Multiple Event Location Research Articles

Preliminary study of multiple event location on the Kunming Telemetry Seismic Network

Based on the first P wave arrival time data of local earthquakes recorded in the Kunming Telemetry Seismic Network in 1982–1989, the P travel time corrections of the stations in the network were obtained by use of the parameter separation method and the multiple event location method. This set of the corrections reflects the feature of lateral inhomogeneous structure of the upper crust beneath the network to certain degree. The geographic distribution of the sation corrections has obviously regional characteristics, by which the studied area is divided into three sub-areas. In the Western Yunnan area where the stations are the most dense, except the stations of Yunxian, Shidian and Wanding in south, the station corrections are not greater than 0.15 sec. In the eastern area (to the east of Chuxiong) where the network has slightly wider station interval, most of them show obviously positive delay. In southern area, including stations of Wenshan, Simao, Jinghong, Yunxian, Shidian and Wanding, all the stations have large negative delay. The results consist with the basic feature of geologic setting in Yunnan area. The accuracy of the relocated hypocentral parameters based on the corrected travel time data has fairly improved. Therefore the station corrections can be used to the routine processing of earthquake location in the Kunming Seismic Network.

Separated earthquake location

We present a practical method of locating earthquakes in laterally varying media. The procedure has two distinct phases: an estimation phase, and an application phase. In the estimation phase, all existing arrival time residuals relative to some reference, one‐dimensional velocity model for a region are used to estimate a set of path anomalies (station corrections) that vary as a function of source position. This is done by sorting earthquakes into groups localized in space and applying a novel multiple event location algorithm. This algorithm uses a pair of complementary projection operators to combine a priori information about the lateral structure that is indeterminate from the available data with the information that can be determined uniquely from the available data. Having estimated path anomalies as a function of source location, we can generate a table of path anomalies that can be accessed and interpolated for future earthquake location. This is the application phase of the method. We have tested the method on synthetic data designed to study how well earthquakes can be located in the vicinity of the rupture zone of the 1984 Morgan Hill, California, earthquake. We found precise absolute locations could be obtained, provided the a priori information was specified appropriately. Distortion of relative locations occurred near large lateral velocity gradients and for groups of events spread over large areas because of the inadequacy of fitting a single set of path anomalies to the whole group.

Progressive multiple event location (PMEL)

AbstractThis paper introduces a new method of using earthquake arrival times to simultaneously determine earthquake hypocenters and station corrections. Progressive multiple event location (PMEL) forms a solution in three major steps: (1) hypocenters are located by conventional single event methods using current station correction estimates; (2) an annulled data set (Pavlis and Booker, 1980) is formed and accumulated using Householder transformations; and (3) station correction perturbations are calculated from a pseudoinverse solution of the annulled data set. In practice, PMEL converges rapidly. We analyze the non-uniqueness of PMEL estimates in terms of resolution and covariance matrices. One of the most important features of PMEL is the computer algorithm that we describe which requires only a modest amount of computer memory that is independent of the number of earthquakes to be processed. Consequently, this algorithm can process a virtually unlimited number of earthquakes.We apply PMEL to a set of 8049 arrival times from 652 earthquakes and 4 explosions recorded by a network in the Coso Range, Inyo County, California. PMEL converges after four adjustments to the station corrections. The final rms misfit of 0.05 sec is remarkably good, and the station correction estimates correlate strongly with surface geology.

Teleseismic location techniques and their application to earthquake clusters in the South-Central Pacific

abstract Improved methods for single-and multiple-event hypocenter determinations are developed and applied to the problem of locating earthquake clusters in the South-Central Pacific Ocean. Bayesian statistical methods are used to incorporate a priori information about arrival-time variance into the derivation of hypocenter confidence ellipsoids, permitting a more realistic calculation of critical parameters in the case where the number of stations is small. The diagonal elements of certain projection operators, called “data importances” by Minster et al. (1974), are used to evaluate network balance. The hypocentroid of an event cluster is defined to be the average location of events within the cluster, and the deviations of individual hypocenters from the hypocentroid are called cluster vectors . The problem of estimating the cluster vectors can be decoupled from the problem of estimating the hypocentroid by a simple but fundamental mathematical result, here termed the hypocentroidal decomposition theorem. The algorithm based on this analysis appears to have many advantages over other published methods for multiple-event location, both in its efficient use of available information and its computational speed. The application of this method to three clusters of shallow intraplate seismicity in the South-Central Pacific, designated Regions A, B, and C, demonstrates that the seismicity within each cluster is very localized; the rms lengths of the cluster vectors for each group of epicenters are estimated to be only 9, 6, and 12 km, respectively. Estimates of the epicentroids are