Kenneth R. Anderson

Seismic scattering and shallow structure of the moon in oceanus procellarum

Long, reverberating trains of seismic waves produced by impacts and moonquakes may be interpreted in terms of scattering in a surface layer overlying a non-scattering elastic medium. Model seismic experiments are used to qualitatively demonstrate the correctness of the interpretation. Three types of seismograms are found, near impact, far impact and moonquake. Only near impact and moonquake seismograms contain independent information. Details are given in the paper of the modelling of the scattering processes by the theory of diffusion.Interpretation of moonquake and artificial impact seismograms in two frequency bands from the Apollo 12 site indicates that the scattering layer is 25 km thick, with a Q of 5000. The mean distance between scatterers is approximately 5 km at 25 km depth and approximately 2 km at 14 km depth; the density of scatterers appears to be high near the surface, decreasing with depth. This may indicate that the scatterers are associated with cratering, or are cracks that anneal with depth. Most of the scattered energy is in the form of scattered surface waves.

Lunar crust - Structure and composition.

Lunar seismic data from artificial impacts recorded at three Apollo seismometers are interpreted to determine the structure of the moons interior to a depth of about 100 kilomneters. In the Fra Mauro region of Oceanus Procellarum, the moon has a layered crust 65 kilometers thick. The seismic velocities in the upper 25 kilometers are consistent with those in lunar basalts. Between 25 and 65 kilometers, the nearly constant velocity (6.8 kilometers per second) corresponds to velocities in gabbroic and anorthositic rocks. The apparent velocity is high (about 9 kilometers per second) in the lunar mantle immediately below the crust.

Velocity structure and properties of the lunar crust.

Lunar seismic data from three Apollo seismometers are interpreted to determine the structure of the Moons interior to a depth of about 100 km. The travel times and amplitudes ofP arrivals from Saturn IV B and LM impacts are interpreted in terms of a compressional velocity profile. The most outstanding feature of the model is that, in the Fra Mauro region of Oceanus Procellarum, the Moon has a 65 km thick layered crust. Other features of the model are: (i) rapid increase of velocity near the surface due to pressure effects on dry rocks, (ii) a discontinuity at a depth of about 25 km, (iii) near constant velocity (6.8 km/s) between 25 and 65 km deep, (iv) a major discontinuity at 65 km marking the base of the lunar crust, and (v) very high velocity (about 9 km/s) in the lunar mantle below the crust. Velocities in the upper layer of the crust match those of lunar basalts while those in the lower layer fall in the range of terrestrial gabbroic and anorthositic rocks.

Syntactic analysis of seismic waveforms using augmented transition network grammars

Abstract The Augmented Transition Network (ATN) formalism provides a convenient environment for developing syntactic waveform analysis techniques. As an example, an ATN grammar that describes seismic glitch noise waveforms is presented. The grammar produces English descriptions of waveforms and reduces the false alarm rate of an automatic signal detection system. ATN grammars can be extended in several ways to provide error correcting and non-left-right parsing capabilities.

A reevaluation of an efficient algorithm for determining the convex hull of a finite planar set

Graham [l] proposed an algorithm for determining the convex hull of a finite set of points in the plane which takes no more than N log2 (N)l + CN “operations”, where N is the number of points in the set. Jarvis [2] pointed out that Cin Graham’s algorithm is actually quite large and developed an algorithm which takes at most A@¶ + 1) simpler operations, where Mis the number of points on the hull. This paper presents a reevaluation of Graham’s algorithm which shows it to be much more efficient than it appears from his description. The basic conclusion is that although angles are obviously useful for describing an algorithm, sines and cosines are usually a more “natural” representation computationally.

Automatic analysis of microearthquake network data

Abstract The microearthquake data collected by the dense network of seismographs in California may contain a wealth of information about the tectonic process, structure and properties of the earths crust. However, before this data can be fully exploited for the purposes of earthquake prediction research, a relatively automatic, accurate, and uniform procedure for the analysis of seismic data is needed. A simple algorithm which identifies earthquakes and times first arrivals using data from an array of single component seismometers is being studied. The algorithm is simple enough that it could easily be implemented on a microprocessor monitoring a single station in real time. A unique aspect of the algorithm is that it not only picks arrivals but also objectively determines arrival time accuracy. Earthquakes are located by an iterative method. First, the most accurate arrivals are used to locate the event. Then arrivals which do not agree with this location are repicked using a more sensitive algorithm. This process is repeated until a stable location is obtained. For clear, unambiguous arrivals, the arrival times determined by the algorithm agree to within a few hundredths of a second of arrival times determined by an analyst. For weaker, more ambiguous arrivals, computer picks and hand picks are always within the accuracy prescribed by the algorithm. Using such an algorithm in routine processing of seismic data could require as little as 2 percent of the human effort now required by manual processing, and would provide for the first time, an accurate objective data base for earthquake prediction research. Syntactic structural analysis (a formal method of symbolic description) of seismic wave-forms is being investigated as a tool to improve the descriptive capability of the algorithm and as a formalism in which other picking algorithms can be expressed easily and compared. Using syntactic procedures to automatically provide descriptions of the first-arrival waveform is proposed as way of providing useful information about the earth that is not presently available on a routine basis.

Lunar velocity structure and compositional and thermal inferences

Seismic data from the Apollo Passive Seismic Network stations are analyzed to determine the velocity structure and to infer the composition and physical properties of the lunar interior. Data from artificial impacts (S-IVB booster and LM ascent stage) cover a distance range of 70–1100 km. Travel times and amplitudes, as well as theoretical seismograms, are used to derive a velocity model for the outer 150 km of the Moon. TheP wave velocity model confirms our earlier report of a lunar crust in the eastern part of Oceanus Procellarum.The crust is about 60 km thick and may consist of two layers in the mare regions. Possible values for theP-wave velocity in the uppermost mantle are between 7.7 km s−1 and 9.0 km s−1. The 9 km s−1 velocity cannot extend below a depth of about 100 km and must decrease below this depth. The elastic properties of the deep interior as inferred from the seismograms of natural events (meteoroid impacts and moonquakes) occurring at great distance indicate that there is an increase in attenuation and a possible decrease of velocity at depths below about 1000 km. This verifies the high temperatures calculated for the deep lunar interior by thermal history models.