The distribution and characteristics of deep earthquakes provide important information on both the large-scale aspects of mantle convection as well as on the small-scale mechanics of apparent shear failure within descending lithospheric plates at large confining pressures. Neither of these processes is well understood, but the detailed knowledge of the spatial and temporal distribution of earthquakes, as well as the focal geometries, scalar moments, and details of the earthquake rupture process, provides constraints which can be used to differentiate between competing models of mantle circulation and theories of deep seismic rupture. With the objective of expanding and improving the database of source parameters for deep earthquakes, we evaluate the feasibility of applying the Harvard centroid moment tensor (CMT) algorithm to earlier (pre-1977) earthquakes recorded on seismographs with analog recording. Abundant records from the World Wide Standardized Seismograph Network (WWSSN) are available for events after 1962, and the data from these high-quality stations with a known standard response are easily digitized and subjected to analysis by the CMT algorithm. For earlier events, no standardized network of seismometers is available. Using a modem deep earthquake as a test case, we show that reliable single-station moment tensor solutions can be obtained from the Press-Ewing instrument at Pasadena. We apply the single-station technique to three historical earthquakes recorded on different types of electromagnetic and mechanical seismographs: the 1957 Java earthquake (Press-Ewing); the 1922 Peru earthquake (Wiechert); and the 1911 Peru-Brazil earthquake (Galitzin). Robust results can be achieved from these data provided the instruments are well calibrated, and an analysis of selected earlier deep earthquakes may quickly double or triple the time span of the database of deep earthquake source parameters.
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Physics and Astronomy (miscellaneous)
- Space and Planetary Science