklahoma and Kansas Can Be Confidently Traced Back to Oil and Gas Activities. GeoHazards, 6(3), 36. https://doi.org/10.3390/geohazards6030036

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