Decoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting

Decoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting Haritha Chandriyan, Ramakrushna Reddy, Paresh Nath Singha Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3987112/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract This study investigates the collaborative application of fractal clustering patterns and cumulative Coulomb stress (CCS) in the context of earthquake precursory signal identification. We evaluated CCS created by the events based on the period when the Correlation fractal dimension (Dc) commenced falling into relatively lower values. We tested this approach to four strong (M > 7) earthquakes of southern and Baja California, revealing a correlation between these parameters. The crustal readjustment period prior to large earthquakes frequently exhibits a succession of events that result in positive CCS and a higher degree of spatial clustering, indicating low Dc. Preceding strong earthquakes, positive CCS values have been observed concurrently with the onset of low Dc, indicating the potential significance of Dc in seismic hazard assessment studies. We examined these parameters in the Ridgecrest and Baja California regions following the 2010 Mw 7.2 and 2019 M w 7.1 events. Signs of strain were observed in the northwestern region of the epicenters, indicated by the presence patch of low Dc and positive CCS. We observed that earthquake frequency is typically highest in regions with low to medium Dc values. Multiple sections of the Garlock Fault, manifested by low Dc regions, are loaded, posing a significant seismic risk in Southern California. Similarly, the southern segment of the San Andreas fault displays demonstrate low Dc and high stress, has been inactive for a prolonged period. While these faults may be inactive, we must not underestimate the unpredictability of earthquakes. Coulomb stress Correlation Fractal dimension Self-organization Earthquake forecasting Earthquake hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Previous seismic activity in the Eastern California Shear Zone (ECSZ) and adjacent regions has drawn considerable scientific interest. The occurrence of three strong (M w > 7) earthquakes since 1990 (Fig. 1) has highlighted the ECSZ as an area of anomalous seismicity, prompting further investigation. The potential for future M w > 7 earthquakes in the southern and Baja California region, particularly near the Garlock fault and southern segment of the San Andreas Fault (SAF), necessitates a comprehensive understanding of the stress dynamics within the study region (Toda and Stein 2020 ). The prolonged quiescence (500 years) of this faults Figure 1 The four significant earthquakes in Southern and Baja California region are represented by red stars. Green dots indicate the seismicity of the region from 1990 to before the 2019 mainshock. Distribution of earthquakes after the 2019 mainshock is marked by yellow dots. segments, and enhanced coulomb stress on the Garlock fault segment, followed by the occurrence of 2019 Ridgecrest, raises pertinent questions regarding the potential activation of these fault systems (Ross et al. 2019 ; Nanjo 2020 ; Ramos et al. 2020 ). The complex interplay of stress accumulation and fault behavior demands an integrated approach to assessing seismic hazards in the region. If Ridgecrest were to experience another significant shock, Toda and Stein ( 2020 ) described a potential scenario in which a chain reaction would be set off among the Garlock fault and southern SAF portions. Understanding the orientation and magnitude of stresses within the crust holds critical importance (Zoback et al. 2003 ; Moulas et al. 2019 ; Delorey et al. 2021 ; Milliner et al. 2022 ) as it profoundly influences the location, size, and spatial extent of earthquake ruptures. Earthquakes exhibit a fractal distribution pattern (Kagan and Knopoff 1980 ; Kagan 1981 ; Matsuzaki 1994 ; Lei and Kusunose 1999 ). A notable phenomenon is the relative decrease of the fractal correlation dimension (Dc) towards low values during earthquake clustering across both space and time. Researchers have put forward that regions with higher stress levels tend to have lower fractal dimension values (Main 1992 ; Sammonds et al. 1992 ; Maus and Dimri 1994 ; Sunmonu and Dimri 2000 ; Telesca et al. 2001 ; Thingbaijam et al. 2008 ; Mondal et al. 2019 ). Consequently, lower Dc values can be interpreted as indicators of an impending earthquake (Hirata et al. 1987 ; Murase 2004 ; Kumar et al. 2013 ; Roy et al. 2015 ). Previous studies suggest intense clustering accompanied low Dc before the rupture of significant earthquakes, for instance, Mw 7.7 and Mw 7.6 Turkey-Syria earthquakes (Chandriyan and Roy 2024 ), Mw 6.4, 2020 Puerto Rico (Mangalagiri et al. 2023 ), Mw 7.8 New Zealand (Mondal et al. 2019 ), Mw 9 Indian Ocean earthquake, M s 7.8 Izmit, M s 8.5 Denali (Roy and Nath 2007 ) etc. We make use of these strong earthquakes with the perspective of determining how the low Dc values and static stress changes are interconnected. In most cases, monitoring occurrences of smaller earthquakes is essential because they often accompany larger earthquakes in preparation. The studies suggest that seismicity patterns can occasionally correlate with minor stress changes (Nalbant et al.; Harris et al. 1995 ; Deng and Sykes 1997 ). To validate the low Dc induced by fractal clustering of earthquakes, we use the Coulomb method for static stress calculation. It should be noted that we have not considered the influence of any of the past big earthquakes. While examining the static stress changes, earthquakes with high magnitudes (M > 5) have generally received more attention. However, we will focus on events of lower magnitude (M ≥ 2.5) to observe even the smallest variations reflecting in the stress from smaller to intermediate earthquakes during the interseismic period. Here, we attempt to understand how these events can be useful in tracking the triggered zones along with the spatiotemporal variation in Dc. Several investigations have been conducted to assess Coulomb stress changes by examining the impact of past earthquakes in the study region (King et al. 1994b; Deng and Sykes 1997 ; Parsons and Dreger 2000 ; Fialko et al. 2002 ; Kilb et al. 2002 ; Felzer et al. 2002 ; Smith and Sandwell 2003 ; Meng and Peng 2014 ; Verdecchia and Carena 2015 ; Mancini et al. 2020 ; Pope and Mooney 2020 ; Im et al. 2021 ). Nevertheless, our study diverges significantly from this approach. The core premise of our research lies in identifying the specific influence of static stress originating from earthquake clusters that precede the mainshocks. Our focus is exclusively directed toward earthquakes that are associated with a decrease in the fractal correlation dimension (Dc). Based on the observed correlation between Dc and Coulomb stress from our retrospective study, we attempt to assess distinctive patterns of seismic patterns after the 2019 Ridgecrest sequence and the 2010 El-Mayor Cucapah earthquake. 2. Data and Methods Newly available focal mechanism catalogue derived through deep learning method (Cheng et al. 2023 ), was sourced from Southern California Earthquake Data Center (SCEDC), https://scedc.caltech.edu/data/alt-2022-cheng.html . This catalogue spans earthquake with high-resolution parameters spanning from January 11, 1981, to November 6, 2021. For events beyond this timeframe, the focal mechanism data was obtained from the SCEDC website. The updated catalog by Cheng et al. ( 2023 ) and parameters obtained from the GCMT catalogue for Mw > 7 are listed in the supplementary file (Table S1 ). The input data for fractal analysis was obtained from SCEDC, https://service.scedc.caltech.edu/eq-catalogs/date_mag_loc.php .. The initial stages of data processing and analysis were performed using ZMAP (Wiemer 2001 ), which can be accessed at http://www.seismo.ethz.ch/en/research-and-teaching/productssoftware/software/ZMAP/ ). Subsequent fractal analysis was conducted with FractalAnalyzer (Roy and Gupta 2015 ), https://sourceforge.net/projects/fractalanalyzer/ . It is a Matlab-based tool, facilitates the examination of spatial and temporal variations within the earthquake clusters. Preliminary analysis of the Dc carried out across the southern California (SoC) and Baja California (BaC) regions has revealed a consistent decline in Dc prior to earthquake, Mw 7.3, 1992 Landers earthquake, Mw 7.1, 1999 Hectormine, Mw 7.2, 2010 El-Mayor Cucapah earthquake, and Mw 7.1, 2019 Ridgecrest earthquake (Chandriyan et al. 2022 ). Based on our preliminary investigations, we pinpointed the approximate period when Dc exhibited a sharp drop. In Table S2 , the previous Dc values are for reference. The Coulomb failure criterion states that rupture along a plane occurs when shear stress (τ s ) exceeds the restraining force of friction that exists between fault blocks (King et al. 1994). This restraining force derives from the difference between the coefficient of friction (µ s ) and pore fluid pressure (p) within the rock, multiplied by the normal stress (τ n ). By disregarding the pore pressure, the change in coulomb stress can be expressed as ΔCS = Δτ s - µ s Δ τ n . Key parameters for this analysis include Poisson’s ratio set as 0.25 and µ s as 0.4 (Stein et al.,1994), and a shear modulus of 0.8 MPa.Coulomb 3.3 software is used for the CCS calculation and can be obtained from https://www.usgs.gov/node/279387 (Toda et al. 2011 ). For each significant earthquake, we chose the receiver fault orientation along the maximum magnitude earthquake fault plane from the dataset under consideration. In subsequent sections, the notations “analysis F” and “analysis M” represent CCS estimations before the foreshock and mainshock, respectively. Considering that the maximum depth of seismic activity identified for the study area is approximately 15 km (Hauksson 2011 ), our CCS calculations were limited till the depth mentioned above for all the upcoming analyses. The assessment of Dc variation after the 2019 Ridgecrest and 2010 El-Mayor Cucapah has been done using the modified Grassberger and Procaccia ( 1983 )method. The correlation integral provides the cumulative correlation function (C(r)) which depends on earthquake pairs (N) of seismicity and cluster, epicentral distance (r ij ), and length scale (r) between them. $$\text{C }\left(\text{r}\right)\text{=}\frac{\text{2}}{\text{N(N-1)}}\text{ }\sum _{\text{j=1}}^{\text{N}}\sum _{\text{i=j+1}}^{\text{N}}\text{H(}{\text{r-r}}_{\text{ij}}\text{)}$$ 1 The FractalAnalyzer can compute multifractal dimension parameter Dq (q = 0 to 22), which is defined as \({\text{D}}_{\text{q}}\text{=}\frac{\text{log}{\text{C}}_{\text{q}}\text{(r)}}{\text{log}\text{r (r→0)}}\) (2) Where Cq (r) is the correlation integral of q th order fractal $${\text{C}}_{\text{q}}\left(\text{r}\right)\text{= }{\left\{\frac{\text{1}}{\text{N}}\text{ }\sum _{\text{j=1}}^{\text{N}}{\left[\frac{\text{1}}{\text{N}}\text{ }\sum _{\text{i=j}}^{\text{N}}\text{H}\left(\text{r-}\text{}{\text{X}}_{\text{i}}\text{-}{\text{X}}_{\text{j}}\right)\right]}^{\text{q-1}}\right\}}^{\frac{\text{1}}{\text{q-1}}}$$ 3 H is the Heaviside step function. Eq. ( 3 ) is the modified version of Grassberger and Procaccia ( 1983 ), and FractalAnalyzer was developed on this basis. When q = 2, it corresponds to the fractal correlation dimension (Dc). The previous study adopted a consistent choice of 50 events per window for all analyses to identify discernible Dc patterns in the southern and Baja California regions (Chandriyan et al. 2022 ). Consequently, we have taken 50 events/windows for fractal analysis of the post-2019 Ridgecrest series (July 07, 2019, to December 31, 2022) and the post-2010 El-Mayor Cucapah earthquake (April 05, 2010, to December 31, 2022). The computation of Dc for the input fractal data involves evaluating the correlation among the initial 50 earthquakes using Eq. 3 . This procedure has been iteratively applied to the subsequent windows. The minimum magnitude completeness (Mc), obtained through the Gutenberg-Richter relationship applied to the declustered catalogue (Reasenberg 1985 ), yields a value of Mc 2.1 for both the Ridgecrest and Baja California regions (Figs S1 a and c). Detailed information regarding the input data utilized for fractal analyses for both the regions are provided in the supplementary section (Table S3 and S4). 3. Results The Mw 7.3 Landers mainshock, which ruptured in ECSZ on June 28, 1992, was preceded by the Mw 6.1 Joshua Tree earthquake on April 23, 1992. Analysis F indicates that the epicentral region of M w 6.1 was surrounded by positive values of coulomb stress continuously from depths 5 to 10 km and 14–15 km. Analysis M demonstrates a continuous and elevated distribution of positive static stress extending through the depths, from 0 to 15 km, particularly around the epicentral area of the impending mainshock. The CCS estimated at 6.5 km and 15 km depths is illustrated in Figs. 2 b and c, respectively. Both these analyses suggest the distribution of positive static stress in the vicinity of future shocks. Moreover, the positive stressed region is more spatially distributed at 6.5 km (Cheng et al. 2023 ) than the GCMT depth. In addition, a background seismicity analysis was carried out before Mw 6.1 and Mw 7.3, indicating a highly stressed region close to the future earthquake (Fig. S2 ). Investigation of CCS induced by events from the onset of low Dc to before the 1999 Hectormine earthquake demonstrates positive stress patches at 6 km and 7 km (Fig. 2 d), close to the Hectormine hypocentral region. The results are similar when the stress is calculated along the rupture plane of Mw 7.1 and along the maximum earthquake (M4.94) rupture plane. Similar to the Landers and its foreshock, a correlation between Dc and high stress can be seen for this mainshock. According to the deep-learning data and GCMT, centroid depths for this earthquake are 1.72 km and 15 km, respectively. We did multiple analyses changing the calculation depth by incrementing 1 km in each step; however, only at the depths mentioned above, i.e., at 6 km and 7 km, could the trace of positive stress be observed. Background seismicity analysis (01.01.1994-16.10.1999, 3.5 ≤ M ≤ 4.97) computed along maximum magnitude earthquake rupture shows stress shadow at 0–5 km and 8–15 km and stress positive region at 6 and 7 km, shown in Fig. S3 . The third significant earthquake to strike the California region was the Mw 7.2 El-Mayor Cucapah earthquake in 2010, which was preceded by a foreshock of Mw 5.8 in December 2009. In analysis F, our observations reveal positively stressed regions extending from the surface to 6 km depth, the CCS distribution at 6 km is shown in Fig. 3a. For analysis M, we detected a region of positive stress near the hypocentral location of the Mw 7.2 event. For this mainshock, CCS induced by events since the onset of low Dc also contributes to enhanced stress. The CCS measurements exhibit a more widespread distribution of stress at 8 km (Fig. 3b) compared to the centroid depth of 12.8 km (as per GCMT), where a stress shadow is evident (Fig. S4 ). Background seismicity since 2005, assessed along the fault plane of the Mw 5.8 foreshock, reveals elevated stress levels at depths of 13.5 km and 14.7 km. Upon analysing the same dataset, considering the receiver fault orientation along the fault plane of the maximum earthquake magnitude, trigger zones of approximately 0.2 bar magnitude are observed at the mainshock location. The computed background seismicity prior to the mainshock rupture implies an increased stress region at the hypocenter of the mainshock (at 8 km), as depicted in the supplementary Fig. S5 . Figure 3a ) CCS induced by events at 6 km before the occurrence of Mw 5.8 calculated the considering minimum strike, dip, rake as 149 0 /68 0 /159 0 , b) CCS induced by events before the occurrence of Mw 7.2 at 8 km measured along, SDR:170 0 /78 0 /-166 0 . Major faults in the area are IF: Imperial Fault, LSF: Laguna Salada Fault, and CPF: Cerro Prieto Fault. The 2019 Mw 7.1 Ridgecrest was preceded by Mw 6.4 foreshock approximately two days prior to the mainshock (Ross et al. 2019 ; Huang et al. 2020 ; Lei et al. 2021 ). Analysis F reveals localized positive stress in the Mw 6.4 hypocentral region since the onset of low Dc. Two analyses have been done keeping receiver fault orientation along the maximum earthquake plane and the future foreshock plane; both results show the existence of trigger zones at 12 km. The presence of trigger zones at 12 km (Fig. 4a) is evident for this event. This distinct patch of positive stress region implies that monitoring small earthquake activity can provide insights into future trigger points. In analysis M, significant stress is observed around the hypocenter of Mw 7.1 at a depth of 2 km (Fig. 4b), and a stress shadow is observed at 12 km (Fig. S6), considering both the receiver fault orientation. The enhanced stress following the low Dc accentuates the correlation between these parameters. An inspection of background seismicity has been done before the foreshock as well as the mainshock, reveals enhanced stress regions around the hypocenter area (Figs S7a and b). Figure 4 The CCS changes induced by the earthquakes, a) before the rupture of Mw 6.4 at 11.9 km considering SDR as 317 0 /52 0 /173 0 respectively, and b) before the rupture of Mw 7.1 at 2 km for the SDR, 318 0 /89 0 /-173 0 . Earthquakes are represented by white stars and major faults are marked by grey dash lines. GF: Garlock fault, SNF: Sierra Nevada fault, 1-Little Lake fault zone, 2-Airport Lake fault zone. 3.1 Insights into future seismicity In the Ridgecrest region, we examined 4493 earthquakes, out of it 178 clusters that are identified through the Reasenberg algorithm with 95 percent confidence. The analysis of depth-wise seismicity distribution during this interval reveals a notable concentration of earthquake occurrences up to 10 km, with only a few events crossing beyond this depth (see Fig. S1 b) This is in accordance with the findings of Hauksson et al. (2012), who reported a sudden decline in earthquake occurrences below 10 km. We analysed the spatial variation of Dc with respect to time (Fig. 5 ), a substantial Dc fluctuation was observed between July 07, 2019, and September 24, 2019, with the values varying within the range of 0.4 and 1. After September 24, 2019, a rise in Dc indicates a lesser rate of earthquake occurrence, suggesting subsiding aftershock activity. The peak Dc value (1.02) was recorded on May 24, 2020, followed by a gradual decline that led to its lowest point of 0.10. Within this transitional phase, two earthquakes (M > 5) occurred on June 04, 2020, and June 24, 2020. The seismicity associated with the events and Dc variation is provided in the supplementary file (Fig. S9). The lowest Dc (0.09–0.1) was observed from July 06, 2020, to July 28, 2020, a year after the rupture of the 2019 sequence. In general, a consistent fluctuation in Dc has been observed from April 21, 2021, to December 31, 2022, accompanied by less clustering of earthquakes. For the same period, we measured the CCS, indicating a positively stressed region NW of the 2019 Ridgecrest epicenter. A continuous assessment of CCS of events that occurred from July 07, 2019, to December 31, 2022, was carried out at various depths, employing precise 1 km intervals, and extended up to a depth of 15 km. Results suggest that additional stress has been introduced along the Garlock fault after the Ridgecrest earthquakes, mainly below 10 km (Fig.S8). Also, positive stress is observed within the Coso geothermal area, the Sierra Nevada fault, and a segment of the Homestead Valley fault at shallow depths (Fig. 6 a). We mapped the spatial variation of Dc up to December 31, 2022, to investigate whether the low Dc zones fall in high-stress regions. The prominent stressed region near latitude 36.5 0 (Fig. 6 a) is characterized by a low Dc patch (Fig. 6 b). The spatial distribution of high static stress patches is highly correlated that of the low Dc zones, suggesting a good relationship among these parameters. To assess whether the earthquakes occurring after this timeframe aligns with low Dc zones, we plotted events with M ≥ 2.5 within the period spanning from December 31, 2022, to June 30, 2023. Fig. 6b illustrates that the highest frequency of earthquake occurrences is predominantly concentrated within regions characterized by low Dc values. Moreover, we identified the windows which are causing low Dc and then mapped those windows individually. In this context, we chose two distinct windows to calculate CCS and map the spatial variations in Dc. In the first window, the spatial fluctuations of Dc from May 09, 2020, to July 24, 2020, are depicted (Fig. 7a). The seismic events occurred after July 24, 2020, until June 30, 2023, is superimposed over the Dc map demonstrates that earthquakes of M ≥ 2.5 or greater fall predominantly in areas characterized by low to medium Dc values. Notably, Earthquakes with magnitudes M ≥ 4 are consistently observed to occur within regions characterized by low Dc values. In the second window, we considered the Dc for the last six windows (83–89), the period ranges from December 24, 2021, to December 31, 2022. The spatiotemporal variation is correlated with seismic events that have occurred from December 31, 2022 June 30, 2023. A high percentage of the earthquakes (M ≥ 2.5) occurred under this region fall in low to medium Dc regions (Fig. 7b). Our findings reveal the presence of a low Dc patch across different segments of the Garlock fault. Intriguingly, high Dc values are discerned along the southeastern side of the Mw 6.4 epicenter, which is the central segment of the Garlock fault. Figure 7a ) Spatial variation of Dc for window no. 69–74 (May 09, 2020-July 24, 2020), earthquakes (M ≥ 3.5) from July 24, 2020 to July 31, 2021 are represented by pink dots. The green star indicates events with a magnitude greater than 4, b) Spatial variation of Dc for the last 6 windows (June 02, 2021- October,12 2022). Pink dots indicate earthquakes (M ≥ 3.5) occurred in the region from October 12, 2022 to June 30, 2023. Additionally, clear fluctuations in Dc values are observable both before and after each seismic event. Most fluctuating Dc values are seen after the 2010 earthquake which lasted till March 14, 2011. The minimum Dc value (Dc = 0) was observed on February 1st, 2017. Reduced variability in Dc values was noted during two distinct periods: from August 1st, 2013, to March 10th, 2016, and from January 29th, 2018, to June 12th, 2020. Notably, no significant earthquakes occurred within these specified time intervals. The clusters observed within the latitudinal range of 32.6 0 to 33 0 (Fig. S10b) may represent a potential zone for a future seismic event, seismicity density has been seen as high with an associating Dc drop. The calculated CCS for earthquakes following the 2010 mainshock reveals a positive stress pattern within the range of 33 0 to 33.5 0 latitude and − 115 0 to -116 0 longitude (Fig. 9 a). The analysis indicates a higher concentration of trigger zones at a depth of 12 km while considering a depth range of 0 to 30 km. The spatial distribution of Dc values, superimposed with seismic events occurring after December 2022 until June 30, 2023, suggests an elevated stress region in the NW to the El-Mayor earthquake rupture location (Fig. 9 b). A notable resemblance has been observed between the distribution of positive stress and the area with low Dc values, as we observed in the case of Ridgecrest region. Spatial variation of Dc near latitude, 32.8 0 /33.2 0 and longitude, -115.4 0 and − 115.8 0 shows a large low Dc value patch with higher number of events (M > 4 and 5). Following the 2010 earthquake, two periods indicated rapid changes in Dc from low to high values, which were analyzed separately. In the first part, we mapped the Dc variation (December 23, 2015- January 01, 2017) and plotted the earthquakes that followed this period till June 06, 2020. Nearly 70 percent of earthquake fall in the low to moderate Dc regions (Fig. 10a). A similar trend was observed for the second part with 75 percent of events falling in the low Dc regions (Fig. 10b). To map Dc, we considered the events during 30 April 2020 to 18 August 2021 then an earthquake of M > 2.5 followed by this day to 30 June 2023 were plotted. Individual low Dc window analysis in the Baja California region implies that the chance of occurrence of earthquakes is higher in low to moderate Dc region than in high Dc. In general, a higher likelihood of earthquake occurrences in areas characterized by low to moderate Dc values compared to regions with higher Dc values is observed in BaC. Figure 10a ) Spatial variation of Dc for the first low Dc window (w.no. 230–248, December 23, 2015- March 14, 2017). Earthquakes (M ≥ 3.5) occurred from March 14, 2017, to June 30, 2020 marked by pink dots, b) Dc map for second Low Dc window (w.no. 270–277, August 18, 2021- October 203, 2022), M ≥ 3.5 earthquakes occurred during August 18, 2021 to October 03, 2022 is shown by pink dots. 4. DISCUSSION In the case of the 1992 Landers mainshock and foreshock, high stress was observed near the hypocenter region when considering the events occurring during the low Dc period. According to Masterlark and Wang ( 2002 ), the identified hypocenter depths for Mw 7.3 and Mw 6.1 are 8 km and 11–14 km, respectively. (Bennett et al. 1995 ) reports the emergence of a cluster around the Mw 7.3 center at a distance of about 7 km. Furthermore, Hauksson et al. ( 1993 ) propose that the mainshock rupture occurs between 3–6 km. Cohee and Beroza ( 1994 ) state that the hypocenters of fault segments linked to the Lander’s ruptures, such as the Homestead Valley and Camp Rock faults, are located up to 15 km. The continuous distribution of high stress till 15 km estimated based on low Dc values could represent the early stressed state of the multi faults involved in the Landers rupture. The larger spatial extent of positive stress at 6.5 km compared to 15 km suggest the distribution of more rupture sources at former depth, which is derived through deep-learning techniques. The substantial disparity between the centroid depth value given by Cheng et al. ( 2023 ) and the values provided by the Global Centroid Moment Tensor (GCMT) highlights the remarkable precision of the dataset generated through machine learning techniques. This also highlights the significance of incorporating novel methodologies in earthquake research. The high stress observed at depths of 6 and 7 km prior to Hectormine probably indicate the impending event. Parson and Dreger (2000) suggest hypocentral depth of 6 km, whileOglesby et al. ( 2003 ) proposed a nucleation depth of 7.5 km. Different hypocentral values are reported in the literature (Ji et al. 2002 ; Pollitz and Sacks 2002 ). Therefore, the high stress observed at 6 and 7 km from our CCS analysis can be considered as the preparatory phase of the Hectormine event. According to Hauksson et al. ( 2002 ), 6 km also can also be correlated with the depth of aftershocks indicating pre-existing weak zones. Our investigation indicates the presence of stress levels of approximately 0.2 bar at the hypocentral region (considering earthquakes since 1998). This finding aligns with the observations made by Masterlark and Wang ( 2002 ), who reported comparable stress values for earthquakes that occurred, followed by the 1992 Landers rupture. However, our study could delineate the trigger zone using the earthquake data year before the rupture using Dc drop period. A similar correlation of Dc and high stress is seen for the 2010 foreshock and mainshock. This sequence is regarded as one of the best examples of poorly estimated hypocentral depth (Yu et al. 2019 ), cause of multiple values provided by researchers (Hauksson et al. 2011 ; Sarychikhina et al. 2015 ; Fletcher et al. 2016 ). Using depth phase modeling Yu et al. ( 2019 ) put forward the focal depth as 8 km (peak depth = 6 km) had stated more earthquake activity between 3–10 km. While we calculated the CCS before the rupture of Mw 7.2, the maximum stress is seen at 8 km. Therefore, the trigger zones observed before Mw 5.8 and Mw 7.2 rupture can correlate with the Dc variations. Our study highlights that the Ridgecrest region has experienced an accumulation of stress owing to seismic events taking place in the adjacent areas of its epicenter (Figs. 4a and b). The possibility of triggering the Ridgecrest event by Hectormine was ruled out due to a study by Pope and Mooney ( 2020 ). Moreover, the findings by Tong et al. ( 2021 ) confirms the above statement which put forward that rupture of this mainshock is primarily due to the local stress field. A distinct sign of a confined stress zone is observable before the Mw 6.4 earthquake rupture at 11.9 km and 12.7 km. The hypocenter of the Mw 7.1 earthquake exhibits a stress level of approximately 0.2 bar before the rupture of the Mw 6.4 foreshock, implying that the region was already under a state of increased stress (Figs. 4a and b). Interestingly, the trend of positive linear stretch seen along Mw 7.1 has correlated with its ruptured fault line, NE-SW trending fault ruptures are reported by researchers (Pollitz et al. 2020 ; Shelly 2020 ). Therefore, we propose this as an early indication of the Mw 7.1. Many focal depths have been suggested for 2019 earthquakes; relatively a shallow depth (1–8 km) is observed for Mw 7.1 and a depth range for Mw 6.4 is given as 8–12 km (Ross et al. 2019 ; Lomax 2020 ; Plesch et al. 2020 ; Pollitz et al. 2020 ; Tong et al. 2021 ). Jin and Fialko 2020 ) reported peak moment release was observed at 3–4 km, the recent focal mechanism catalogue provides ~ 2 km as centroid depth. Therefore, we did a CCS analysis considering the shallow depth in the case of the mainshock and the relatively deeper depth for the foreshock. Barnhart et al. ( 2019 ) estimated stress of approximately 0.01–0.02 bar at Mw 7.1 hypocenter a few minutes before its rupture. Here, we observed nearly 0.2 bar before Mw 6.4 and Mw 7.1. A study by Jin and Fialko ( 2020 ) points out the failure of dynamic stress to culminate rupture of Mw 7.1, though its value was higher than static stress. This suggests the importance of monitoring CCS of earthquakes occurring in the low Dc windows. Considering the observations, we can infer that the assessment of CCS variation in southern and Baja California implicates the possibility of a strong correlation between low Dc and increasing crustal stress before the occurrence of strong earthquakes. Therefore, this study underscores the potential of integrating these two parameters for hazard assessments in seismically active regions. We can further explain this earthquake behaviour through Self-organized criticality (SOC), which comprehends how incremental stress contributes to significant stress bursts. This theory proposes that dynamic systems can spontaneously reach a critical state, where a minor event can trigger a chain reaction, potentially resulting in a catastrophic outcome. The small magnitude events of interseismic periods can contribute to accelerating the crust to attain the state of SOC and later disrupt it. In the study region, we observed fractal clustering preceding the strong events resulting in low Dc (Chandriyan et al. 2022 ), resulting from crustal organization towards the rupture. Moreover, this study identifies the positive stress close to each earthquake’s hypocenter. Using the Grassberger and Procaccia ( 1983 ) algorithm, Murase ( 2004 ) investigated pre-seismic earthquake clustering and the associated variations in Dc preceding the 2003 MJ 8 Tokachi-oki earthquake, indicating a shift to smaller Dc values five years before the mainshock. While studying the fractal characteristics of 2020 Mw 6.4, Puerto Rico earthquake, we observed a decline in Dc values and high stress in the vicinity of epicenter(Mangalagiri et al. 2023 ). Similar correlations were observed between the 2023 Kahramanmaras twin ruptures and the 2009 Fiordland earthquakes (Mondal et al. 2019 ; Chandriyan and Roy 2024 ). Thereby, it indicates the possibility of using Dc as a stress indicator and a numeric precursory signal. The highly fluctuating Dc followed by the strong events implies the results of stress liberation and stress increase along certain segments across different fault (Chandriyan and Roy 2024 ), manifested through aftershocks. In the case of 2010 and 2019 earthquakes, intense aftershock activity has been reported (Hauksson 2011 ; Kroll et al . 2013; Ross et al . 2017; Liu et al . 2019; Shelly 2020 ; Shcherbakov 2021). Although, relatively low Dc precedes the major earthquakes (e.g., Mondal et al. 2019 ; Roy and Nath 2007 ; Roy and Padhi 2007; Chandriyan et al. 2022 ; Chandriyan and Roy 2024 ), intermediate events also display similar characteristics. One such example is, two M > 5 earthquakes that ruptured in 2020 in Ridgecrest region during the transition from high to low Dc phase. In the Ridgecrest region, the spatial variation of Dc over three years shows localized small Dc zones trending NW direction that is similar to the distribution of CCS. This finding strongly indicates a significant correlation between stress patterns and Dc changes. A similar result is observed for post-2010 analysis of seismicity. The spatial variation of Dc mapped post-occurrence of the 2010 and 2019 earthquakes as well as the individual windows, indicate that the low-medium value of Dc regions contains a higher percentage of earthquake occurrence. As we measure Dc based on the relative clustering of earthquakes, the region that accumulates strain is displayed by a high frequency of events, therefore showing low Dc values. Therefore, the high Dc regions contain a lesser number of earthquakes. By monitoring the response of Dc over time and focusing on CCS computations for specific clusters, we can obtain valuable insights into potential future rupture zone locations. The region characterized by a large extent of low Dc values to the NW of the 2010 earthquake and the 2019 Ridgecrest earthquake could potentially be linked to geothermal fluid-related activities of the Coso and Salton Sea geothermal field. The Coso region is renowned for its high seismic activity, making it one of the most active areas in Southern California (Hauksson et al. 1995 ).Feng and Lees ( 1998 ) identified NW stretch of microearthquakes constituting high seismicity fracture zones across the Coso region. In addition to induced seismicity(Brodsky and Lajoie 2013 ; Llenos and Michael 2016 ), the Salton Sea geothermal field witness’s multiple earthquake swarms, Hauksson et al. ( 2013 ) reported.Trugman et al. ( 2014 ) explain the contributions to seismicity from anthropogenic causes as well as swarms in the Coso and Salton Sea geothermal regions, highlighting that swarms play the main role as contributors. As a result, the parameter changes over these regions cannot be considered an anomalous seismicity pattern. A continuous stress readjustment occurs in the Ridgecrest and adjoining regions as evidenced by the lowest Dc corresponding to the two June 2020 earthquakes. Interestingly, the lack of positive CCS and high Dc observed south of the 2019 Ridgecrest suggests the possibility of a barrier south of its hypocenter. This can be attributed to the presence of a low-velocity zone at the Garlock fault in the southeast, effectively facilitating the halt of seismic wave propagation (White et al. 2021 ). The Garlock fault, a left-slip fault in southern California, has shown low seismic activity and aseismic creep in its western segment, a few small earthquakes, and no creep in its eastern segment (Astiz, 1983). This fault has remained inactive for a considerable period (Madugo et al. 2012 ; Hatem and Dolan 2018 ). The fault’s earthquake recurrence is irregular, with the most recent surface-rupturing event occurring between A.D. 1450 and 1640 (Dawson et al. 2003 ). Immediate to the 2019 events, scientists reported enhanced stress distribution along this dormant fault (Barnhart et al. 2019 ; Lozos and Harris 2020; Nanjo 2020 ). The 2019 Ridgecrest earthquake sequence did not cause the Garlock fault to fail, but it did undergo postseismic creep and an earthquake swarm (Ramos et al. 2020 ). As per our analysis, we have observed a distinct region of localized positive stress along the western segment of Garlock fault preceding the occurrence of Mw 6.4. The low Dc areas along the western as well as the eastern (with respect to Mw 7.1 epicenter) part of the Garlock fault could be an indication of high crustal stress accumulating along these segments. Barnhart et al. ( 2019 ) suggests that Garlock fault is sensitive to static stress changes. Moreover, Nanjo ( 2020 ) spotted a low b value patch along this fault segment, is also an indication of high stress. Our study implicates the possibility of future events along the western and eastern segments as low Dc were distributed across these segments.Astiz and Allen (1983) proposed that the likelihood of the eastern segment of the Garlock fault rupturing is higher than the western segment’s if it behaves similarly to the San Andreas fault. In a recent study by (Andrew et al. 2023 ), the structural complexity of the eastern segment of the Garlock fault were highlighted, implying the need for reconsidering the present seismic hazard assessment models incorporating multi-stranded fault. Intriguingly, our research indicates that the centre segment of the Garlock fault is seismically calm, as reflected by high Dc, indicating the absence of major stress changes. The finding is consistent with Wang and Zhan (2020) observations, which identified inactivity across this region, followed by the 2019 earthquake sequence. Based on these observations, there is little chance that a major earthquake will rupture the segment. Baja California encounters a higher frequency of earthquakes compared to the Ridgecrest, an important segment of the North America-Pacific plate boundary (Dokka and Travis 1990 ; Hauksson et al. 1995 ; Jin and Fialko 2020 ; Thompson Jobe et al. 2020 ). An interesting pattern we noticed was a periodic decrease in Dc values following Mw 7.2; Chandriyan et al. (2024) reported a similar pattern for the Mw 7.7, 06 February 2023, Nurdağı-Pazarcık earthquake. This mainshock occurred at the Eastern Anatolian Fault Zone, where the Arabian and Anatolian plates slide past each other by left-lateral movement. In the case of 2023 Turkey- Syria doublet, the pattern was halted soon after the rupture of 2020 Mw 6.7 event. If the Baja California region do not undergo any major stress change, we can possibly anticipate further drop in Dc toward the end of 2024. By saying that, we do not know the exact cause of this episodic behaviour of seismicity in Baja California. Another similarity observed is that Mw 7.1 and 7.6 did not rupture along the main faults; therefore, the unanticipated rupture region further put forward the structural heterogeneities and complex earthquake dynamics of these events. The current stress state in Baja California implies the existence of positive CCS mainly along southern SAF, Brawley seismic zone boundary, Cerro Prieto fault, and NW of 2010 epicenters.(Fialko 2006 ) highlighted the prolonged dormancy of the SSAF lasting for around 257 years. He cautioned about the potential end of the interseismic period, emphasizing the critical significance of continuously monitoring the rate at which strain accumulates. The increase in crustal stress near this fault line has been reported in the literature (Freed and Lin 2002 ). The Brawley seismic zone boundary binds the southern SAF to the Cerro Prieto fault, and its earthquake activity depends mainly on the movement of the former (Hauksson et al. 2002 ). This region witnesses small to intermediate magnitude earthquakes, and the recent earthquake swarm M5.25, 05 June 2021, occurred in this seismic zone, suggesting the need for a detailed seismic study in this area. In addition, our study detected spatially clustered events (latitude: 32.8 0 and 33 0 ), which could indicate a potential future earthquake rupture zone. Interestingly, the distribution of a more positive CCS region found at a depth of 12 km suggests enhanced crustal stress changes at this depth. A recent study by(Zhang et al. 2022 ) identified high-velocity crust from 5 to 12 km, marking the termination of the seismogenic zone at 12 km. Therefore, the high number of trigger zones we observed could be due to these high-velocity rocks. Both the southern SAF and the Garlock fault pose significant threats to society, as they are both capable of generating high-magnitude (M > 7.5) earthquakes (Olsen et al. 2006 ). Our study indicates the presence of stress accumulation coupled with low Dc values along these fault segments. As of today, an accurate quantification of threshold triggering stress cannot be estimated. It has been stated that a Coulomb stress change as small as 0.1 bar can influence the aftershock locations (Reasenberg 1985 ; King et al. 1994a; Hardebeck et al. 1998 ; Anderson and Johnson 1999 ). However, it is unclear whether a static stress change of less than 0.1 bar can cause or delay an earthquake (Harris 2000 ). According to (Freed 2005 ), critical stressed faults can be ruptured with minimal stress increase. Furthermore, Ziv and Rubin ( 2000 ) concluded that central California has no minimum triggering stress. As a result, we cannot ignore the localized stress change ranging from 0.1 to 0.2 bar during the interseismic phase. Therefore, through implementing the Dc changes, we can identify highly stressed crustal zones, which can be instrumental in forecasting potential seismic hazards and thereby mitigating the extent of their impact. 5. Conclusions Fractal clustering pattern of earthquakes that occurred during the interseismic period can be of great significance in terms of forecasting purposes. In this study, we focussed on clusters of earthquakes that occurred during the decreasing phase of Dc. Based on the earlier study, the numerical precursors for 1992 Landers, 1999 Hectormine, 2010 El-Mayor Cucapah, and 2019 Ridgecrest were found. Here, the CCS examined prior to both foreshocks and mainshocks revealed the existence of positive stress near the hypocentral regions. The conspicuous observation is the increased CCS surrounding hypocentral regions subsequent to the reduction in Dc values. As a result, our study confirms the relationship between Dc values and static stress across for four major earthquakes in the Southern and Baja California regions. Since their rupture, we further explored the seismicity patterns in regions surrounding the Mw 7.2, 2010 and Mw 7.1, 2019 earthquakes. A distinct pattern has emerged in the fractal analysis of post-2010 and 2019 earthquakes. A near-constant trend of Dc is identified in the Ridgecrest region, suggesting a downtrend in seismicity. In the case of Baja California, an oscillatory nature of Dc is observed around a four-year interval period. We found that upcoming events tend to concentrate within areas characterized by relatively low to medium Dc values. The Garlock Fault, recognized for its aseismic creep activity in the aftermath of the 2019 Ridgecrest earthquake, displays signs of elevated stress in multiple sections. Additionally, the spatial distribution of Dc implies the presence of regions with lower Dc values along this fault segment. The notable trend is the accumulation of stress in the northwestern area of Mw 7.1 and Mw 7.2 earthquakes, which is evident from the spatial distribution of CCS and Dc values, indicating geothermal energy extraction activities and earthquake swarms. The southern SAF and the Brawley seismic zone boundary demonstrate low Dc and high stress, demanding a detailed seismic hazard study. Highlighting the extended periods of quiescence observed in both the Garlock fault and the southern San Andreas Fault is of utmost importance, as these factors demand substantial attention in studies focused on evaluating seismic risks. Declarations The authors declare that no funds, grants, or other support were received during the preparation of the manuscript. Competing Interests T he authors have no relevant financial or non-financial interests to disclose Author Contributions Haritha Chandriyan contributed to data curation, methodology, formal data analysis, investigation, result interpretation and visualization, writing-original draft, and review, and editing of the draft. Ramakrushna Reddy was involved in validation, supervision, and manuscript review, and editing. P.N.S Roy contributed to conceptualization of the study, methodology, supervision, and manuscript review. Acknowledgments The authors thank the Indian Institute of Technology Kharagpur for providing the opportunity to conduct this research. We express our gratitude to the creators of Coulomb, Zmap, and Fractal Analyzer softwares. Author Haritha Chandriyan thank IIT Kharagpur for providing the doctoral fellowship. 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Bulletin of the Seismological Society of America 110:1765–1780. https://doi.org/10.1785/0120200169 Tong P, Yao J, Liu Q, et al (2021) Crustal Rotation and Fluids: Factors for the 2019 Ridgecrest Earthquake Sequence? Geophys Res Lett 48:1–10. https://doi.org/10.1029/2020GL090853 Trugman DT, Borsa AA, Sandwell DT (2014) Did stresses from the Cerro Prieto Geothermal Field influence the El Mayor-Cucapah rupture sequence? Geophys Res Lett 41:8767–8774. https://doi.org/10.1002/2014GL061959 Verdecchia A, Carena S (2015) One hundred and fifty years of Coulomb stress history along the California-Nevada border, USA. Tectonics 34:213–231. https://doi.org/10.1002/2014TC003746 White MCA, Fang H, Catchings RD, et al (2021) Detailed traveltime tomography and seismic catalogue around the 2019 Mw7.1 Ridgecrest, California, earthquake using dense rapid-response seismic data. Geophys J Int 227:204–227. https://doi.org/10.1093/gji/ggab224 Wiemer S (2001) A software package to analyze seismicity: ZMAP. Seismological Research Letters 72:373–382. https://doi.org/10.1785/gssrl.72.3.373 Yu C, Hauksson E, Zhan Z, et al (2019) Depth Determination of the 2010 El Mayor-Cucapah Earthquake Sequence (M ≥ 4.0). J Geophys Res Solid Earth 124:6801–6814. https://doi.org/10.1029/2018JB016982 Zhang H, Wang W, Chai L (2022) The correlation of earthquake swarms and local velocity heterogeneities in the Brawley seismic zone, southern California. Physics of the Earth and Planetary Interiors 322:. https://doi.org/10.1016/j.pepi.2021.106814 Ziv A, Rubin AM (2000) Static stress transfer and earthquake triggering: No lower threshold in sight? J Geophys Res Solid Earth 105:13631–13642. https://doi.org/10.1029/2000jb900081 Zoback MD, Barton CA, Brudy M, et al (2003) Determination of stress orientation and magnitude in deep wells. International Journal of Rock Mechanics and Mining Sciences 40:1049–1076. https://doi.org/10.1016/j.ijrmms.2003.07.001 Supplementary Files Figsupplementary.pdf TableS1.docx TableS2.xlsx TableS3.txt TableS4.txt Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 04 Mar, 2024 Editor invited by journal 03 Mar, 2024 Editor assigned by journal 27 Feb, 2024 First submitted to journal 26 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3987112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276143270,"identity":"6cc3f842-9041-40d4-8e52-409ea64286a1","order_by":0,"name":"Haritha Chandriyan","email":"","orcid":"","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":false,"prefix":"","firstName":"Haritha","middleName":"","lastName":"Chandriyan","suffix":""},{"id":276143271,"identity":"ddb073c0-720f-4a65-b6c7-45fb0e0bf1c8","order_by":1,"name":"Ramakrushna Reddy","email":"","orcid":"","institution":"National Taiwan University","correspondingAuthor":false,"prefix":"","firstName":"Ramakrushna","middleName":"","lastName":"Reddy","suffix":""},{"id":276143272,"identity":"74991853-62f2-48d4-a16c-4de1fe6c40a1","order_by":2,"name":"Paresh Nath Singha Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYPACCTkQeeABCVosjMFaEkjQUpHYAKKI0mIu3f50w882ifT5YYcfAm2xk9NtIKDFcs4Zs5u9bRK5G2+nGQC1JBubHSCgxeBGDtsNXpCW2QkgLQcStxHWkv7s5l+gwwxnp38gVkuC2W2gLQny0jlE2mI5I8fstsw5CcMN0jkFBxIMiPCLuQTQYW/K6uTlZ6dv/vChwk6OsPdBBCMbkHEAziVGC8MfBgb5BiJUj4JRMApGwcgEAFz2SI8dz3EXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6459-4037","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":true,"prefix":"","firstName":"Paresh","middleName":"Nath Singha","lastName":"Roy","suffix":""}],"badges":[],"createdAt":"2024-02-25 06:49:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3987112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3987112/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-024-06899-1","type":"published","date":"2024-09-16T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52064950,"identity":"4cb37e3b-d5e9-426b-aef3-bd5b16608870","added_by":"auto","created_at":"2024-03-06 06:32:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":786425,"visible":true,"origin":"","legend":"\u003cp\u003eThe four significant earthquakes in Southern and Baja California region are represented by red stars. Green dots indicate the seismicity of the region from 1990 to before the 2019 mainshock. Distribution of earthquakes after the 2019 mainshock is marked by yellow dots.\u003c/p\u003e\n\u003cp\u003esegments, and enhanced coulomb stress on the Garlock fault segment, followed by the occurrence of 2019 Ridgecrest, raises pertinent questions regarding the potential activation of these fault systems (Ross et al. 2019; Nanjo 2020; Ramos et al. 2020). The complex interplay of stress accumulation and fault behavior demands an integrated approach to assessing seismic hazards in the region. If Ridgecrest were to experience another significant shock, Toda and Stein (2020) described a potential scenario in which a chain reaction would be set off among the Garlock fault and southern SAF portions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/cd3f37bbab5ff0bc6b12eb76.png"},{"id":52064247,"identity":"2ccd7b9e-0d5d-4fa5-b730-361c0cc7ba64","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517086,"visible":true,"origin":"","legend":"\u003cp\u003ea) The CCS induced by earthquakes from April 14,1990 to just before the occurrence of Mw 6.1 measured at 15 km (SDR:167\u003csup\u003e0\u003c/sup\u003e/90\u003csup\u003e0\u003c/sup\u003e/-167\u003csup\u003e0\u003c/sup\u003e). The CCS induced by earthquakes from April 14,1990 to just before the occurrence of Mw 7.3 at b) 6.5 km and c) 15 km (SDR:344\u003csup\u003e0\u003c/sup\u003e/89\u003csup\u003e0\u003c/sup\u003e/170\u003csup\u003e0\u003c/sup\u003e). White star indicates M \u0026gt; 6 earthquakes. Major fault lines in the study area are demarcated by grey dashed lines. BMEWCF: Burnt Mountain East Wide Canyon fault, EPWDCF: Eureka Peak West Deception Canyon fault, PMF: Pinto Mountain Fault, BCF: Blue Cut Fault, MCFS: Mission Creek Fault Strand, 1-Johnson Valley fault, 2-Emerson fault, 3-Homestead Valley fault, 4-Camp Rock fault. Fig.1d) distribution of CCS at 6 km before the occurrence of Mw 7.1(white star) from 28\u003csup\u003eth\u003c/sup\u003e October 1998 considering strike, dip, and rake as 334\u003csup\u003e0\u003c/sup\u003e/71\u003csup\u003e0\u003c/sup\u003e/161\u003csup\u003e0\u003c/sup\u003e. Major fault lines in the study area are shown by the grey dashed line. PMF: Pinto Mountain Fault, 1, 2-Lavic Lake North West Bullion fault, 3-Calico Hidalgo fault, 4-Camp Rock Fault, and 5-Pisgah Bullion West Bullion fault.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/4c230ea46ad33d2a0a61c2d9.png"},{"id":52064250,"identity":"dbc01993-5c4f-4b39-99a5-a40605e563b5","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229613,"visible":true,"origin":"","legend":"\u003cp\u003ea) CCS induced by events at 6 km before the occurrence of Mw 5.8 calculated the considering minimum strike, dip, rake as 149\u003csup\u003e0\u003c/sup\u003e/68\u003csup\u003e0\u003c/sup\u003e/159\u003csup\u003e0\u003c/sup\u003e, b) CCS induced by events before the occurrence of Mw 7.2 at 8 km measured along, SDR:170\u003csup\u003e0\u003c/sup\u003e/78\u003csup\u003e0\u003c/sup\u003e/-166\u003csup\u003e0\u003c/sup\u003e. Major faults in the area are IF: Imperial Fault, LSF: Laguna Salada Fault, and CPF: Cerro Prieto Fault.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/b68bb03d73694823b03b6a9c.png"},{"id":52064951,"identity":"455f490d-846a-4466-9434-3b1fb61da125","added_by":"auto","created_at":"2024-03-06 06:32:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273333,"visible":true,"origin":"","legend":"\u003cp\u003eThe CCS changes induced by the earthquakes, a) before the rupture of Mw 6.4 at 11.9 km considering SDR as 317\u003csup\u003e0\u003c/sup\u003e/52\u003csup\u003e0\u003c/sup\u003e/173\u003csup\u003e0 \u003c/sup\u003erespectively, and b) before the rupture of Mw 7.1 at 2 km for the SDR, 318\u003csup\u003e0\u003c/sup\u003e/89\u003csup\u003e0\u003c/sup\u003e/-173\u003csup\u003e0\u003c/sup\u003e. Earthquakes are represented by white stars and major faults are marked by grey dash lines. GF: Garlock fault, SNF: Sierra Nevada fault, 1-Little Lake fault zone, 2-Airport Lake fault zone.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/6c14d0446455205b86c9218b.png"},{"id":52064249,"identity":"c1e37d56-c6b4-41fb-94f7-08f2cfcca965","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246365,"visible":true,"origin":"","legend":"\u003cp\u003eDepicts the result of fractal analysis. Variation of Dc from 07-07-2019 to 31-12-2022 considering 50 events/window is shown by the orange lines. Blue star indicates earthquakes, M\u0026gt; 5 occurred during the above period and grey dots represent background seismicity with minimum earthquake magnitude as 2.1.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/0be5af8f7fe41b2488f7d803.png"},{"id":52064252,"identity":"078292e1-94d9-460a-93d2-b2f92be691a2","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":314016,"visible":true,"origin":"","legend":"\u003cp\u003ea) CCS after the 2019 earthquake series to December 2022 is calculated for depth 2 km (SDR: 251\u003csup\u003e0\u003c/sup\u003e/30\u003csup\u003e0\u003c/sup\u003e/-48\u003csup\u003e0\u003c/sup\u003e). White star indicates major earthquakes occurred in the Ridgecrest region, including the 1995 Ridgecrest series as well as 2019 events. Major faults are shown by dashed lines; 1-Little Lake fault zone, 2-Airport Lake fault zone GF: Garlock Fault, SNF: Sierra Nevada fault, KCF: Kern Canyon fault, OWF: Owens Valley fault, HMF: Homestead Valley fault, b) spatial variation of Dc after 2019 Ridgecrest earthquake sequence, considering the Dc values calculated for the period July 07, 2019 to December 31, 2022. The pink dots represent earthquake (M≥ 2.5) distribution from January 01, 2023 to June 30, 2023. The green star denotes the 2019 series while the green star represents M \u0026gt;5 earthquakes that occurred during this period.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/e64164a716f591cd1e3413fc.png"},{"id":52064259,"identity":"faf986d7-8727-4ee3-91af-97e71ae84e12","added_by":"auto","created_at":"2024-03-06 06:24:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":375509,"visible":true,"origin":"","legend":"\u003cp\u003ea) Spatial variation of Dc for window no. 69-74 (May 09, 2020-July 24, 2020), earthquakes (M ≥ 3.5) from July 24, 2020 to July 31, 2021 are represented by pink dots. The green star indicates events with a magnitude greater than 4, b) Spatial variation of Dc for the last 6 windows (June 02, 2021- October,12 2022). Pink dots indicate earthquakes (M ≥ 3.5) occurred in the region from October 12, 2022 to June 30, 2023.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/6197e4f68be479138473820f.png"},{"id":52064258,"identity":"a5d90798-8631-4cad-90a8-6fbef36e06d5","added_by":"auto","created_at":"2024-03-06 06:24:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":609564,"visible":true,"origin":"","legend":"\u003cp\u003eThe Dc variation (orange solid line) followed by 2010 El-Mayor Cucapah till December 2022 for 50 events/window is demonstrated here. Blue star indicates earthquakes above magnitude 5 and grey dots represent background seismicity keeping 2.1 as the minimum magnitude\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/c69f041cbb7047eeaa7050bc.png"},{"id":52064251,"identity":"a413eee7-8653-4ea5-be92-419eea3ae054","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":474326,"visible":true,"origin":"","legend":"\u003cp\u003ea) CCS computed for earthquakes that occurred after the 2010 El-Mayor earthquake series at a depth= 12 km. We considered the FM data from 05.04.2010 to 31.12.2022. White star indicates earthquakes with M \u0026gt;5. Grey lines indicate major faults in the region; 1-Southern SAF, 2- East Shoreline fault, 3- Extra fault, 4-Elmore Ranch fault, 5-Superstition Hills fault, 6-Brawley Seismic Zone boundary, IF: Imperial fault, LSF: Laguna Salada fault, and CPF: Cerro Prieto Fault, b) Spatial variation of Dc followed by 2010 El-Mayor earthquake sequence, all the windows are considered for plotting the Dc, after the 05.04.2010 event to 31\u003csup\u003est\u003c/sup\u003e December 2022. Pink dot denotes M ≥ 3.5 earthquakes. Yellow and Green star respectively indicates 2010 earthquakes and events with M \u0026gt;4.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/d2a08a491a6eb9432b4e61b3.png"},{"id":52064260,"identity":"b504928b-2500-4899-9817-6a80afb1bea4","added_by":"auto","created_at":"2024-03-06 06:24:10","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":435409,"visible":true,"origin":"","legend":"\u003cp\u003ea) Spatial variation of Dc for the first low Dc window (w.no. 230-248, December 23, 2015- March 14, 2017). Earthquakes (M ≥ 3.5) occurred from March 14, 2017, to June 30, 2020 marked by pink dots, b) Dc map for second Low Dc window (w.no. 270-277, August 18, 2021- October 203, 2022), M≥ 3.5 earthquakes occurred during August 18, 2021 to October 03, 2022 is shown by pink dots.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/d314cadbffc514f454357b33.png"},{"id":65103842,"identity":"f4923b66-ebfa-4df3-96fa-1780a48ed78c","added_by":"auto","created_at":"2024-09-23 16:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4706372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/7a9a924c-a1d0-4ae8-87f0-4ad1bbf4ab52.pdf"},{"id":52064246,"identity":"2640f4b4-cc50-4d00-b6b7-cf9677c9c96e","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2039512,"visible":true,"origin":"","legend":"","description":"","filename":"Figsupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/d071208cdc7b4bbc99c0921c.pdf"},{"id":52064255,"identity":"ed6e842a-b2ba-40cd-b997-bec239ec0dad","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34985,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/a3c91df82209d64d41b635e6.docx"},{"id":52064253,"identity":"bd53d124-6af6-475e-b3f7-4c561354dfb2","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35024,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/558312f4aac7abbf51bfc99a.xlsx"},{"id":52064256,"identity":"4dd1e468-9fa0-4928-a969-5a2fe7f3641b","added_by":"auto","created_at":"2024-03-06 06:24:09","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":153537,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.txt","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/a6bad5ef8b1955b2182cc4bf.txt"},{"id":52064257,"identity":"7064a171-4b40-437b-bb45-88a542edb685","added_by":"auto","created_at":"2024-03-06 06:24:10","extension":"txt","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":473774,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.txt","url":"https://assets-eu.researchsquare.com/files/rs-3987112/v1/78baa7c3323fbb8ad94f79f4.txt"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDecoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious seismic activity in the Eastern California Shear Zone (ECSZ) and adjacent regions has drawn considerable scientific interest. The occurrence of three strong (M\u003csub\u003ew\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;7) earthquakes since 1990 (Fig.\u0026nbsp;1) has highlighted the ECSZ as an area of anomalous seismicity, prompting further investigation. The potential for future M\u003csub\u003ew\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;7 earthquakes in the southern and Baja California region, particularly near the Garlock fault and southern segment of the San Andreas Fault (SAF), necessitates a comprehensive understanding of the stress dynamics within the study region (Toda and Stein \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The prolonged quiescence (500 years) of this faults\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e The four significant earthquakes in Southern and Baja California region are represented by red stars. Green dots indicate the seismicity of the region from 1990 to before the 2019 mainshock. Distribution of earthquakes after the 2019 mainshock is marked by yellow dots.\u003c/p\u003e \u003cp\u003esegments, and enhanced coulomb stress on the Garlock fault segment, followed by the occurrence of 2019 Ridgecrest, raises pertinent questions regarding the potential activation of these fault systems (Ross et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nanjo \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The complex interplay of stress accumulation and fault behavior demands an integrated approach to assessing seismic hazards in the region. If Ridgecrest were to experience another significant shock, Toda and Stein (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) described a potential scenario in which a chain reaction would be set off among the Garlock fault and southern SAF portions.\u003c/p\u003e \u003cp\u003eUnderstanding the orientation and magnitude of stresses within the crust holds critical importance (Zoback et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Moulas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Delorey et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Milliner et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as it profoundly influences the location, size, and spatial extent of earthquake ruptures. Earthquakes exhibit a fractal distribution pattern (Kagan and Knopoff \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Kagan \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Matsuzaki \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Lei and Kusunose \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). A notable phenomenon is the relative decrease of the fractal correlation dimension (Dc) towards low values during earthquake clustering across both space and time. Researchers have put forward that regions with higher stress levels tend to have lower fractal dimension values (Main \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sammonds et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Maus and Dimri \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sunmonu and Dimri \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Telesca et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Thingbaijam et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mondal et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, lower Dc values can be interpreted as indicators of an impending earthquake (Hirata et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Murase \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Roy et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Previous studies suggest intense clustering accompanied low Dc before the rupture of significant earthquakes, for instance, Mw 7.7 and Mw 7.6 Turkey-Syria earthquakes (Chandriyan and Roy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Mw 6.4, 2020 Puerto Rico (Mangalagiri et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Mw 7.8 New Zealand (Mondal et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Mw 9 Indian Ocean earthquake, M\u003csub\u003es\u003c/sub\u003e 7.8 Izmit, M\u003csub\u003es\u003c/sub\u003e 8.5 Denali (Roy and Nath \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) etc.\u003c/p\u003e \u003cp\u003eWe make use of these strong earthquakes with the perspective of determining how the low Dc values and static stress changes are interconnected. In most cases, monitoring occurrences of smaller earthquakes is essential because they often accompany larger earthquakes in preparation. The studies suggest that seismicity patterns can occasionally correlate with minor stress changes (Nalbant et al.; Harris et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Deng and Sykes \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). To validate the low Dc induced by fractal clustering of earthquakes, we use the Coulomb method for static stress calculation. It should be noted that we have not considered the influence of any of the past big earthquakes. While examining the static stress changes, earthquakes with high magnitudes (M\u0026thinsp;\u0026gt;\u0026thinsp;5) have generally received more attention. However, we will focus on events of lower magnitude (M\u0026thinsp;\u0026ge;\u0026thinsp;2.5) to observe even the smallest variations reflecting in the stress from smaller to intermediate earthquakes during the interseismic period. Here, we attempt to understand how these events can be useful in tracking the triggered zones along with the spatiotemporal variation in Dc.\u003c/p\u003e \u003cp\u003eSeveral investigations have been conducted to assess Coulomb stress changes by examining the impact of past earthquakes in the study region (King et al. 1994b; Deng and Sykes \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Parsons and Dreger \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fialko et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kilb et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Felzer et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Smith and Sandwell \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Meng and Peng \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Verdecchia and Carena \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mancini et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pope and Mooney \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Im et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, our study diverges significantly from this approach. The core premise of our research lies in identifying the specific influence of static stress originating from earthquake clusters that precede the mainshocks. Our focus is exclusively directed toward earthquakes that are associated with a decrease in the fractal correlation dimension (Dc). Based on the observed correlation between Dc and Coulomb stress from our retrospective study, we attempt to assess distinctive patterns of seismic patterns after the 2019 Ridgecrest sequence and the 2010 El-Mayor Cucapah earthquake.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cp\u003eNewly available focal mechanism catalogue derived through deep learning method (Cheng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), was sourced from Southern California Earthquake Data Center (SCEDC), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scedc.caltech.edu/data/alt-2022-cheng.html\u003c/span\u003e\u003cspan address=\"https://scedc.caltech.edu/data/alt-2022-cheng.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This catalogue spans earthquake with high-resolution parameters spanning from January 11, 1981, to November 6, 2021. For events beyond this timeframe, the focal mechanism data was obtained from the SCEDC website. The updated catalog by Cheng et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and parameters obtained from the GCMT catalogue for Mw\u0026thinsp;\u0026gt;\u0026thinsp;7 are listed in the supplementary file (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The input data for fractal analysis was obtained from SCEDC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://service.scedc.caltech.edu/eq-catalogs/date_mag_loc.php\u003c/span\u003e\u003cspan address=\"https://service.scedc.caltech.edu/eq-catalogs/date_mag_loc.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.. The initial stages of data processing and analysis were performed using ZMAP (Wiemer \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.seismo.ethz.ch/en/research-and-teaching/productssoftware/software/ZMAP/\u003c/span\u003e\u003cspan address=\"http://www.seismo.ethz.ch/en/research-and-teaching/productssoftware/software/ZMAP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequent fractal analysis was conducted with FractalAnalyzer (Roy and Gupta \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sourceforge.net/projects/fractalanalyzer/\u003c/span\u003e\u003cspan address=\"https://sourceforge.net/projects/fractalanalyzer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. It is a Matlab-based tool, facilitates the examination of spatial and temporal variations within the earthquake clusters. Preliminary analysis of the Dc carried out across the southern California (SoC) and Baja California (BaC) regions has revealed a consistent decline in Dc prior to earthquake, Mw 7.3, 1992 Landers earthquake, Mw 7.1, 1999 Hectormine, Mw 7.2, 2010 El-Mayor Cucapah earthquake, and Mw 7.1, 2019 Ridgecrest earthquake (Chandriyan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on our preliminary investigations, we pinpointed the approximate period when Dc exhibited a sharp drop. In Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, the previous Dc values are for reference.\u003c/p\u003e \u003cp\u003eThe Coulomb failure criterion states that rupture along a plane occurs when shear stress (τ\u003csub\u003es\u003c/sub\u003e) exceeds the restraining force of friction that exists between fault blocks (King et al. 1994). This restraining force derives from the difference between the coefficient of friction (\u0026micro;\u003csub\u003es\u003c/sub\u003e) and pore fluid pressure (p) within the rock, multiplied by the normal stress (τ\u003csub\u003en\u003c/sub\u003e). By disregarding the pore pressure, the change in coulomb stress can be expressed as ΔCS\u0026thinsp;=\u0026thinsp;Δτ\u003csub\u003es\u003c/sub\u003e - \u0026micro;\u003csub\u003es\u003c/sub\u003e Δ τ\u003csub\u003en\u003c/sub\u003e. Key parameters for this analysis include Poisson\u0026rsquo;s ratio set as 0.25 and \u0026micro;\u003csub\u003es\u003c/sub\u003e as 0.4 (Stein et al.,1994), and a shear modulus of 0.8 MPa.Coulomb 3.3 software is used for the CCS calculation and can be obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usgs.gov/node/279387\u003c/span\u003e\u003cspan address=\"https://www.usgs.gov/node/279387\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Toda et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For each significant earthquake, we chose the receiver fault orientation along the maximum magnitude earthquake fault plane from the dataset under consideration. In subsequent sections, the notations \u0026ldquo;analysis F\u0026rdquo; and \u0026ldquo;analysis M\u0026rdquo; represent CCS estimations before the foreshock and mainshock, respectively. Considering that the maximum depth of seismic activity identified for the study area is approximately 15 km (Hauksson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), our CCS calculations were limited till the depth mentioned above for all the upcoming analyses.\u003c/p\u003e \u003cp\u003eThe assessment of Dc variation after the 2019 Ridgecrest and 2010 El-Mayor Cucapah has been done using the modified Grassberger and Procaccia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)method. The correlation integral provides the cumulative correlation function (C(r)) which depends on earthquake pairs (N) of seismicity and cluster, epicentral distance (r\u003csub\u003eij\u003c/sub\u003e), and length scale (r) between them.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{C }\\left(\\text{r}\\right)\\text{=}\\frac{\\text{2}}{\\text{N(N-1)}}\\text{ }\\sum _{\\text{j=1}}^{\\text{N}}\\sum _{\\text{i=j+1}}^{\\text{N}}\\text{H(}{\\text{r-r}}_{\\text{ij}}\\text{)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe FractalAnalyzer can compute multifractal dimension parameter Dq (q\u0026thinsp;=\u0026thinsp;0 to 22), which is defined as\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{D}}_{\\text{q}}\\text{=}\\frac{\\text{log}{\\text{C}}_{\\text{q}}\\text{(r)}}{\\text{log}\\text{r (r\u0026rarr;0)}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere Cq (r) is the correlation integral of q\u003csup\u003eth\u003c/sup\u003e order fractal\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\text{C}}_{\\text{q}}\\left(\\text{r}\\right)\\text{= }{\\left\\{\\frac{\\text{1}}{\\text{N}}\\text{ }\\sum _{\\text{j=1}}^{\\text{N}}{\\left[\\frac{\\text{1}}{\\text{N}}\\text{ }\\sum _{\\text{i=j}}^{\\text{N}}\\text{H}\\left(\\text{r-}\\text{}{\\text{X}}_{\\text{i}}\\text{-}{\\text{X}}_{\\text{j}}\\right)\\right]}^{\\text{q-1}}\\right\\}}^{\\frac{\\text{1}}{\\text{q-1}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eH is the Heaviside step function. Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) is the modified version of Grassberger and Procaccia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), and FractalAnalyzer was developed on this basis. When q\u0026thinsp;=\u0026thinsp;2, it corresponds to the fractal correlation dimension (Dc). The previous study adopted a consistent choice of 50 events per window for all analyses to identify discernible Dc patterns in the southern and Baja California regions (Chandriyan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, we have taken 50 events/windows for fractal analysis of the post-2019 Ridgecrest series (July 07, 2019, to December 31, 2022) and the post-2010 El-Mayor Cucapah earthquake (April 05, 2010, to December 31, 2022). The computation of Dc for the input fractal data involves evaluating the correlation among the initial 50 earthquakes using Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This procedure has been iteratively applied to the subsequent windows. The minimum magnitude completeness (Mc), obtained through the Gutenberg-Richter relationship applied to the declustered catalogue (Reasenberg \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), yields a value of Mc 2.1 for both the Ridgecrest and Baja California regions (Figs \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea and c). Detailed information regarding the input data utilized for fractal analyses for both the regions are provided in the supplementary section (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e and S4).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe Mw 7.3 Landers mainshock, which ruptured in ECSZ on June 28, 1992, was preceded by the Mw 6.1 Joshua Tree earthquake on April 23, 1992. Analysis F indicates that the epicentral region of M\u003csub\u003ew\u003c/sub\u003e 6.1 was surrounded by positive values of coulomb stress continuously from depths 5 to 10 km and 14\u0026ndash;15 km. Analysis M demonstrates a continuous and elevated distribution of positive static stress extending through the depths, from 0 to 15 km, particularly around the epicentral area of the impending mainshock. The CCS estimated at 6.5 km and 15 km depths is illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and c, respectively. Both these analyses suggest the distribution of positive static stress in the vicinity of future shocks. Moreover, the positive stressed region is more spatially distributed at 6.5 km (Cheng et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) than the GCMT depth. In addition, a background seismicity analysis was carried out before Mw 6.1 and Mw 7.3, indicating a highly stressed region close to the future earthquake (Fig.\u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInvestigation of CCS induced by events from the onset of low Dc to before the 1999 Hectormine earthquake demonstrates positive stress patches at 6 km and 7 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), close to the Hectormine hypocentral region. The results are similar when the stress is calculated along the rupture plane of Mw 7.1 and along the maximum earthquake (M4.94) rupture plane. Similar to the Landers and its foreshock, a correlation between Dc and high stress can be seen for this mainshock. According to the deep-learning data and GCMT, centroid depths for this earthquake are 1.72 km and 15 km, respectively. We did multiple analyses changing the calculation depth by incrementing 1 km in each step; however, only at the depths mentioned above, i.e., at 6 km and 7 km, could the trace of positive stress be observed. Background seismicity analysis (01.01.1994-16.10.1999, 3.5\u0026thinsp;\u0026le;\u0026thinsp;M\u0026thinsp;\u0026le;\u0026thinsp;4.97) computed along maximum magnitude earthquake rupture shows stress shadow at 0\u0026ndash;5 km and 8\u0026ndash;15 km and stress positive region at 6 and 7 km, shown in Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe third significant earthquake to strike the California region was the Mw 7.2 El-Mayor Cucapah earthquake in 2010, which was preceded by a foreshock of Mw 5.8 in December 2009. In analysis F, our observations reveal positively stressed regions extending from the surface to 6 km depth, the CCS distribution at 6 km is shown in Fig.\u0026nbsp;3a. For analysis M, we detected a region of positive stress near the hypocentral location of the Mw 7.2 event. For this mainshock, CCS induced by events since the onset of low Dc also contributes to enhanced stress. The CCS measurements exhibit a more widespread distribution of stress at 8 km (Fig.\u0026nbsp;3b) compared to the centroid depth of 12.8 km (as per GCMT), where a stress shadow is evident (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Background seismicity since 2005, assessed along the fault plane of the Mw 5.8 foreshock, reveals elevated stress levels at depths of 13.5 km and 14.7 km. Upon analysing the same dataset, considering the receiver fault orientation along the fault plane of the maximum earthquake magnitude, trigger zones of approximately 0.2 bar magnitude are observed at the mainshock location. The computed background seismicity prior to the mainshock rupture implies an increased stress region at the hypocenter of the mainshock (at 8 km), as depicted in the supplementary Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3a\u003c/b\u003e) CCS induced by events at 6 km before the occurrence of Mw 5.8 calculated the considering minimum strike, dip, rake as 149\u003csup\u003e0\u003c/sup\u003e/68\u003csup\u003e0\u003c/sup\u003e/159\u003csup\u003e0\u003c/sup\u003e, b) CCS induced by events before the occurrence of Mw 7.2 at 8 km measured along, SDR:170\u003csup\u003e0\u003c/sup\u003e/78\u003csup\u003e0\u003c/sup\u003e/-166\u003csup\u003e0\u003c/sup\u003e. Major faults in the area are IF: Imperial Fault, LSF: Laguna Salada Fault, and CPF: Cerro Prieto Fault.\u003c/p\u003e \u003cp\u003e The 2019 Mw 7.1 Ridgecrest was preceded by Mw 6.4 foreshock approximately two days prior to the mainshock (Ross et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lei et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Analysis F reveals localized positive stress in the Mw 6.4 hypocentral region since the onset of low Dc. Two analyses have been done keeping receiver fault orientation along the maximum earthquake plane and the future foreshock plane; both results show the existence of trigger zones at 12 km. The presence of trigger zones at 12 km (Fig.\u0026nbsp;4a) is evident for this event. This distinct patch of positive stress region implies that monitoring small earthquake activity can provide insights into future trigger points. In analysis M, significant stress is observed around the hypocenter of Mw 7.1 at a depth of 2 km (Fig.\u0026nbsp;4b), and a stress shadow is observed at 12 km (Fig. S6), considering both the receiver fault orientation. The enhanced stress following the low Dc accentuates the correlation between these parameters. An inspection of background seismicity has been done before the foreshock as well as the mainshock, reveals enhanced stress regions around the hypocenter area (Figs S7a and b).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e The CCS changes induced by the earthquakes, a) before the rupture of Mw 6.4 at 11.9 km considering SDR as 317\u003csup\u003e0\u003c/sup\u003e/52\u003csup\u003e0\u003c/sup\u003e/173\u003csup\u003e0\u003c/sup\u003e respectively, and b) before the rupture of Mw 7.1 at 2 km for the SDR, 318\u003csup\u003e0\u003c/sup\u003e/89\u003csup\u003e0\u003c/sup\u003e/-173\u003csup\u003e0\u003c/sup\u003e. Earthquakes are represented by white stars and major faults are marked by grey dash lines. GF: Garlock fault, SNF: Sierra Nevada fault, 1-Little Lake fault zone, 2-Airport Lake fault zone.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Insights into future seismicity\u003c/h2\u003e \u003cp\u003eIn the Ridgecrest region, we examined 4493 earthquakes, out of it 178 clusters that are identified through the Reasenberg algorithm with 95 percent confidence. The analysis of depth-wise seismicity distribution during this interval reveals a notable concentration of earthquake occurrences up to 10 km, with only a few events crossing beyond this depth (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb) This is in accordance with the findings of Hauksson et al. (2012), who reported a sudden decline in earthquake occurrences below 10 km. We analysed the spatial variation of Dc with respect to time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e), a substantial Dc fluctuation was observed between July 07, 2019, and September 24, 2019, with the values varying within the range of 0.4 and 1. After September 24, 2019, a rise in Dc indicates a lesser rate of earthquake occurrence, suggesting subsiding aftershock activity. The peak Dc value (1.02) was recorded on May 24, 2020, followed by a gradual decline that led to its lowest point of 0.10. Within this transitional phase, two earthquakes (M\u0026thinsp;\u0026gt;\u0026thinsp;5) occurred on June 04, 2020, and June 24, 2020. The seismicity associated with the events and Dc variation is provided in the supplementary file (Fig. S9). The lowest Dc (0.09\u0026ndash;0.1) was observed from July 06, 2020, to July 28, 2020, a year after the rupture of the 2019 sequence. In general, a consistent fluctuation in Dc has been observed from April 21, 2021, to December 31, 2022, accompanied by less clustering of earthquakes. For the same period, we measured the CCS, indicating a positively stressed region NW of the 2019 Ridgecrest epicenter. A continuous assessment of CCS of events that occurred from July 07, 2019, to December 31, 2022, was carried out at various depths, employing precise 1 km intervals, and extended up to a depth of 15 km. Results suggest that additional stress has been introduced along the Garlock fault after the Ridgecrest earthquakes, mainly below 10 km (Fig.S8). Also, positive stress is observed within the Coso geothermal area, the Sierra Nevada fault, and a segment of the Homestead Valley fault at shallow depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe mapped the spatial variation of Dc up to December 31, 2022, to investigate whether the low Dc zones fall in high-stress regions. The prominent stressed region near latitude 36.5\u003csup\u003e0\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) is characterized by a low Dc patch (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The spatial distribution of high static stress patches is highly correlated that of the low Dc zones, suggesting a good relationship among these parameters. To assess whether the earthquakes occurring after this timeframe aligns with low Dc zones, we plotted events with M\u0026thinsp;\u0026ge;\u0026thinsp;2.5 within the period spanning from December 31, 2022, to June 30, 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Fig. 6b illustrates that the highest frequency of earthquake occurrences is predominantly concentrated within regions characterized by low Dc values. Moreover, we identified the windows which are causing low Dc and then mapped those windows individually. In this context, we chose two distinct windows to calculate CCS and map the spatial variations in Dc. In the first window, the spatial fluctuations of Dc from May 09, 2020, to July 24, 2020, are depicted (Fig.\u0026nbsp;7a). The seismic events occurred after July 24, 2020, until June 30, 2023, is superimposed over the Dc map demonstrates that earthquakes of M\u0026thinsp;\u0026ge;\u0026thinsp;2.5 or greater fall predominantly in areas characterized by low to medium Dc values. Notably, Earthquakes with magnitudes M\u0026thinsp;\u0026ge;\u0026thinsp;4 are consistently observed to occur within regions characterized by low Dc values. In the second window, we considered the Dc for the last six windows (83\u0026ndash;89), the period ranges from December 24, 2021, to December 31, 2022. The spatiotemporal variation is correlated with seismic events that have occurred from December 31, 2022 June 30, 2023. A high percentage of the earthquakes (M\u0026thinsp;\u0026ge;\u0026thinsp;2.5) occurred under this region fall in low to medium Dc regions (Fig.\u0026nbsp;7b). Our findings reveal the presence of a low Dc patch across different segments of the Garlock fault. Intriguingly, high Dc values are discerned along the southeastern side of the Mw 6.4 epicenter, which is the central segment of the Garlock fault.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;7a\u003c/b\u003e) Spatial variation of Dc for window no. 69\u0026ndash;74 (May 09, 2020-July 24, 2020), earthquakes (M\u0026thinsp;\u0026ge;\u0026thinsp;3.5) from July 24, 2020 to July 31, 2021 are represented by pink dots. The green star indicates events with a magnitude greater than 4, b) Spatial variation of Dc for the last 6 windows (June 02, 2021- October,12 2022). Pink dots indicate earthquakes (M\u0026thinsp;\u0026ge;\u0026thinsp;3.5) occurred in the region from October 12, 2022 to June 30, 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, clear fluctuations in Dc values are observable both before and after each seismic event. Most fluctuating Dc values are seen after the 2010 earthquake which lasted till March 14, 2011. The minimum Dc value (Dc\u0026thinsp;=\u0026thinsp;0) was observed on February 1st, 2017. Reduced variability in Dc values was noted during two distinct periods: from August 1st, 2013, to March 10th, 2016, and from January 29th, 2018, to June 12th, 2020. Notably, no significant earthquakes occurred within these specified time intervals. The clusters observed within the latitudinal range of 32.6\u003csup\u003e0\u003c/sup\u003e to 33\u003csup\u003e0\u003c/sup\u003e (Fig. S10b) may represent a potential zone for a future seismic event, seismicity density has been seen as high with an associating Dc drop. The calculated CCS for earthquakes following the 2010 mainshock reveals a positive stress pattern within the range of 33\u003csup\u003e0\u003c/sup\u003e to 33.5\u003csup\u003e0\u003c/sup\u003e latitude and \u0026minus;\u0026thinsp;115\u003csup\u003e0\u003c/sup\u003e to -116\u003csup\u003e0\u003c/sup\u003e longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis indicates a higher concentration of trigger zones at a depth of 12 km while considering a depth range of 0 to 30 km. The spatial distribution of Dc values, superimposed with seismic events occurring after December 2022 until June 30, 2023, suggests an elevated stress region in the NW to the El-Mayor earthquake rupture location (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). A notable resemblance has been observed between the distribution of positive stress and the area with low Dc values, as we observed in the case of Ridgecrest region. Spatial variation of Dc near latitude, 32.8\u003csup\u003e0\u003c/sup\u003e/33.2\u003csup\u003e0\u003c/sup\u003e and longitude, -115.4\u003csup\u003e0\u003c/sup\u003e and \u0026minus;\u0026thinsp;115.8\u003csup\u003e0\u003c/sup\u003e shows a large low Dc value patch with higher number of events (M\u0026thinsp;\u0026gt;\u0026thinsp;4 and 5). Following the 2010 earthquake, two periods indicated rapid changes in Dc from low to high values, which were analyzed separately. In the first part, we mapped the Dc variation (December 23, 2015- January 01, 2017) and plotted the earthquakes that followed this period till June 06, 2020. Nearly 70 percent of earthquake fall in the low to moderate Dc regions (Fig.\u0026nbsp;10a). A similar trend was observed for the second part with 75 percent of events falling in the low Dc regions (Fig.\u0026nbsp;10b). To map Dc, we considered the events during 30 April 2020 to 18 August 2021 then an earthquake of M\u0026thinsp;\u0026gt;\u0026thinsp;2.5 followed by this day to 30 June 2023 were plotted. Individual low Dc window analysis in the Baja California region implies that the chance of occurrence of earthquakes is higher in low to moderate Dc region than in high Dc. In general, a higher likelihood of earthquake occurrences in areas characterized by low to moderate Dc values compared to regions with higher Dc values is observed in BaC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;10a\u003c/b\u003e) Spatial variation of Dc for the first low Dc window (w.no. 230\u0026ndash;248, December 23, 2015- March 14, 2017). Earthquakes (M\u0026thinsp;\u0026ge;\u0026thinsp;3.5) occurred from March 14, 2017, to June 30, 2020 marked by pink dots, b) Dc map for second Low Dc window (w.no. 270\u0026ndash;277, August 18, 2021- October 203, 2022), M\u0026thinsp;\u0026ge;\u0026thinsp;3.5 earthquakes occurred during August 18, 2021 to October 03, 2022 is shown by pink dots.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn the case of the 1992 Landers mainshock and foreshock, high stress was observed near the hypocenter region when considering the events occurring during the low Dc period. According to Masterlark and Wang (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), the identified hypocenter depths for Mw 7.3 and Mw 6.1 are 8 km and 11\u0026ndash;14 km, respectively. (Bennett et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) reports the emergence of a cluster around the Mw 7.3 center at a distance of about 7 km. Furthermore, Hauksson et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) propose that the mainshock rupture occurs between 3\u0026ndash;6 km. Cohee and Beroza (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) state that the hypocenters of fault segments linked to the Lander\u0026rsquo;s ruptures, such as the Homestead Valley and Camp Rock faults, are located up to 15 km. The continuous distribution of high stress till 15 km estimated based on low Dc values could represent the early stressed state of the multi faults involved in the Landers rupture. The larger spatial extent of positive stress at 6.5 km compared to 15 km suggest the distribution of more rupture sources at former depth, which is derived through deep-learning techniques. The substantial disparity between the centroid depth value given by Cheng et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the values provided by the Global Centroid Moment Tensor (GCMT) highlights the remarkable precision of the dataset generated through machine learning techniques. This also highlights the significance of incorporating novel methodologies in earthquake research.\u003c/p\u003e \u003cp\u003eThe high stress observed at depths of 6 and 7 km prior to Hectormine probably indicate the impending event. Parson and Dreger (2000) suggest hypocentral depth of 6 km, whileOglesby et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) proposed a nucleation depth of 7.5 km. Different hypocentral values are reported in the literature (Ji et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pollitz and Sacks \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Therefore, the high stress observed at 6 and 7 km from our CCS analysis can be considered as the preparatory phase of the Hectormine event. According to Hauksson et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), 6 km also can also be correlated with the depth of aftershocks indicating pre-existing weak zones. Our investigation indicates the presence of stress levels of approximately 0.2 bar at the hypocentral region (considering earthquakes since 1998). This finding aligns with the observations made by Masterlark and Wang (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), who reported comparable stress values for earthquakes that occurred, followed by the 1992 Landers rupture. However, our study could delineate the trigger zone using the earthquake data year before the rupture using Dc drop period. A similar correlation of Dc and high stress is seen for the 2010 foreshock and mainshock. This sequence is regarded as one of the best examples of poorly estimated hypocentral depth (Yu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), cause of multiple values provided by researchers (Hauksson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sarychikhina et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fletcher et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Using depth phase modeling Yu et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) put forward the focal depth as 8 km (peak depth\u0026thinsp;=\u0026thinsp;6 km) had stated more earthquake activity between 3\u0026ndash;10 km. While we calculated the CCS before the rupture of Mw 7.2, the maximum stress is seen at 8 km. Therefore, the trigger zones observed before Mw 5.8 and Mw 7.2 rupture can correlate with the Dc variations.\u003c/p\u003e \u003cp\u003eOur study highlights that the Ridgecrest region has experienced an accumulation of stress owing to seismic events taking place in the adjacent areas of its epicenter (Figs.\u0026nbsp;4a and b). The possibility of triggering the Ridgecrest event by Hectormine was ruled out due to a study by Pope and Mooney (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the findings by Tong et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) confirms the above statement which put forward that rupture of this mainshock is primarily due to the local stress field. A distinct sign of a confined stress zone is observable before the Mw 6.4 earthquake rupture at 11.9 km and 12.7 km. The hypocenter of the Mw 7.1 earthquake exhibits a stress level of approximately 0.2 bar before the rupture of the Mw 6.4 foreshock, implying that the region was already under a state of increased stress (Figs.\u0026nbsp;4a and b). Interestingly, the trend of positive linear stretch seen along Mw 7.1 has correlated with its ruptured fault line, NE-SW trending fault ruptures are reported by researchers (Pollitz et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shelly \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, we propose this as an early indication of the Mw 7.1. Many focal depths have been suggested for 2019 earthquakes; relatively a shallow depth (1\u0026ndash;8 km) is observed for Mw 7.1 and a depth range for Mw 6.4 is given as 8\u0026ndash;12 km (Ross et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lomax \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Plesch et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pollitz et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tong et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Jin and Fialko \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported peak moment release was observed at 3\u0026ndash;4 km, the recent focal mechanism catalogue provides\u0026thinsp;~\u0026thinsp;2 km as centroid depth. Therefore, we did a CCS analysis considering the shallow depth in the case of the mainshock and the relatively deeper depth for the foreshock. Barnhart et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) estimated stress of approximately 0.01\u0026ndash;0.02 bar at Mw 7.1 hypocenter a few minutes before its rupture. Here, we observed nearly 0.2 bar before Mw 6.4 and Mw 7.1. A study by Jin and Fialko (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) points out the failure of dynamic stress to culminate rupture of Mw 7.1, though its value was higher than static stress. This suggests the importance of monitoring CCS of earthquakes occurring in the low Dc windows. Considering the observations, we can infer that the assessment of CCS variation in southern and Baja California implicates the possibility of a strong correlation between low Dc and increasing crustal stress before the occurrence of strong earthquakes. Therefore, this study underscores the potential of integrating these two parameters for hazard assessments in seismically active regions.\u003c/p\u003e \u003cp\u003eWe can further explain this earthquake behaviour through Self-organized criticality (SOC), which comprehends how incremental stress contributes to significant stress bursts. This theory proposes that dynamic systems can spontaneously reach a critical state, where a minor event can trigger a chain reaction, potentially resulting in a catastrophic outcome. The small magnitude events of interseismic periods can contribute to accelerating the crust to attain the state of SOC and later disrupt it. In the study region, we observed fractal clustering preceding the strong events resulting in low Dc (Chandriyan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), resulting from crustal organization towards the rupture. Moreover, this study identifies the positive stress close to each earthquake\u0026rsquo;s hypocenter. Using the Grassberger and Procaccia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) algorithm, Murase (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) investigated pre-seismic earthquake clustering and the associated variations in Dc preceding the 2003 MJ 8 Tokachi-oki earthquake, indicating a shift to smaller Dc values five years before the mainshock. While studying the fractal characteristics of 2020 Mw 6.4, Puerto Rico earthquake, we observed a decline in Dc values and high stress in the vicinity of epicenter(Mangalagiri et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar correlations were observed between the 2023 Kahramanmaras twin ruptures and the 2009 Fiordland earthquakes (Mondal et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chandriyan and Roy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thereby, it indicates the possibility of using Dc as a stress indicator and a numeric precursory signal.\u003c/p\u003e \u003cp\u003eThe highly fluctuating Dc followed by the strong events implies the results of stress liberation and stress increase along certain segments across different fault (Chandriyan and Roy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), manifested through aftershocks. In the case of 2010 and 2019 earthquakes, intense aftershock activity has been reported (Hauksson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kroll \u003cem\u003eet al\u003c/em\u003e. 2013; Ross \u003cem\u003eet al\u003c/em\u003e. 2017; Liu \u003cem\u003eet al\u003c/em\u003e. 2019; Shelly \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shcherbakov 2021). Although, relatively low Dc precedes the major earthquakes (e.g., Mondal et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Roy and Nath \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Roy and Padhi 2007; Chandriyan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chandriyan and Roy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), intermediate events also display similar characteristics. One such example is, two M\u0026thinsp;\u0026gt;\u0026thinsp;5 earthquakes that ruptured in 2020 in Ridgecrest region during the transition from high to low Dc phase. In the Ridgecrest region, the spatial variation of Dc over three years shows localized small Dc zones trending NW direction that is similar to the distribution of CCS. This finding strongly indicates a significant correlation between stress patterns and Dc changes. A similar result is observed for post-2010 analysis of seismicity. The spatial variation of Dc mapped post-occurrence of the 2010 and 2019 earthquakes as well as the individual windows, indicate that the low-medium value of Dc regions contains a higher percentage of earthquake occurrence. As we measure Dc based on the relative clustering of earthquakes, the region that accumulates strain is displayed by a high frequency of events, therefore showing low Dc values. Therefore, the high Dc regions contain a lesser number of earthquakes. By monitoring the response of Dc over time and focusing on CCS computations for specific clusters, we can obtain valuable insights into potential future rupture zone locations. The region characterized by a large extent of low Dc values to the NW of the 2010 earthquake and the 2019 Ridgecrest earthquake could potentially be linked to geothermal fluid-related activities of the Coso and Salton Sea geothermal field. The Coso region is renowned for its high seismic activity, making it one of the most active areas in Southern California (Hauksson et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).Feng and Lees (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) identified NW stretch of microearthquakes constituting high seismicity fracture zones across the Coso region. In addition to induced seismicity(Brodsky and Lajoie \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Llenos and Michael \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the Salton Sea geothermal field witness\u0026rsquo;s multiple earthquake swarms, Hauksson et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reported.Trugman et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) explain the contributions to seismicity from anthropogenic causes as well as swarms in the Coso and Salton Sea geothermal regions, highlighting that swarms play the main role as contributors. As a result, the parameter changes over these regions cannot be considered an anomalous seismicity pattern.\u003c/p\u003e \u003cp\u003eA continuous stress readjustment occurs in the Ridgecrest and adjoining regions as evidenced by the lowest Dc corresponding to the two June 2020 earthquakes. Interestingly, the lack of positive CCS and high Dc observed south of the 2019 Ridgecrest suggests the possibility of a barrier south of its hypocenter. This can be attributed to the presence of a low-velocity zone at the Garlock fault in the southeast, effectively facilitating the halt of seismic wave propagation (White et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Garlock fault, a left-slip fault in southern California, has shown low seismic activity and aseismic creep in its western segment, a few small earthquakes, and no creep in its eastern segment (Astiz, 1983). This fault has remained inactive for a considerable period (Madugo et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hatem and Dolan \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The fault\u0026rsquo;s earthquake recurrence is irregular, with the most recent surface-rupturing event occurring between A.D. 1450 and 1640 (Dawson et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Immediate to the 2019 events, scientists reported enhanced stress distribution along this dormant fault (Barnhart et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lozos and Harris 2020; Nanjo \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The 2019 Ridgecrest earthquake sequence did not cause the Garlock fault to fail, but it did undergo postseismic creep and an earthquake swarm (Ramos et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As per our analysis, we have observed a distinct region of localized positive stress along the western segment of Garlock fault preceding the occurrence of Mw 6.4. The low Dc areas along the western as well as the eastern (with respect to Mw 7.1 epicenter) part of the Garlock fault could be an indication of high crustal stress accumulating along these segments. Barnhart et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggests that Garlock fault is sensitive to static stress changes. Moreover, Nanjo (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) spotted a low b value patch along this fault segment, is also an indication of high stress. Our study implicates the possibility of future events along the western and eastern segments as low Dc were distributed across these segments.Astiz and Allen (1983) proposed that the likelihood of the eastern segment of the Garlock fault rupturing is higher than the western segment\u0026rsquo;s if it behaves similarly to the San Andreas fault. In a recent study by (Andrew et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the structural complexity of the eastern segment of the Garlock fault were highlighted, implying the need for reconsidering the present seismic hazard assessment models incorporating multi-stranded fault. Intriguingly, our research indicates that the centre segment of the Garlock fault is seismically calm, as reflected by high Dc, indicating the absence of major stress changes. The finding is consistent with Wang and Zhan (2020) observations, which identified inactivity across this region, followed by the 2019 earthquake sequence. Based on these observations, there is little chance that a major earthquake will rupture the segment.\u003c/p\u003e \u003cp\u003eBaja California encounters a higher frequency of earthquakes compared to the Ridgecrest, an important segment of the North America-Pacific plate boundary (Dokka and Travis \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hauksson et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Jin and Fialko \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thompson Jobe et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). An interesting pattern we noticed was a periodic decrease in Dc values following Mw 7.2; Chandriyan et al. (2024) reported a similar pattern for the Mw 7.7, 06 February 2023, Nurdağı-Pazarcık earthquake. This mainshock occurred at the Eastern Anatolian Fault Zone, where the Arabian and Anatolian plates slide past each other by left-lateral movement. In the case of 2023 Turkey- Syria doublet, the pattern was halted soon after the rupture of 2020 Mw 6.7 event. If the Baja California region do not undergo any major stress change, we can possibly anticipate further drop in Dc toward the end of 2024. By saying that, we do not know the exact cause of this episodic behaviour of seismicity in Baja California. Another similarity observed is that Mw 7.1 and 7.6 did not rupture along the main faults; therefore, the unanticipated rupture region further put forward the structural heterogeneities and complex earthquake dynamics of these events.\u003c/p\u003e \u003cp\u003eThe current stress state in Baja California implies the existence of positive CCS mainly along southern SAF, Brawley seismic zone boundary, Cerro Prieto fault, and NW of 2010 epicenters.(Fialko \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) highlighted the prolonged dormancy of the SSAF lasting for around 257 years. He cautioned about the potential end of the interseismic period, emphasizing the critical significance of continuously monitoring the rate at which strain accumulates. The increase in crustal stress near this fault line has been reported in the literature (Freed and Lin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Brawley seismic zone boundary binds the southern SAF to the Cerro Prieto fault, and its earthquake activity depends mainly on the movement of the former (Hauksson et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This region witnesses small to intermediate magnitude earthquakes, and the recent earthquake swarm M5.25, 05 June 2021, occurred in this seismic zone, suggesting the need for a detailed seismic study in this area. In addition, our study detected spatially clustered events (latitude: 32.8\u003csup\u003e0\u003c/sup\u003e and 33\u003csup\u003e0\u003c/sup\u003e), which could indicate a potential future earthquake rupture zone. Interestingly, the distribution of a more positive CCS region found at a depth of 12 km suggests enhanced crustal stress changes at this depth. A recent study by(Zhang et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified high-velocity crust from 5 to 12 km, marking the termination of the seismogenic zone at 12 km. Therefore, the high number of trigger zones we observed could be due to these high-velocity rocks.\u003c/p\u003e \u003cp\u003eBoth the southern SAF and the Garlock fault pose significant threats to society, as they are both capable of generating high-magnitude (M\u0026thinsp;\u0026gt;\u0026thinsp;7.5) earthquakes (Olsen et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Our study indicates the presence of stress accumulation coupled with low Dc values along these fault segments. As of today, an accurate quantification of threshold triggering stress cannot be estimated. It has been stated that a Coulomb stress change as small as 0.1 bar can influence the aftershock locations (Reasenberg \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; King et al. 1994a; Hardebeck et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Anderson and Johnson \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, it is unclear whether a static stress change of less than 0.1 bar can cause or delay an earthquake (Harris \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to (Freed \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), critical stressed faults can be ruptured with minimal stress increase. Furthermore, Ziv and Rubin (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) concluded that central California has no minimum triggering stress. As a result, we cannot ignore the localized stress change ranging from 0.1 to 0.2 bar during the interseismic phase. Therefore, through implementing the Dc changes, we can identify highly stressed crustal zones, which can be instrumental in forecasting potential seismic hazards and thereby mitigating the extent of their impact.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eFractal clustering pattern of earthquakes that occurred during the interseismic period can be of great significance in terms of forecasting purposes. In this study, we focussed on clusters of earthquakes that occurred during the decreasing phase of Dc. Based on the earlier study, the numerical precursors for 1992 Landers, 1999 Hectormine, 2010 El-Mayor Cucapah, and 2019 Ridgecrest were found. Here, the CCS examined prior to both foreshocks and mainshocks revealed the existence of positive stress near the hypocentral regions. The conspicuous observation is the increased CCS surrounding hypocentral regions subsequent to the reduction in Dc values. As a result, our study confirms the relationship between Dc values and static stress across for four major earthquakes in the Southern and Baja California regions. Since their rupture, we further explored the seismicity patterns in regions surrounding the Mw 7.2, 2010 and Mw 7.1, 2019 earthquakes. A distinct pattern has emerged in the fractal analysis of post-2010 and 2019 earthquakes. A near-constant trend of Dc is identified in the Ridgecrest region, suggesting a downtrend in seismicity. In the case of Baja California, an oscillatory nature of Dc is observed around a four-year interval period. We found that upcoming events tend to concentrate within areas characterized by relatively low to medium Dc values. The Garlock Fault, recognized for its aseismic creep activity in the aftermath of the 2019 Ridgecrest earthquake, displays signs of elevated stress in multiple sections. Additionally, the spatial distribution of Dc implies the presence of regions with lower Dc values along this fault segment. The notable trend is the accumulation of stress in the northwestern area of Mw 7.1 and Mw 7.2 earthquakes, which is evident from the spatial distribution of CCS and Dc values, indicating geothermal energy extraction activities and earthquake swarms. The southern SAF and the Brawley seismic zone boundary demonstrate low Dc and high stress, demanding a detailed seismic hazard study. Highlighting the extended periods of quiescence observed in both the Garlock fault and the southern San Andreas Fault is of utmost importance, as these factors demand substantial attention in studies focused on evaluating seismic risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of the manuscript.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003cem\u003ehe authors have no relevant financial or non-financial interests to disclose\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHaritha Chandriyan contributed to data curation, methodology, formal data analysis, investigation, result interpretation and visualization, writing-original draft, and review, and editing of the draft. Ramakrushna Reddy was involved in validation, supervision, and manuscript review, and editing. P.N.S Roy contributed to conceptualization of the study, methodology, supervision, and manuscript review.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Indian Institute of Technology Kharagpur for providing the opportunity to conduct this research. \u0026nbsp;We express our gratitude to the creators of Coulomb, Zmap, and Fractal Analyzer softwares. Author Haritha Chandriyan thank IIT Kharagpur for providing the doctoral fellowship.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson G, Johnson H (1999) A new statistical test for static stress triggering: Application to the 1987 Superstition Hills earthquake sequence. 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Natural Hazards 77:S5\u0026ndash;S18. https://doi.org/10.1007/s11069-014-1188-2\u003c/li\u003e\n\u003cli\u003eRoy PNS, Gupta DK (2015) Fractal analyzer: A MATLAB application for multifractal seismicity analysis. Seismological Research Letters 86:1424\u0026ndash;1431. https://doi.org/10.1785/0220150013\u003c/li\u003e\n\u003cli\u003eRoy PNS, Nath SK (2007) Precursory correlation dimensions for three great earthquakes. Curr Sci 93:1522\u0026ndash;1529\u003c/li\u003e\n\u003cli\u003eSammonds PR, Meredith PG, Main IG (1992) Role of pore fluids in the generation of seismic precursors to shear fracture. Nature 359:228\u0026ndash;230. https://doi.org/10.1038/359228a0\u003c/li\u003e\n\u003cli\u003eSarychikhina O, Glowacka E, Robles B, et al (2015) Estimation of Seismic and Aseismic Deformation in Mexicali Valley, Baja California, Mexico, in the 2006\u0026ndash;2009 Period, Using Precise Leveling, DInSAR, Geotechnical Instruments Data, and Modeling. 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International Journal of Rock Mechanics and Mining Sciences 40:1049\u0026ndash;1076. https://doi.org/10.1016/j.ijrmms.2003.07.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coulomb stress, Correlation Fractal dimension, Self-organization, Earthquake forecasting, Earthquake hazards","lastPublishedDoi":"10.21203/rs.3.rs-3987112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3987112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the collaborative application of fractal clustering patterns and cumulative Coulomb stress (CCS) in the context of earthquake precursory signal identification. We evaluated CCS created by the events based on the period when the Correlation fractal dimension (Dc) commenced falling into relatively lower values. We tested this approach to four strong (M\u0026thinsp;\u0026gt;\u0026thinsp;7) earthquakes of southern and Baja California, revealing a correlation between these parameters. The crustal readjustment period prior to large earthquakes frequently exhibits a succession of events that result in positive CCS and a higher degree of spatial clustering, indicating low Dc. Preceding strong earthquakes, positive CCS values have been observed concurrently with the onset of low Dc, indicating the potential significance of Dc in seismic hazard assessment studies. We examined these parameters in the Ridgecrest and Baja California regions following the 2010 Mw 7.2 and 2019 M\u003csub\u003ew\u003c/sub\u003e 7.1 events. Signs of strain were observed in the northwestern region of the epicenters, indicated by the presence patch of low Dc and positive CCS. We observed that earthquake frequency is typically highest in regions with low to medium Dc values. Multiple sections of the Garlock Fault, manifested by low Dc regions, are loaded, posing a significant seismic risk in Southern California. Similarly, the southern segment of the San Andreas fault displays demonstrate low Dc and high stress, has been inactive for a prolonged period. While these faults may be inactive, we must not underestimate the unpredictability of earthquakes.\u003c/p\u003e","manuscriptTitle":"Decoding Self-similar Earthquake Patterns and Static Stress; a Pathway to Enhanced Earthquake Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 06:24:04","doi":"10.21203/rs.3.rs-3987112/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-04T23:59:47+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-04T05:09:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2024-03-03T22:50:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-27T05:25:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-02-26T05:56:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4b522383-28ad-41f1-bb84-f03093491d33","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T15:59:21+00:00","versionOfRecord":{"articleIdentity":"rs-3987112","link":"https://doi.org/10.1007/s11069-024-06899-1","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2024-09-16 15:57:01","publishedOnDateReadable":"September 16th, 2024"},"versionCreatedAt":"2024-03-06 06:24:04","video":"","vorDoi":"10.1007/s11069-024-06899-1","vorDoiUrl":"https://doi.org/10.1007/s11069-024-06899-1","workflowStages":[]},"version":"v1","identity":"rs-3987112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3987112","identity":"rs-3987112","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}