| Attribute/Logical Rolename |
Domain |
Datatype |
NULL |
Definition |
orid |
|
NUMERIC(15, 0) |
NO |
Origin identification. Each origin is assigned a unique positive integer which identifies it in the database. The orid is used to identify one of the many hypotheses of the actual location of the event |
| sxx |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| syy |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| szz |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| stt |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| sxy |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| sxz |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| syz |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| stx |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| sty |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| stz |
|
NUMERIC(7, 3) |
YES |
Elements of the covariance matrix for the location defined by orid. The covariance matrix is symmetric (and positive definite) so that sxy = syx, etc., (x, y, z, t) refer to latitude, longitude, depth and origin time, respectively. These attributes (together with sdobs, ndef and dattype) provide all the information necessary to construct the K-dimensional (K=2, 3, 4) confidence ellipse or ellipsoids at any confidence limit desired |
| azismall |
|
NUMERIC(5, 2) |
YES |
Azimuth of smallest principal error |
| dipsmall |
|
NUMERIC(4, 2) |
YES |
Dip of smallest principal error |
| magsmall |
|
NUMERIC(7, 5) |
YES |
Magnitude of smallest principal error |
| aziinter |
|
NUMERIC(5, 2) |
YES |
Azimuth of intermediate principal error |
| dipinter |
|
NUMERIC(4, 2) |
YES |
Dip of intermediate principal error |
| maginter |
|
NUMERIC(7, 5) |
YES |
Magnitude of intermediate principal error |
| azilarge |
|
NUMERIC(5, 2) |
YES |
Azimuth of largest principal error |
| diplarge |
|
NUMERIC(4, 2) |
YES |
Dip of largest principal error |
| maglarge |
|
NUMERIC(7, 5) |
YES |
Magnitude of largest principal error |
| lddate |
|
DATE |
YES |
Load date. Date and time that the record was created, in Oracle date datatype |