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I present a high-precision earthquake relocation catalog and first-motion focal mechanisms before and during the 2019 Ridgecrest earthquake sequence in eastern California. I obtain phase arrivals, first-motion polarities, and waveform data from the Southern California Earthquake Data Center for more than 24,000 earthquakes with the magnitudes varying between −0.7 and 7.1 from 1 January to 31 July 2019. I first relocate all the earthquakes using phase arrivals through a previously developed 3D seismic-velocity model and then improve relative location accuracies using differential times from waveform cross correlation. The majority of the relocated seismicity is distributed above 12 km depth. The seismicity migration along the northwest–southeast direction can be clearly seen with an aseismic zone near the Coso volcanic field. Focal mechanisms are solved for all the relocated events based on the first-motion polarity data with dominant strike-slip fault solutions. The Mw 6.4 and 7.1 earthquakes are positioned at 12.45 and 4.16 km depths after the 3D relocation, respectively, with strike-slip focal solutions. These results can help our understanding of the 2019 Ridgecrest earthquake sequence and can be used in other seismological and geophysical studies.

With the increasing use of automotive radars for assisted and autonomous driving, future cars will typically be equipped with more and more radars, covering 360° around the car. LRR radars at 77GHz and MRR/SRR/USRR at 79GHz will create a significant amount of mutual interference, reducing the radar sensitivity or creating false alarms, both impacts being highly undesirable. We consider PMCW waveforms, especially APS and m-sequences, and analyze how their auto-and cross-correlation properties impact the radar performance and can be used to reduce the amount of received or created interference. Our approach is partly analytical and partly simulation-based. This work shows that a significant reduction of the degradations is possible by a suitable sequence selection strategy.

Anthropogenic induced seismicity has been widely reported and investigated in many regions, including the shale gas fields in the Sichuan basin, where the frequency of earthquakes has increased substantially since the commencement of fracking in late 2014. However, the details of how earthquakes are induced remain poorly understood, partly due to lack of high-resolution spatial-temporal data documenting the evolution of such seismic events. Most previous studies have been based on a diffusive earthquake catalog constructed by routine methods. Here, however, we have constructed a high resolution catalog using a machine learning detector and waveform cross-correlation. Despite limited data, this new approach has detected one-third more earthquakes and improves the magnitude completeness of the catalog, illuminating the comprehensive spatial-temporal migration of the emerging seismicity in the target area. One of the clusters clearly delineates a potential unmapped fault trace that may have led to the Mw 5.2 in September 2019, by far the largest earthquake recorded in the region. The migration of the seismicity also demonstrates a pore-pressure diffusion front, suggesting additional constraints on the inducing mechanism of the region. The patterns of the highly clustered seismicity reconcile the causal link between the emerging seismicity and the activity of hydraulic fracturing in the region, facilitating continued investigation of the mechanisms of seismic induction and their associated risks.

Bromo volcano is one of a series of active volcanoes in Indonesia, located on the Tengger caldera, Probolinggo, East Java. Bromo volcano has increased volcanic activity in the end 2015 until early 2016. The research was conducted to determine the characteristics of volcanic tremors. The methods used to determine the characteristics of volcanic tremors are spectral analysis and cross-correlation waveform analysis. Spectrogram analysis obtained frequency value 1-8 Hz for volcanic tremor of Bromo Volcano. From the results of spectral analysis volcanic tremor, it’s can be seen the value of the frequency peaks irregular means the type of spasmodic tremor. Based on the pattern of the peaks of the tremor spectral analysis using 3 stations having the same pattern and the analysis of cross-correlation waveform volcanic tremor of Bromo volcano was indicated from the same source mechanism because the correlation coefficient value is good that ranged about 0.6 – 0.8.

Waveform cross‐correlation approach was first applied to three‐dimensional very high frequency (VHF) lightning mapping. A Hilbert transform of the original waveform and a three‐step cross‐correlation parsing process were employed to compute the time differences with a small 10‐μs time window for the final time‐of‐arrival (TOA) positioning. The lightning channel continuity was significantly improved, and its fine structure, including the K process, was clearly distinguished with speeds of up to 1.1 × 107 m/s. The two overlapped K processes indicate the high location accuracy of the time‐of‐arrival, based on the waveform correlation method. The median (mean) values of the positioning precision with the proposed method were evaluated by duplicate located sources and estimated as σx = 20 m (26 m), σy = 19 m (28 m), and σz = 54 m (67 m).

The waveform cross‐correlation technique is a popular tool for estimating the differential times of seismic phases in a fast and reliable manner. Differential times are used for a variety of methods, with the double‐difference relocation method HypoDD being the most popular. In this work, we analyzed the precision and possible error of cross‐correlated differential times by conducting a simple comparison with reference manual datasets. Our study was carried out on two well‐studied mainshock–aftershock datasets from the seismically active West Bohemia region (Czechia). We observed that the magnitude difference δML between two cross‐correlated earthquakes presents a significant bias, resulting in the over‐ or underestimation of the final differential time of both P and S waves. The earthquakes of differing magnitudes exhibit unequal first pulse durations in otherwise similar waveforms. As a result, the cross‐correlated differential time, which shifts seismograms to the position of maximum cross‐correlation, is different from the differential time between phase arrivals. Our test cases revealed that the resulting deviation from the true differential time depends on the actual δML and can reach values higher than 0.025 s when δML>2. Hence, in standard differential time datasets, the error has a greater impact on the data related to strong events—mainshocks. In HypoDD applications, the error leads to mislocations of mainshocks, and at the same time, the locations of the weak events are improved. We demonstrate the mislocation potential of the error on relocated hypocenters of mainshock–aftershock sequences and earthquake swarms from West Bohemia, as well as on synthetic tests. The error cannot be avoided by changing the cross‐correlated window length or filtration. We propose a few suggestions to suppress the consequences of the magnitude difference data bias. Nonetheless, the differential times error and its effects cannot currently be completely suppressed using the mentioned methods.

It is found that the best results, in terms of the efficiency of detecting regional seismic phases from blast sources, are achieved by using the method of waveform cross correlation applied to the data from a small-aperture array of three-component sensors

The presence of man-made explosions in a seismic catalogue leads to errors in statistical analyses of seismicity. Recently, the need to monitor man-made explosions used for mining, road excavating, and other constructional applications has been become a demanding challenge for the seismologists. In this way, we gain new insight into the cross-correlation technique and conduct this approach to discriminate explosions from seismic datasets. Following this, improved P-wave arrival times are used for more precise relocation. In this study, the waveform cross-correlation technique provides a reliable means for discriminating explosions which have cross-correlation coefficients (CC) of 0.6 or greater with their own corresponding stacked waveforms. The results illustrate that approximately 80 % of seismicity of southeast of Tehran, recorded by the Iranian Seismological Center (IRSC), includes events which have cross-correlation coefficients of ≥0.6 with their corresponding stacked waveforms. Furthermore, with improved P-wave arrival time, there is a better chance to relocate explosions precisely in the region under study.

This study investigated the utility of multistation waveform cross correlation to help discern induced seismicity. Template matching was applied to all Ohio earthquakes cataloged since the arrival of nearby EarthScope TA stations in late 2010. Earthquakes that were within 5 km of fluid injection activities in regions that lacked previously documented seismicity were found to be swarmy. Moreover, the larger number of events produced by template matching for these swarmy sequences made it easier to establish more detailed temporal and spatial relationships between the seismicity and fluid injection activities, which is typically required for an earthquake to be considered induced. Study results detected three previously documented induced sequences (Youngstown, Poland Township, and Harrison County) and provided evidence that suggests two additional cases of induced seismicity (Belmont/Guernsey County and Washington County). Evidence for these cases suggested that unusual swarm-like behaviors in regions that lack previously documented seismicity can be used to help distinguish induced seismicity, complementing the traditional identification of an anthropogenic source spatially and temporally correlated with the seismicity. In support of this finding, we identified 17 additional cataloged earthquakes in regions of previously documented seismicity and away from disposal wells or hydraulic fracturing that returned very few template matches. The lack of swarminess helps to indicate that these events are most likely naturally occurring.

The identification of repeating earthquakes by waveform cross correlation (multiplet analysis) has many applications in the analysis of seismic data and is increasingly being applied to volcanic‐seismic datasets. Multiplet analysis can be used to evaluate stress changes at volcanoes, to constrain the depth of explosions, or to improve locations of small‐amplitude events (Buurman and West, 2010; Thelen et al. , 2011; Battaglia et al. , 2012; West, 2013). Repeating earthquakes often occur during tectonic earthquake sequences (Nadeau et al. , 1995; Peng and Zhao, 2009), can occur within large regional seismic data catalogs (Schaff and Richards, 2011), and have been observed as microearthquakes on creeping faults (Waldhauser and Ellsworth, 2002; Malservisi et al. , 2005) and as icequakes in glaciers (Carmichael et al. , 2012; Thelen et al. , 2013). Multiplet analysis thus has the potential to be an intrinsic part of the seismologist’s toolbox; however, many studies cite computational limitations when analyzing multiplets (Petersen, 2007; Thelen et al. , 2010, 2011; Ketner and Power, 2013). Cross correlation is computationally expensive and large datasets can rapidly become difficult to analyze. Cross‐correlation algorithms are considered Big‐ O ( N 2) problems (in which N is the total number of events) in terms of the performance time of the algorithm (Black, 2004). Seismic data catalogs of hundreds of thousands of events are not uncommon at well‐monitored volcanoes (Ketner and Power, 2013), and global seismic catalogs can number many millions of events (Addair et al. , 2014). Waveform cross correlation for the identification of repeating earthquakes has been carried out on seismic events for many years (Nadeau et al. , 1995) and initially was confined to small datasets. Cross correlation can be used to …

For much of Oklahoma, augmentation of the seismic network with new public stations in the activated areas has followed rather than preceded the spread of seismicity across the state, and consequently the network geometry is often unfavorable for resolving the underlying fault structures. With this study, we reanalyze the existing earthquake catalog with additional data from two industry‐operated networks for the period May 2013 to November 2016. These networks include 40 seismic stations and cover seismically active north‐central Oklahoma with a station spacing on the order of 25 km. Relative locations obtained from waveform cross correlation reveal a striking pattern of seismicity, illuminating many previously unmapped faults. Absolute depths are usually well constrained to within 1 km. Relative locations provide about one order of magnitude better precision for resolving the structure of seismicity clusters. Relocated epicenters tend to cluster in linear trends of less than 1 km to more than 20 km in length. In areas with stations closer than about 10 km, we can resolve fault planes by strike and dip. These are generally in agreement with surface‐wave‐derived moment‐tensor solutions.

We combine classic and state-of-the-art techniques to characterize the seismic and volcanic features in the Coso area in southern California. Seismic tomography inversions are carried out to map the variations of Vp, Vs, and Vp/Vs beneath Coso. The velocities in the top layers of our model are correlated with the surface geological features. The Indian Wells Valley, with high silica content sediment strata, shows low-velocity anomalies up to 3 km depth, whereas the major mountain ranges, such as the south Sierra Nevada and the Argus Range, show higher velocities. The resulting three-dimensional velocity model is used to improve absolute locations for all local events between January 1981 and August 2011 in our study area. We then apply similar-event cluster analysis, waveform cross correlation, and differential time relocation methods to improve relative event location accuracy. A dramatic sharpening of seismicity patterns is obtained after using these methods. We also estimate high-resolution near-source Vp/Vs ratio within each event cluster using the differential times from waveform cross correlation. The in situ Vp/Vs method confirms the trend of the velocity variations from the tomographic results. An anomalous low-velocity body with low Vp, Vs, and Vp/Vs ratios, corresponding to the ductile behavior underlying the Coso geothermal field from 6 to 12 km depth, can be explained by the existence of frozen felsic magmatic materials with the inclusion of water. The material is not likely to include pervasive partial melt due to a lack of high Vp/Vs ratios.

We observed some ‘negative repeating doublets’, with nearly opposite three-component waveforms, in the aftershock sequence of the 2008 Wenchuan earthquake. The ‘negative repeaters’ are identified by using the broadband seismic record (with frequency range from 1 to 10 Hz) of the Wenchuan station (WCH) with a near epicenter distance from 19.7 to 26.6 km. These opposite three-component waveforms are not due to the changing of polarities of the seismic station.

In the list of ‘repeating pairs’ or ‘doublets’ of earthquakes in China identified by Schaff and Richards using teleseismic waveform cross-correlation, there were 23 repeating pairs located in Liaoning Province. In this study the waveforms of these events were cross-correlated using records from Liaoning Regional Seismograph Network (LRSN), and the ‘repeating events’ in the sense of regional waveform cross-correlation were obtained. The result was compared with that of Schaff and Richards and was used for the assessment of the seismic phase picking and event location practice of LRSN. The result shows that ‘repeating events’ in the sense of teleseismic waveform cross-correlation and those in the sense of regional waveform cross-correlation have significant difference, although with some overlap. However, the overall assessment of the location accuracy and the phase pick errors of LRSN by using these two sets of ‘repeating events’, respectively, provides similar results, while ‘repeating events’ in the sense of regional waveform cross-correlation seem to be better performing in such an assessment. With the assumption that the separation between the ‘repeaters’ be less than 1 km, the uncertainty in routine earthquake location of LRSN is estimated to be below 5 km, with the average of 2 km. In the observational bulletins of LRSN the time error in phase picking is estimated to be within ±1s for 94% Pg readings and for 88% Sg readings.

Removing the crustal signature from teleseismic travel times is an im- portant procedure to reduce the trade-off between crustal and mantle velocity het- erogeneities in seismic tomography. Because reverberations of long- and short-period body-wave arrivals in the crust affect the waveforms of the direct arrivals differently, the crustal effects on travel times measured by waveform cross correlation are fre- quency dependent. With synthetic responses of selected crustal models, this short note illustrates the significance of frequency-dependent crustal corrections to finite- frequency body-wave travel-time tomography. The differences in crustal correction between long- and short-period body waves at the same station can be as large as 0.6 sec, depending on the crustal thickness, velocity contrast at the Moho, and lay- ering within the crust.

We compare traditional methods of seismic event location, based on phase pick data and analysis of events one-at-a-time, with a modern method based on cross-correlation measurements and joint analysis of numerous events. In application to four different regions representing different types of seismicity and monitored with networks of different station density, we present preliminary results indicating what fraction of seismic events may be amenable to analysis with modern methods. The latter can supply locations ten to a hundred times more precise than traditional methods. Since good locations of seismic sources are needed as the starting point for so many user communities, and potentially can be provided due to current improvements in easily-accessible computational capability, we advocate wide-scale application of modern methods in the routine production of bulletins of seismicity. This effort requires access to waveform archives from well-calibrated stations that have long operated at the same location.

A new computer search of the Apollo lunar seismic data set for identification of deep moonquakes using a combination of waveform cross-correlation and single-link cluster analysis has increased the number of positively identified deep moonquakes by more than a factor of five, from the previous 1360 to 7245. At least 88 new deep moonquake source regions (nests) have been discovered, while some source regions previously identified as separate entities are now determined to be identical, reducing the number of previously known source regions from 108 to 77. The newly identified deep moonquakes and their relationships to each other are likely to revise our understanding of their spatial and temporal distributions.

We developed a sliding-window cross-correlation (SCC) detection technique and applied the technique to continuous waveforms recorded by the Cooperative New Madrid Seismic Network stations following the 18 April 2008 Illinois earthquake. The technique detected more than 120 aftershocks down to M_L 1.0 in the 2 week time window following the mainshock, which is three times more than the number of aftershocks reported by the seismic network. Most aftershocks happened within 24 hrs of the mainshock. We then relocated all events by the double-difference relocation algorithm. Accurate P- and S-wave differential arrival times between events were obtained by waveform cross correlation. After relocation, we used the L1 norm to fit all located events by a plane to determine the mainshock fault plane. The best-fit plane has a strike of 292°±11° and dips 81°±7° to the northeast. This plane agrees well with the focal mechanism solutions of the mainshock and four largest aftershocks. By combining the aftershock locations and focal mechanism solutions, we conclude that the 18 April earthquake occurred on a nearly vertical left-lateral strike-slip fault orienting in the west-northwest–east-southeast direction. The fault coincides with the proposed left-stepping Divide accommodation zone in the La Salle deformation belt and indicates reactivation of old deformation zone by contemporary stresses in the Midcontinent.

We compute high-precision earthquake locations using southern California pick and waveform data from 1981 to 2005. Our latest results are significantly improved compared to our previous catalog by the following: (1) We locate events with respect to a new crustal P and S velocity model using three-dimensional ray tracing, (2) we examine six more years of waveform data and compute cross-correlation results for many more pairs than our last analysis, and (3) we compute locations within similar event clusters using a new method that applies a robust fitting method to obtain the best locations satisfying all the differential time constraints from the waveform cross correlation. These results build on the relocated catalogs of Hauksson and Shearer (2005) and Shearer et al. (2005) and provide additional insight regarding the fine-scale fault structure in southern California and the relationship between the San Andreas Fault (SAF) and nearby seismicity. In particular, we present results for two regions in which the seismicity near the southern SAF seems to align on dipping faults.

[1] We relocated part of the aftershock activity in Cinarcik Basin and surrounding areas that are associated with the 1999 Izmit earthquake Mw 7.4. Double difference relocation algorithm is used to relocate the aftershocks. The data set was obtained from a temporary seismic network deployed 10 days after the main shock by cooperation between Bogazici University Kandilli Observatory and Earthquake Research Institute, LGIT (Grenoble), and IPGP (Paris). For a better station coverage, additional data set was obtained from a network operated by TUBITAK Marmara Research Center. Differential travel times were calculated using both arrival time readings and waveform cross correlation method. We relocated 1145 of the aftershocks and interpreted the results with emphasis on the Yalova and Tuzla cluster located within the Marmara Sea. The results show better focused seismicity patterns for the Yalova cluster, providing clear evidence for the proposed models. We present a revised location of the 1963 Cinarcik earthquake which took place in area of Yalova cluster. Finally we propose that the activity of Tuzla cluster represents a parallel subsidiary fault of the Main Marmara Fault.

SUMMARY Theoretical analyses suggest that finite-frequency effects should be considered in tomographic inversions of seismic phase ‘arrival times’ measured with waveform cross correlation at relatively low frequencies. This has led to the development of so-called ‘banana-doughnut [sensitivity] kernels’—hereinafter BDKs. Here we address a practical question: has the use of these kernels produced tomographic images of global mantle heterogeneity that are significantly better than those based on ray theory? A simple model comparison suggests that the answer is ‘not yet’. The effect of BDKs on both the pattern and the amplitude of mantle wavespeed perturbations appears to be smaller than that of practical (and subjective) considerations (such as the level of damping, the weighting of different data sets, and the choice of data fit) and does not exceed realistic estimates of image uncertainty due to, for instance, errors in the data. By itself, the fact that the better theory has not yet resulted in significant model improvements does not imply that models based on BDKs are incorrect. Deep ‘plumes’ may very well exist, but the benificial effect of BDKs on the tomographic images (and on ‘plume’ identification in particular) has been overstated.