GEOPHYSICAL EFFECTS STUDY (REDACTED VERSION)

• Document Type: CREST
• Collection: STARGATE
• Document Number (FOIA) /ESDN (CREST): CIA-RDP96-00788R001800300001-0
• Release Decision: RIFPUB
• Original Classification: U
• Document Page Count: 53
• Document Creation Date: November 4, 2016
• Document Release Date: June 23, 1998
• Sequence Number: 1
• Publication Date: December 1, 1984
• Content Type: REPORT

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-71
Final Report December 1984
Covering the Period 15 November 1983 to 15 December 1984
GEOPHYSICAL EFFECTS STUDY (U)
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[phone] ' Cable: SRI INTL MPK ' TWX: 910-373-2046
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LIST OF ILLUSTRATIONS . . . . . . . . . . . . . . . . . . . . . . V
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . V
I OBJECTIVE . . . . . . . . . . . . . . . . . . . . . . . . . 1
II EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . 3
III INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . 5
A. General . . . . . . . . . . . . . . . . . . . . . . . . 5
B. Report Organization . . . . . . . . . . . . . . . . . . 6
IV METHOD OF APPROACH . . . . . . . . . . . . . . . . . . . . . 7
A. Literature Search . . . . . . . . . . . . . . . . . . . 7
B. Data Acquisition . . . . . . . . . . . . . . . . . . . 7
1. ELF Measurements . . . . . . . . . . . . . . . . . 7
a. Introduction . . . . . . . . . . . . . . . . 7
b. Los Altos Site (TRI) . . . . . . . . . . . . 8
c. SRI Site . . . . . . . . . . . . . . . . . . 10
2. Satellite Downlink Geophysical Data-Acquisition
System . . . . . . . . . . . . . . . . . . . . . . 11
3. Data-Acquisition System . . . . . . . . . . . . . 13
4. Magnetic Data Tapes from NOAA . . . . . . . . . . 14
C. Data Analysis . . . . . . . . . . . . . . . . . . . . . 15
1. Integrated Data-Analysis System . . . . . . . . . 15
2. Summary of Data Analyzed . . . . . . . . . . . . . 16
3. Summary of Data Unanalyzed. . . . . . . . . . . . 17
D. Analysis Techniques and Data Preparation . . . . . . . 18
1. Techniques Used . . . . . . . . . . . . . . . . . 18
2. Description of Techniques . . . . . . . . . . . . 19
a. Epoch Analysis . . . . . . . . . . . . . . . 19
b. Time-Lag Regression . . . . . . . . . . . . . 20
3. Description of Analysis Methods . . . . . . . . . 20
a . RV Data . . . . . . . . . . . . . . . . . . . 20
b. Geophysical Data . . . . . . . . . . . . . . 21
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A. Results of Geophysical Analysis . . . . . . . . . . . . 23
1. Introduction . . . . . . . . . . . . . . . . . . . 23
2. Solar Flux . . . . . . . . . . . . . . . . . . . . 23
3. Sunspot Number . . . . . . . . . . . . . . . . . . 24
4. Solar Flares . . . . . . . . . . . . . . . . . . . 25
5. Magnetic Indices . . . . . . . . . . . . . . . . . 28
6. SIDs . . . . . . . . . . . . . . . . . . . . . . . 30
B. Results Pertaining to ELF . . . . . . . . . . . . . . . 31
1. Introduction . . . . . . . . . . . . . . . . . . . 31
2. Intercomparison of ELF System. . . . . . . . . . . 32
3. ELF/RV Comparison . . . . . . . . . . . . . . . . . 32
VI EVALUATION AND RECOMMENDATIONS . . . . . . . . . . . . . . . 35
VII SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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1 ELF Data-Acquisition System . . . . . . . . . . . . . . . . . 9
2 Real-Time Geophysical Data Acquisition via Westar IV
Downlink . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Real-Time Geophysical Data-Acquisition System. . . . . . . . 14
4 Geophysical/Performance Data-Analysis System . . . . . . . . 15
1 Geophysical Data Bases . . . . . . . . . . . . . . . . . . . 6
2 Description of RV Data . . . . . . . . . . . . . . . . . . . 21
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k The objective of this effort is to investigate the
possible effects of ambient geophysical/extremely low-frequency electro-
magnetic factors on remote viewing (RV) performance
(U) RV (remote viewing) is the acquisition and description, by mental
means, of information blocked from ordinary perception by distance or
shielding.
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IT EXECUTIVE SUMMARY (U)
?~ SRI International was tasked,
`to investigate a potential correlation between remote viewing (RV)
performance and ambient geophysical/extremely-low-frequency electromagnetic
(ELF) activity. The possibility of such correlation is indicated, for
example, by studies showing psychophysiological effects1f2* and behavioral
changes'' associated with ELF electromagnetic fields. The geophysical
variables of interest include such factors as ELF intensity/fluctuations,
ionospheric conditions, geomagnetic indices, sunspot number, and solar-
flare characteristics. The questions addressed in this program are
Do geophysical/performance correlations exist such that
measurement of the ambient geophysical variables could
be used as an indicator of expected performance?
If so, can optimum performance windows be identified?
(U) The structure of the program to investigate the above issues
consists of
A literature search
Real-time ELF measurements
-- SRI International (Menlo Park, California location)
-- Time Research Institute (Los Altos, California
field station).
Real-time geophysical data acquisition via the National
Oceanic and Atmospheric Administration (NOAA) Westar IV
satellite downlink.
Computer correlation studies of RV performance versus
variables of interest.
(U) In this report, we present findings from our over-six-year
analysis of scored RV sessions--as they relate to geophysical environmental
(U) References are listed at the end of this report.
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(U) GEOPHYSICAL DATA BASES
Solar terrestrial
- Geomagnetic--ground-measured indices Ap, sum of Kp, Cp, C9
- Solar flux (MHz): 15,400, 8,800, 4,995, 2,800, 2,695, 1,415, 606,
410, 245
- Sunspot number
- Solar flares
? Ionospheric measurements
- Sudden ionospheric disturbances (SID)
- Sudden enhancements of signal strength (SES)
? ULF/ELF
- 19 frequencies (from 1 to 30 Hz)
B. (U) Report Organization
(U) The remainder of this report is organized to include: Method of
Approach (Section IV), Results (V), Evaluation and Recommendations (VI),
and Summary (VII). The Method of Approach section contains descriptions
of the project tasks, which include Time Research Institute's data acqui-
sition systems, other sources of geophysical data acquisition, lists of
geophysical data that have been analyzed, and the analysis technique
employed. The Results section contains the findings from the comparisons
of both the ELF data sets among themselves, and the comparisons of RV
performance data with the ELF and other geophysical data. The Evaluation
and Recommendations section summarizes the findings and possible applica-
tions of our research, and identifies areas where further investigation is
needed. The Summary section summarizes the overall effort and its impli-
cations with regard to RV performance enhancement and countermeasures
development.
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III INTRODUCTION (U)
A. (U) General
(U) In order to accomplish the goals listed in the Executive Summary,
this program was designed to be a joint effort between SRI International
and Time Research Institute (TRI) of Los Altos, California, with SRI as the
prime contractor. Time Research Institute is a research organization that
specializes in temporal analysis of geophysical variables and their potential
correlation with phenomena of interest, such as weather patterns, earth-
quakes, and the like.
(U) Time Research Institute was responsible for establishing the
appropriate hardware and software systems for collecting and analyzing data
on environmental conditions and their correlation with RV performance. The
purpose of the correlation study was to determine whether RV performance
is enhanced or degraded by measurable changes occurring in the geophysical
(including solar-terrestrial) environments. The specific geophysical data
bases examined in this effort are given in Table 1.
(U) Should correlations between geophysical variables and RV per-
formance be rigorously established over time, the application potential
of the effort is twofold:
? Time periods in which enhanced RV performance might be
expected could be identified, resulting in increased
quality and accuracy of information obtained through
such channels; similarly, time periods in which degraded
RV performance might be expected could be avoided.
Thus, optimum performance windows would be identified.
An increased understanding of the types of environmental
changes that correlate with RV performance could provide
clues as to the mechanisms involved in RV functioning.
Such knowledge would lead to a more focussed research
on factors that could enhance RV performance, and would
also provide information critical to the development of
defensive countermeasures against RV.
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(U) A literature search into the areas of known effects of static
and low-frequency magnetic and electric fields on biological processes
was carried out. Much of the literature available in the ELF range dealt
with the effects (or lack thereof) of 60-Hz fields. Papers were sought
that described both the gross effects of these fields and the mechanisms
by which they could affect biological organisms. Some reports describing
higher electromagnetic frequencies (e.g., microwave) were also included
for their inferential value. (A bibliography appears as an appendix to
this report.)
B. (U) Data Acquisition
1. (U) ELF Measurements
a. (U) Introduction
(U) Although the ELF range (3 to 300 Hz) has been studied
in some detail, many unknowns remain. Although it is known that extremely-
low frequencies generated by geophysical means (e.g., electrical-storm
activity) tend to distribute themselves globally, little information is
available on the variation of the ELF environment from location to loca-
tion. Therefore, local variations may exist that are caused by both man-
made sources, and by the geological structure of the area. In the
San Francisco Bay Area, man-made sources that generate ELF on a local
scale include motors, telephone lines, power lines, and electrical subways
[Bay Area Rapid Transit (BART)]; it needed to be determined whether the
emission from such sources constitutes a significant contribution to the
omnipresent global ELF field.
(U) In order to address the above issue, two ELF monitoring
stations were set up--one at SRI Menlo Park (the RV laboratory), the other
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CIA
(U)
at the TRI field station, 17 km distant. It was anticipated that the SRI
environment might be an electrically "noisy" one due to the large amount
of electrical and electronics activity in the area--a hypothesis that was
verified. With the requirement that two ELF monitoring sites be implemented
for the program, it was decided that the two systems would be made identical.
In this way, differences between the systems would be minimized, thus
reducing the opportunity for artifactual differences between the outputs.
b. (U) Los Altos Site (TRI)
(U) Since May 1982, TRI has been operating a prototype ELF
monitoring site in Los Altos, where data have been collected twice daily
for the purpose of correlating ELF disturbances against various phenomena
of interest. In this period, analysis techniques were developed that were
directly applicable to the present task.
(U) One of the first tasks was the upgrading of the Los
Altos ELF monitoring site to provide coverage during power interrupts.
Details of this effort can be found in an interim report prepared by SRI
International.'
(U) The second task was the development of an upgraded high-
data-rate ELF system (in duplicate) to be installed at the TRI and the SRI
sites. Figure 1 is a block diagram of the basic upgraded ELF data-rate
ELF system (in duplicate) to be installed at the TRI and the SRI sites.
Figure 1 is a block diagram of the basic upgraded ELF data-acquisition
system. The ELF signal is collected by an antenna, amplified, and then
digitized by an analog-to-digital (A/D) converter so that the signal can
be input into a computer for the purpose of analysis.
(U) The antenna is a "bioantenna" (a Live Oak tree). This
procedure was based on one recommended by the Radioscience Laboratory at
Stanford University. The detected signal is the voltage measured across
a pair of electrodes implanted vertically approximately six feet apart
along the lower tree trunk. A full description of the method is given in
Reference 6, a reprint of which is included in the interim report mentioned
above.
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'ID
ELF
INPUT
DATA
A-TO-D
CONVERTER
COMPUTER:
IBM PC JR.
HALF-
HOURLY
DATA
MAKES SIGNAL READABLE BY
COMPUTER
DETERMINES INTENSITY OF SIGNAL
FREQUENCY COMPONENTS
FLOPPY DISKETTES TRANSPORTED
TO IBM XT COMPUTER
(U) The system is configured around an IBM PC jr. micro-
processor, which is not only cost-effective, but is compatible with an
IBM XT computer where much of the ELF analysis is done. Data transfer
and reduction is simple; floppy diskettes are transferable from one computer
to the other.
(U) Software has been developed for the IBM PC jr. that
reads input data from the A/D converter, performs a fast-Fourier transform
(FFT), then outputs seven data files of 19 frequencies each to a floppy .
diskette. The system operates on universal time (UT), writing records of
the means and standard deviations of 19 frequencies in the 1-to-30-Hz
range--each half hour, half-UT day, UT day, and half Pacific-time day.
This system is far superior in speed and accuracy to the prototype system.
In the upgraded system, approximately 318 ELF samples are recorded each
half hour. By comparison, the prototype ELF system sampled and processed
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the ELF environment only 20 times in an equivalent period, for a total
of about 420 samples in an entire day.
(U) The overall system calibration is as follows. A com-
bined amplification of the signal is 1000X; the signal is amplified lOX
in a preamplifier located at the antenna, and 10OX in a main amplifier.
This presents a maximum 5-V peak-to-peak signal at the A/D converter.
The A/D converter operates on an input voltage in the range of 0 to 5.12 V.
The output digital value is in the range of 0 to 256. Thus, each count
on the digital output represents 20 mV at the input. The FFT algorithm
converts the digital sample inputs into coefficients that are proportional
to this input. A value of 100 counts at the frequency 1.6 Hz, for example,
would be interpreted as indicating that the 1.6-Hz component of the measured
signal has a voltage amplitude of 2 V at input to the A/D converter. The
input signal having been amplified 1000X, this represents a 2-mV component
at the antenna input.
(U) Further details concerning measurement and calibration,
including special requirements in amplifier design, isolation circuit
diagrams, and so forth, are available in the TRI subcontractor final
report to SRI International.'
(U) The first new ELF data acquisition began five months
after the start of the TRI subcontract with SRI International. TRI was
able to initiate the first generation of the upgraded system in only five
months in spite of delays in ordering specialized components for the new
systems, and delays in the fabrication of the circuitry of the systems
themselves. Further delays were experienced in ordering additional
specialized components and in fabrication of a needed second-generation
preamplifier. Final data acquisition was begun seven months after initia-
tion of the subcontract.
(U) The SRI ELF system was implemented after extensive
testing of the upgraded system at Los Altos. A second-generation
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(U)
preamplifier/amplifier was installed in August 1984. Some differences were
immediately seen between the SRI and the TRI Los Altos stations. The dc
output of the oak tree that was selected to be the ELF antenna at SRI was
twice the level of the oak at Los Altos. It is a larger tree, and its dc
potential with equivalent electrode spacing (300 mV) was twice that of the
Los Altos site. This caused the amplified dc measurement component to
exceed the limits of the A/D converter. Hence, no dc measurements are
presently being made at the SRI site.
(U) As expected, the SRI location was found to be in an
electrically-noisier area than the Los Altos station. The 60-Hz signal
from power lines (and the like) at SRI was strong enough to approach the
limits of the A/D converter when the amplification was adequate for ELF
signal detection. The system software has been designed to omit data that
exceed the A/D converter limits, then record the fact. To date, the
system has operated successfully without losing data because limits were
exceeded. There was a concern, however, that some large-amplitude ELF
anomalies could cause the limits of the amplifier and the A/D converter
to be exceeded, in which case data would be lost. A third-generation
preamplifier has been designed, which contains the attributes of previous
preamplifiers, but, a 60-Hz filter has been specially designed and added
to the circuitry. This enables greater amplification of the ELF components
of the signal without risk of exceeding the input limits to the A/D con-
verter. The third-generation preamplifier is presently being fabricated
and will be used in follow-on work.
2. (U) Satellite Downlink Geophysical Data-Acquisition System
(U) A near-real time satellite downlink system for solar-
terrestrial data has recently become available from the National Oceanic
and Atmospheric Administration (NOAA). With this unit, it is possible
to provide immediate feedback and/or analysis in conjunction with RV
sessions. (Normally, there are long delays in procuring solar-terrestrial
data; without the downlink, delays of 10 days to 6 months are standard.)
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By means of software developed at TRI,* the downlink system provides for
accumulation of a detailed data base directly on computer diskettes.
(U) A satellite controller and a disk antenna for the downlink
system were ordered and installed at the Los Altos site early in the
project. The downlink system is configured around an IBM PC jr. micro-
processor, as shown in Figure 2. Data transfer is accomplished by means
BURROUGHS 6925
COMPUTER
FIGURE 2 (U) REAL-TIME GEOPHYSICAL DATA
ACQUISTION VIA WESTAR IV
DOWNLINK
(U) The format of the NOAA downlink is oriented toward text transmission,
and is not well suited to data-.base acquisition. Software for recording
the data is not provided by NOAA.
CONTROLLER
[BM PC JR.
DATA
DISKETTE
IBM XT
COMPUTER
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DATA
DISKETTE
DATA
DISKETTE
DATA
DISKETTE
FIGURE 3 (U) REAL-TIME GEOPHYSICAL DATA-ACQUISITION SYSTEM
(U)
Burroughs 6925 computer for use in that analysis requiring high-speed or
large memory capacity.
4. (U) Magnetic Data Tapes from NOAA
(U) In addition to the above data collection from ELF and down-
link systems, archived solar-geophysical data of interest were selected.
Two criteria in this selection were used: first, the theoretical likelihood
the data might correlate with RV performance, and second, its availability--
.based on appearance in the regularly published NOAA bulletin "Solar-
Geophysical Data Prompt Reports." The data were ordered from the National
Environmental Satellite Data and Information Service at the National Geo-
physical Data Center in Boulder, Colorado. Of 12 data sets requested,
only 7 could be supplied; several of the data sets, although published
in the "Solar-Geophysical Data Prompt Reports," were not available on
magnetic tape.
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similar to that of the ELF system; that is, by transport of floppy diskettes
from the downlink computer to the larger IBM XT analysis computer.
(U) The geophysical data downlink began data acquisition in the
third month of the TRI subcontract. The initial data were in the form
of a direct recording, which was received from the satellite, on to com-
puter diskettes. The intent was to acquire as long a data base as possible
by acquiring raw data at the same time we were developing the software to
reduce it. The kernel around which the software was written was a BASIC
program from NOAA. The original NOAA software supplied the text data
(received from the downlink) in the form of tabular summaries on a monitor.
This software did not have the capability to store or record data to any
medium. TRI made extensive modifications so that the numerical data could
be stored onto computer diskettes for inclusion into a data base. The
software is capable of averaging and storing about 20 geophysical variables
at multiples of five-min intervals (e.g., 10- or 25-min averages), which
were defined at the time the program was run. It writes six separate data
files to the diskette.
(U) Initially, the program was set to average and write the data
at five-min intervals. After a few months of operation, it was found
that the disk drive of the PC jr. tended to fail with such frequent operation.
The time-averaging span was changed to half-hourly intervals to save wear
on the disk drive. This is the same time increment used for the ELF data.
The two systems now operate in synchronization.
3. (U) Data-Acquisition System
(U) The three systems described above (the Los Altos and the
SRI ELF stations, and the Geophysical Data downlink), operate in concert,
forming the Geophysical Data-Acquisition System. Figure 3 shows the
system components and their relationship to one another. Three IBM PC jrs.
operate 24 hours a day collecting ELF and downlink solar-terrestrial data.
Data from these microcomputers are processed in the IBM XT to form con-
tinuous data bases. Copies of these data bases are sent to the
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C. (U) Data Analysis
1. (U) Integrated Data-Analysis System
(U) Statistical analyses are performed on the data bases
described above, preferably on the IBM XT for cost effectiveness. Figure
4 shows the Geophysical Data/Performance Analysis System. Data from all
direct geophysical sources are input into the IBM XT, where they are
preprocessed into continuous data bases stored on floppy diskettes. Some
data extractions are also performed at this time. The data bases and the
extracted data are read into the Burroughs 6925 computer. The data tapes
from NOAA are also read directly into the Burroughs computer. The NOAA
data files are long (16,000 records per year is common), and require the
high speed of the mainframe computer (and our existing software residing
there) to correct, process, and extract the geophysical data in usable
form. NOAA data were further processed to extract subsets of data of
PERFORM-
ANCE
DATA
ELF
DATA
STATISTICAL
ANALYSIS
DATA BASE
MANAGEMENT SYSTEM
ANALYSIS
REQUIRING LARGE MAINFRAME
(e.g., MULTIVARIATE ANALYSIS)
EPOCH
ANALYSIS
GRAPHS/
STATISTICS
GEO-
PHYSICAL
DATA
GRAPHS/
STATISTICS
STATISTICS
AND
GRAPHS
MANUAL
ANALYSIS
FIGURE 4 (U) GEOPHYSICAL/PERFORMANCE DATA-ANALYSIS SYSTEM
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interest, and to summarize the daily number of occurrences of event-type
data, e.g., solar flares (see analysis section below for details). These
extracted and summarized data were down-loaded from the Burroughs by modem
connection to the IBM XT, where statistical processing was performed.
2. (U) Summary of Data Analyzed
(U) Data were of two types: RV performance scores and geo-
physical. A detailed. description of the analysis techniques appears in
Section IV-D. In brief, the RV data were analyzed in five separate groups
according to their generation in separate experimental series. These five
groups were divided into highest.- and lowest-score categories, and epoch
analysis was performed on the eight subdivisions against each of the
following geophysical variables:
? Geomagnetic indices:
A
P
Sum of K
P
C
p
C9
? Solar flux at the following frequencies:
15,400 MHz
8,800
4,995
2,800
2,695
1,415
606
410
International Relative Sunspot numbers (Ri) or Zurich Sunspot
numbers prior to 1981 (Rz).
Sudden Ionospheric Disturbances partitioned by:
- Sudden enhancement or decrease of LF atmospherics
at approximately 27 kHz; further partitioned by
the intensity of the disturbance.
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? Solar Flares partitioned as follows:
-
All reported flares
--
Area of flares
--
l0-deg segments of longitude
-
Flares larger than Sub-Brilliant (SB)
-- Area of flares
-- Brilliance of flares
-- 10-deg segments of longitude
? ELF Data:
- 19 frequencies from 1 to 30 Hz.
3. (U) Summary of Data Unanalyzed
(U) Certain geophysical data were not analyzed for the following
? Magnetic Intensities at Satellite Altitudes, Solar Protons--
Although the Downlink system is operational, and the soft-
ware functions well, there is a difficulty that is inherent
to the Downlink system. The transmission error rate is
exceedingly high; erroneous characters are transmitted
frequently in the data, and would require hand editing--a
time-consuming and labor-intensive task. Obtaining these
data elsewhere was discussed with individuals at the SELDADS
system, who were the data source. It was found that
summaries of these data do not exist, and that the data are
archived in a 3-s interval on magnetic tape. One month's
data require an entire tape, at a cost of $120 per tape.
We would require about 20 of these tapes for a complete
analysis of the RV data. Obtaining data by this means was
impractical from the data-processing and tape-expense
standpoints.
? Solar Wind--Only fragmentary time spans of these data were
available from NOAA. It was explained that the antennas
used to receive data from Pioneer XII and its predecessor
satellites were redirected to receive signals from the
Martian satellites.
? Radio Propagation Quality Indices--These data were not
available on magnetic tape from NOAA.
? Stanford Mean Magnetic Field--These data were not available
on magnetic tape from NOAA, however, partial analysis was
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(U)
performed on data entered by hand at TR1. About half of
the data were analyzed before software difficulties
prevented completion.
Auroral Electrojet, Equatorial Dst Values--Auroral electro-
jet data were not available for the entire time period.
Existing data for Dst values were sent with an explanation
the
Japanese were now in charge of acquisition of
this
set,
but had not submitted data since 1981. Dst
values
in
December of 1983. Three of the RV data sets
could
been
analyzed, but format problems prevented it.
Most
of the NOAA data received on the tapes were in formats
suitable for direct publication, with titles, headings,
monthly averages, and comments occurring after each month's
daily data. In some cases, the day or month was given
only in the test heading. This format was unsuitable for
computer data analysis, and extensive software had to be
written to reorganize the data into a usable format.
Although such software was written to extract data from
many of the NOAA data sets, this one was bypassed because
the data were incomplete.
that
data
ended
have
? Cosmic Ray Indices--These data were sent by NOAA in a
format that would have required extensive software
development to extract daily values. Although daily
values are published in the "Solar-Geophysical Data Prompt
Reports," they are evidently not available on magnetic
tape.
D. (U) Analysis Techniques and Data Preparation
1. (U) Techniques Used
(U) The analysis was performed on two computers: an IBM XT and
a Burroughs 6925. All analyses that did not require the large memory and
high speed of a mainframe were performed on the IBM XT, at a substantial
cost savings. In particular, an epoch analysis program was translated
from ALGOL to C for use on the XT. The C version for the XT has enhanced
capabilities not available on the ALGOL version.
(U) Two statistical techniques were used in screening the data
(1) epoch analysis, and (2) time-lag linear regression and correlation
coefficients. The epoch analysis was performed primarily on the IBM XT
machine; the time-lag regressions were done on the Burroughs. While the
time-lag regressions allowed definitive statistical statements to be made,
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execution of this program was generally very costly (up to $150 per run),
so such regressions were done sparingly.
2. (U) Description of Techniques
a. (U) Epoch Analysis
(U) The primary statistical program used to scan the data for
possible relationships is called "epoch analysis." The purpose of epoch
analysis is to detect time-lag relationships in noncontinuous, or nonscalar
data sets of interest, such as "high-scoring viewing days." It will also
show areas of nonexact time-lag relationships, where correlations will
not (without further data manipulation such as running averages, and so
forth). An overview of time periods that may relate to the data of
interest is given in a quick and cost-effective way. Epoch analysis has
been used here to indicate which time intervals will be appropriate to
run time-lag regressions--a technique that is costly and frequently
inaccurate if the timed relationships are approximate rather than exact.
(U) The epoch analysis technique compares events with
other timed data sets (such as sunspot number) at intervals preceding and
following the event. The program reads two files simultaneously. The
first file is an event file, the second a data file. The program first
reads an event, then scans the data temporally backward and forward in
time around the event. This information is stored, a second event is
read, and so forth. When all the events and surrounding data have been
read, a printout is created that lists appropriate cross-correlation
statistics between the event and data elements.
(U) As an example, the program calculates the average values
of a variable, such as sunspot number, for discrete time intervals before
or after a set of RV events of interest--say, high-scoring days. For all
days, the technique determines the sunspot number one day prior to high-
performance RV sessions (the events), the day of, the day after, and so
forth. It will then calculate the average value for each day (e.g., -1,
0, +1), then tabulate and graph the results.
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(U) Time-Lag Regression
(U) This is a standard regression analysis, which offsets
the time base of the data in specified intervals. This program may be
instructed to perform sets of regressions of many time lags at a time,
even with data that are not continuous. Time lags are frequently encountered
when analyzing solar-terrestrial data with respect to terrestrial events.
For instance, approximately two to four days may elapse before magnetic
storms resulting from solar flares are detected on earth. Assuming such
mechanisms may also operate with respect to RV data, all geophysical
correlations were carried out for a time-lag range of at least plus or
minus five days.
3. (U) Description of Analysis Methods
(U) In order to determine whether RV performance correlates
with geophysical activity, it is necessary to have access to RV data bases
that have been quantified. Data bases that meet this criterion, both
archival and those generated during the subcontract period, were made
available to TRI for analysis.
(U) Four sets of RV data were analyzed initially; they
constitute the primary data base.x Each data set is referred to by the
year in which most of the data. were gathered. Table 2 shows the name,
number of samples, and begin- and end-date for each data set. The RV
data were processed for the epoch analysis by selecting the dates of the
highest and lowest scores or ratings for each data set.*
(U) A fifth RV data base was examined near the end of the program,
generally confirming the conclusions reached on the basis of the primary
RV data base.
4
(U) Selection was done by finding high and low scores
range contained approximately one-third or less of the
In most data sets, more than one session occurred on a
frequently with different individuals. In some cases,
had extremely high or low scores on a given day (there
in which the value
total data set.
given day--
two individuals
are comments later
about individual variability). These data were left intact, e.g.,
two individuals both scored high or low on the same day, those data
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Name
Sample
Number
Begin
Date
End
Date
Comments
79
36
05/14/79
08/03/79
6 x 6 orientation
80
97
01/18/80
12/14/81
RVer I.S., Class B
81
48
07/30/81
10/21/81
Targeting
84 (II)
231
01/12/84
08/17/84
RV Training (II)
84 (I)
103
03/19/84
07/10/84
RV Training (1)
b. (U) Geophysical Data
? (U) ELF Data--ELF data sets were prepared by concatenating
the data from several diskettes into a single, long-
term file for both the upgraded Los Altos and the SRI
data. This was done for daily, half-daily, and half-
hourly data.
? (U) NOAA Data--These data required extensive preprocessing
and specialized software to be written. For example,
two of the data sets, Sudden Ionospheric Disturbances
(SID) and Solar Flares, contained multiple reports of
the same event observed from different stations.
Indices of location and magnitude of the event fre-
quently do not agree from station to station. Software
was written to eliminate duplicate reports by extracting
the single largest report for each event. Extractions
of single events were done when appropriate. Data files
were constructed that contained the number or value of
the events of interest for each calendar day, from
1 January 1978 until the end of the available data--
usually 31 August 1984. The dates were checked for
order and continuity. In event-oriented data files
such as SID and Flares, events did not occur every day.
entered twice in the high or low data sets. If one individual scored
extremely high, and another extremely low (on the same day), that day
was entered in both the high and low data sets. The exception to this
was the data set of RVer I.S., Class B sessions. Several sessions were
carried out on a single day. To create the corresponding high and low
data files, the mean score for each day was determined, and the high
and low days were selected using the mean scores.
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Records containing the appropriate null dates were
inserted in these data files. Files were also
prepared that were subsets of the event-type data
files. Solar-flare data were cross-tabulated in
l0-deg longitude segments by daily dates. Flares
larger than Sub-Brilliant were cross-tabulated on
a daily basis by area, brilliance, and longitude
measurements. SIDs were extracted by LF Atmospherics
and cross-tabulated on a daily basis by the intensity
of the disturbance. The same was done for VLF Sudden
Enhancements (SES).
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A. (U) Results of Geophysical Analysis
(U) For many years man has speculated that relationships exist
between various biological functions and solar activity or its resulting
geophysical activity. Some empirical observations on the quality of RV'
sessions during periods of known solar activity led to the idea that a
screening of solar-geophysical variables for correlations might be
fruitful. If, indeed, measurable geophysical disturbances affect RV
performance, and these relationships could be defined, the reliability
of RV-derived information could be determined by considering the geo-
physical environment at the times of RV sessions, and possibly enhanced
by scheduling.
(U) This screening of geophysical variables in this report appears
in the order of gross measurements to refined measurements, which may be
closer to the actual mechanisms involved in influencing RV performance.
Because of the fact that changes in most measurable geophysical data are
initiated by solar activity, geophysical data sets co-vary. Hence,
relationships were found in several data sets simultaneously. These
correlations are most likely not due to cause and effect relationship
between the covarying variables, but rather are the result of one or more
of the geophysical processes that are driven by solar activity. Although
correlations to noncausative variables may be useful for forecasting
purposes, forecasts will be greatly enhanced when the correlations used
are closest to the actual geophysical-biological mechanisms involved.
2. (U) Solar Flux
(U) The solar flux is a measurement of the electromagnetic
output of the sun at several different frequencies in the megahertz range.
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~i~ -
(U)
The measurement values are in units of 10 22W m 2 Hz-3. These measure-
ments are a gross indicator of the activity on the solar disk. Epoch
analysis was performed for the high and the low scoring days of each of
three RV data sets. (No solar-flux values were recorded for most of
1979 in the NOAA data, and the 84(.I) data were not yet available when this
analysis was performed.) The relationships found by epoch analysis in
these data were generally unimpressive.
(U) Time lag correlations were run on two individuals from the
1981 experiment. Again, although certain correlations were found among
the mass of data (some of them statistically significant), there was
great variability between the individuals, and no definable pattern emerged.
3. (U) Sunspot Number
(U) Epoch analysis of RV versus sunspot number was performed.
A low peak was found between 29 and 35 days prior to each of the four
sets of high scoring sessions, and a high peak preceded each of these
sessions by 25 to 28 days. A low trough occurred 19 to 23 days before
low-scoring sessions in each of the four data sets. On the day of the
session, the sunspot number was at or above the epoch mean in each of the
four low-data sets. Out of this analysis emerged three time intervals
that may enable forecasting: 27 to 30 days, 16 to 19 days prior to the
sessions, and (for theoretical reasons addressed later) two to four days
after the sessions.
(U) To examine the time lags where possible correlations might
exist, time lag correlations were performed for each individual in the
groups from 1979, 1980, and 1984. Both positive and negative correlations
to sunspot number were found. These results also indicated that clustering
of correlations occurs near the time intervals both preceding and following
session dates found in the epoch analysis. Overall, 779 correlations were
performed. Up to 38 correlations at the p = 0.05 level would be expected
(U) 84(I) data were not processed.
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to occur by chance. Fifty-six correlations were observed, or about 50
percent over what would be expected by chance.
(U) Three time clusters were observed in the significant cor-
relations. In a four-day interval, where 76 correlations are represented,
the chance number of significant correlations at the p = 0.05 level is
estimated to be 3.8. The interval from 27 to 30 days prior to the session
(perhaps reflecting the solar-rotation rate) had eight individuals having
significant correlations at p < 0.05; two other individuals were very
close to significance. Another interval from 16 to 19 days prior to the
sessions yielded eight significant correlations ranging from p < 0.047
to p < 0.0003. A third period appeared two to four days after the sessions.
This three-day interval yielded five individuals having significant cor-
relations, and four others having near-significant correlations.
(U) It can be surmised that because these correlations exist
in patterns, there may be some phenomenon associated with sunspots that
could be more closely linked to the actual mechanisms that influences
RV performance. A likely candidate is the phenomenon of solar flaring,
and its resulting effect on the terrestrial geomagnetic field. In par-
ticular, the finding that the strongest correlations to sunspot number
consistently occurred two to three days after the sessions, points to
flaring. At first one might dismiss observation of RV performance
apparently forecasting sunspot number. However, if one considers that
large flares from active regions on the sun (sunspots) can frequently be
seen before the region rotates onto the solar disk, a hypothesis could
be formed that incorporates flaring as a possible mechanism, because
indeed, one can predict increases in sunspot numbers by observing flares
on the east solar limb.
4. (U) Solar Flares
(U) Solar flares usually occur near sunspot groups called active
regions. The sizes of these flares are classified in several ways by the
intensity of various characteristics associated with them, i.e., according
to their X-ray intensity, optical brilliance, area, and radio-wave
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signatures. There does not seem to be a readily-available comprehensive
index that takes all of the their characteristics into account. For this
reason, flares were subdivided into several data sets prior to analysis:
? The first set was the total daily number of all recorded
flares; these flares were further subdivided into
location of origin in l0-deg longitudinal segments from
the central meridian of the solar disk.
? The second data set was an extraction of the larger
flares from the data. These flares were selected for
their classification of both size and brilliance.
Those classified as sub-brilliant or larger were used.
(The flares were divided by longitude because flaring
at certain longitudes on the solar disk is known to
produce greater terrestrial effects than at other
longitudes. For instance, relativistic energy showers
most frequently originate from the northwest quadrant
of the sun.)
(U) In exploring the theory that limb flaring (flares on the
edge of the solar disk) may play a role in RV performance, special
attention was given to this area of the sun. Indeed, as anticipated,
significantly higher rates of limb flaring were found on the day of (or
just before) four of five of the high-scoring data setsand on the day
of (or just after) low-scoring data sets in four of five cases." What
this implies is that some confidence can be placed on the validity of
relationship between solar flaring and RV performance.
(U) Of key interest is the finding that the flares occurring
on the solar limbs (80 to 89 deg) tend to group with respect to high-
and low-performance data sets. Large flares occurred on the limbs the
day of sessions in three of four of the low-scoring data sets. In the
fourth set, a flaring peak occurred three days later. The number of flares
was significantly greater on low-scoring session days (more than two
standard deviations above the epoch period mean) in each case. The days
following low-scoring sessions continued to have high flare rates across
the solar disk. (On the other hand, in three of four cases, high-scoring
(U) 84(I) data were included.
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data sets were preceded by an increase in limb flaring by one day. These
values were near-significant. We shall return to this point below.) In
addition to the tendency of flares to cluster on poor performance days,
the numbers of flares was more than double the daily expected value for
three of the low-scoring RV data sets.
(U) A speculation from the data is that low-viewing scores might
be associated with flares (occurring on the east limb) rotating onto the
solar disk (because of the numerous flare peaks observed at all locations
on the disk following poor RV sessions). By contrast, high scoring might
be associated with regions rotating off the west limb of the solar disk
because of the fewer flare peaks following good sessions. Indeed, when
diagrams of solar-active regions were examined for the first set of 1984
data, nine of ten of the low-scoring days showed active regions near the
east limb. On the other hand, eight of ten of the high-scoring days
showed active regions near the west limb.
(U) Based on the observation that, statistically, limb flares
coincide with and follow degraded RV sessions, but precede enhanced RV
performance by one day, a speculation could be made that there may be a
close time relationship for enhanced and degraded RV sessions. In
hypothesizing which solar-terrestrial changes could cause changes in RV
performance, the observed rapidity of change and the reversal of the
character of the effect must be taken into consideration. The following
scenario is suggested. As a new active flaring region rotates over the
solar limb, associated with it is some kind of a fast-acting phenomenon
that requires a day or less to produce terrestrial effects. The terrestrial
effect must be relatively brief, and of a character that is capable of
both degrading and enhancing RV performance. An example of one such
phenomenon is the Sudden Ionospheric Disturbances (SID) caused by X-ray
and other electromagnetic-radiation bursts associated with the flaring
process. SIDs can both enhance and decrease the quality of global
electromagnetic signal propagation--possibly at biologically-active fre-
quencies. A specific type of SID, a direct solar-flare effect (SFE),
causes geomagnetic effects observed simultaneously with solar flares.
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Another mechanism could be proton storms resulting from high-energy
flares. Very-high-energy flares can accelerate particles at near-
relativistic speeds; these particles are capable of reaching earth in a
matter of a few hours. Magnetic storms may accompany the arrival of
this high-speed solar wind. Either of these two mechanisms could explain
the immediacy of the observed effect.
(U) With regard to the tendency for RV performance to first be
degraded then enhanced, one might speculate on possible mechanisms for
bipolar behavior based on, for instance, known patterns of magnetic storms.
If RV performance were correlated with the intensity of the earth's
magnetic field, then the following known variations that occur as part
of the magnetic storm process might be capable of introducing the observed
patterns into RV performance. Typically, the storms that begin suddenly
start with.
(1) A sudden increase in the horizontal (H) component of
the earth's magnetic field; they then
(2) Have an initial phase lasting a few minutes to a few
hours, during which H decays to prestorm values;
(3) This is followed by a main phase lasting about one to
three days in which H is below the prestorm value,
first decreasing, then increasing more slowly toward
the prestorm value--many large random variations
occur during this phase;
(4) There is a postperturbation period after the end of
the main phase, in which the value of H continues to
rise toward, or perhaps above the prestorm value;
(5) The last phase is an increase in the daily variation
of H, which increases with increasing latitude. If
RV performance correlated with magnetic intensity,
patterns would certainly be found during the magnetic
storm.
(U) Magnetic Indices
(U) Four magnetic indices, the sum of Kp, A p p
, C , and C9 were
screened with respect to RV performance. K P is a quasi-logarithmic
index of geomagnetic stations between latitudes 47 and 63 deg. K is
specifically designed to measure solar particle radiation by its magnetic
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effects. A P is a daily index of magnetic activity on a linear scale
rather than on the quasi-logarithmic scale of the K indices. It is the
average of the eight values of an intermediate three-hourly index Ap,
defined as approximately one-half the average gamma range of the most
disturbed of the three force components D, H, and Z, in the three-hour
interval at standard stations. The C index is derived from the K
p p
indices by converting the daily sum of A P into the range of 0 to 2.5.
The C9 index is a geomagnetic character figure obtained from the C P index
by reducing the C P values to integers between 0 and 9 according to certain
keys. All these values are closely related, and, not surprisingly, gave
similar results in the epoch analysis comparing them to RV performance.
(U) The four sets of RV scores yielded remarkably similar
patterns with respect to K P , and corroborated the solar flare findings
extremely well. In each case, values were above the epoch mean the day
preceding low RV scores, decreasing in all cases on the day of the sessions.
In three sets, the decrease fell below the mean; in the fourth case, the
value dropped considerably and was only slightly above the mean. The
next day's values of this data set continued to decrease and were below
the mean. Conversely, in the sets of high-scoring days, all four sets
showed K P indices that were below the epoch mean the day before the
sessions and increased to values above the mean in three of the four data
sets. The fact that these patterns were replicated in the majority of
the test data sets, and that high- and low-scoring data sets yielded
inverse patterns with respect to K P is a strong indication of a valid
phenomenon existing. Therefore, what we found were strong similar patterns
in the geomagnetic data with respect to RV performance. The geomagnetic
field was quiet just before all high-scoring sessions, then became more
active the day of the session. Magnetic activity was higher than normal
one to five days prior to low-scoring sessions. This would suggest that
RV sessions be performed when the activity of the geomagnetic field is
on the increase after a quiet period.
(U) Comparing these consistent results with the results of the
solar flare analysis shows that there is evidence for the standard
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one-to-four day time lag between flare and subsequent magnetic activity.
The magnetic indices are increasing on Day 0 (the day of the sessions),
they continue to increase the day after the statistical flaring, then
decrease in two to five days. Results using A P , C P , and C9 indices were
similar; the best definition appeared to be seen with the K P index
followed by A , P C P , and C9. For forecasting purposes this is fortunate.
The K P (and A P ) are readily available by means of the satellite downlink,
C and C9 are not.
p
(U) Sudden ionospheric disturbances result mostly from flaring
of the sun, although not all flares cause SIDs, and SIDs can also result
from unknown causes. SIDs are basically a disturbance of the upper ionized
layer of the atmosphere called the ionosphere, specifically in the D region.
During SIDs, radio waves at medium frequencies are strongly absorbed and
long-distance communications fade out. Cosmic radio noise at 15 to 25 MHz
is absorbed and also fades. At very low frequencies (15 to 40 kHz),
however, the D region becomes a reflector, and atmospheric radio noise is
enhanced. A SID is marked also by a sharp brief geomagnetic disturbance
that behaves differently from a magnetic storm. SIDs are classified
according to their intensity and types of terrestrial effects.
(U) Although many classifications exist, the two most common
(known to enhance low-frequency transmission) were extracted from the
data base; i.e., those classified as enhancing atmospherics, as measured
at 27 kHz (SEA), and those which enhance signal strength in VLF trans-
missions (SES). (The effects of SIDs on ELF is not monitored and is
unknown )
(U) The results of the epoch analysis on SIDs correspond well to
the results of the solar-flare and the magnetic analyses. While the SEAs
showed some corroborative patterns, the results of the analysis on the
SESs were remarkable. The results of the SIDs analysis are similar to
the analysis of the occurrence of peaks of solar flares. That is, there
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are fewer peaks occurring around the times of high-scoring sessions than
low-scoring sessions. There are more strong S1Ds peaks on the day of,
and after low-scoring sessions. A SIDs peak of importance (magnitude)
or greater occurs on the day of the low-scoring sessions in four of
the data sets. Peaks of SIDs also occurred on the day of the session in
four of five years of the high-scoring data sets, and on the day prior
to the sessions in the other data set (1979). Therefore, nine of ten of
the data sets had an SES-type SID occurring on the day of the session.
The remaining data set had a peak in SIDs occurring in close proximity
to the session--on the day before.
(U) It appears that SES-type SIDs are a good candidate for the
previously hypothesized fast-acting result of solar activity that could
influence RV performance. A peak of SES-type SIDs have occurred on or
near every case of RV extreme-score sets. We speculate that there is some
quality of the SID that is associated with the observed bipolar effect,
resulting in both high- and low-quality RV performance. A quality of
SIDs that might produce bipolar effects could be selective electromagnetic
propagation. If SIDs selectively propagate certain VLF or ELF frequencies,
it would not be surprising to find differing biological responses to
frequency and intensity changes--assuming biological responses to ELF
exist. Laboratory studies of ELF magnetic and electric fields (see
references to work of Adey and others in Bibliography) suggest such ELF-
biological relationships. An additional possibility to account for bipolar
behavior might be the frequency characteristics of the atypical magnetic
activity that accompanies SIDs. This can be accomplished by direct measure-
ment of geomagnetic ELF, described in the section below.
(U) The ELF environment, both naturally-occurring and man-made,
is one good candidate for the mechanism by which many types of biological
responses are produced. Laboratory studies have shown both sensitivity
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and selectivity in biological responses to specific frequency and intensity
windows. The intensities found to be biologically active are very weak,
and fall in the same ranges as naturally-occurring magnetic and electrical
fields. For our initial effort, we selected the frequency window of 0
to 30 Hz for two reasons, one pedagogical and one practical:
? It is the frequency range of the known electrical activity
of a human being--particularly brainwave frequencies.
? It is necessary to filter out 60-Hz power-line noise,
and simple electronic 60-Hz filters begin cutoff at
about 30 Hz.
With regard to the latter point, although sharper filters are available,
they are elaborate and expensive. For this exploratory investigation,
the most cost- and time-effective route was chosen.
2. (U) Intercomparison of ELF Systems
(U) A number of comparisons between the various components of
the ELF monitoring system have been made. The systems compared include
the prototype system at TRI involving the use of the oak-tree bioantenna,
the upgraded system at TRI involving use of both the oak tree and a coil
antenna on loan from the Radioscience Laboratory at Stanford University,
and a duplicate upgraded system at SRI involving use of an oak tree.
(U) Correlations were performed between ELF data sets generated
on each of the systems. Although variations existed, by and large the
correlations indicated statistically-significant tracking between the
various systems, including the comparison of key concern--tracking between
the duplicate upgraded systems at TRI and SRI, 17 km distant. In fact,
tracking between the separated TRI and SRI systems, both utilizing oak-
tree antenna, was somewhat better than tracking between the oak-tree and
coil antennas, both at the TRI site.
3. (U) ELF/RV Comparison
(U) Epoch analyses were carried out to determine correlations
between ELF and RV data sets for those periods of overlap in.operation.
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(U)
With regard to the prototype ELF system, whose data base began in May of
1982, the 1984 data base (before the fifth data base was added) was the
only one available for comparison. Examination of the correlation graphs
in the epoch analysis revealed some tendency (over time spans on the order
of days) to inverse relationship between ELF and RV; that is, a tendency
for ELF to be low in the vicinity of high-scoring days, and vice versa.
There were not sufficient data of sufficient definition, however, to permit
statistically-significant conclusions to be drawn.
(U) Beginning in June 1984, the upgraded ELF system became
operational. RV data collected from then through August provided an
opportunity to perform preliminary epoch analyses for a small sample of
RV data (four cases each for high and low scores) on a half-hourly basis.
(This is to be compared with the half-daily basis of the prototype ELF
system.) An apparently consistent pattern that emerged in these analyses
was a change from low-scoring sessions to high-scoring sessions during a
rapid rise in ELF values; the separation between the two was on the order
of an hour and a half (range one-half to two hours and a half). This
appears as another example of bipolar effect mentioned earlier with
respect to geomagnetic activity, where rapid crossing from below- to above-
average activity on the days of extreme-scoring sessions was also observed.
The time/funding scope of this level-of-effort project did not permit
further exploration of this emerging relationship between ELF (and other
geophysical activity) and potential rate-of-change effects on RV per-
formance. This area appears to show exceptional promise, however, and will
be pursued in a follow-on program.
(U) The emergence of a possible relationship between a period
of time when the data are changing at a rapid rate (rather than peaking)
is not surprising in light of known mechanism of biological processes.
Several biological processes function by means of rates-of-change of
stimuli in preference to any particular absolute values of those stimuli--
if the intensity of the stimuli fall within certain ranges.
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VI EVALUATION AND RECOMMENDATIONS (U)
(U) This study has a threefold purpose:
? To determine whether geophysical factors correlate with
RV performance.
? If correlations are found, to identify which geophysical
factors correlate with RV performance with enough lead
time that RV performance could be forecast in advance.
? To optimize the potential for forecasting by seeking those
geophysical factors that constitute the best candidates
for the mechanism by which observed effects on RV per-
formance are produced.
(U) Some degree of success has been achieved in all three categories.
Significant correlations of some significance have been found to exist
between RV performance and solar flux values, sunspot number, and magnetic
indices Mp, Ap, Cp, and C9. Epoch analysis has shown that flares and
resulting SIDs have a strong tendency to cluster in certain time intervals
with respect to extreme-scoring RV sessions. Flares, especially those
on the solar limbs, tend to occur on the day of low-performance RV
sessions, and on the day preceding high-scoring sessions.
(U) SIDs resulting from flares may produce a bipolar effect, such
that an intimate time relationship exists between high-scoring and low-
scoring sessions. Epoch analysis on SIDs show enhanced sudden enhance-
ment of signal strength in VLF (SES) on the day of, or before, both high-
and low-scoring sessions.
(U) Evaluation of the ELF environment with data from the prototype
ELF monitoring system revealed patterns in ELF measurements a number of
days preceding the sessions which were oppositely configured over time
with respect to high-and low-scoring sessions.
(U) A very encouraging aspect of this study is the corroboration of
the findings by all of the various data sets examined. The relationship
of geophysical phenomena to RV performance may be traced from solar
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(U)
activity, to terrestrial activity--including SIDs and geomagnetic activity.
All of these data sets appear at specific time intervals, which are known
to occur with respect to each other, and at definable intervals with
respect to the RV performance data.
(U) The following conclusions have been reached as a result of this
data analysis:
? Correlations between several geophysical phenomena and
RV performance are significant, and have a high probability
of constituting a valid cause-effect phenomenon.
? Time periods of low-scoring sessions cluster (within 24
hours or less) with high-scoring sessions. This suggests
that the mechanism that influences RV performance has a
bipolar effect.
? ELF appears to be a reasonable candidate for a linking
mechanism; it is capable of changing rapidly, is most
likely affected by SIDs and other solar effects, and
specific changes have been noted at the times of the
sessions.
? Should further in-depth statistical studies confirm the
results to date, a successful long-range forecasting
system could possibly be developed using a combination
of solar-terrestrial and monitored ELF data. Solar-
terrestrial correlations were significant, but the time
intervals are not yet defined accurately enough for
reliable forecasting. Successful forecasting might be
accomplished by using solar-terrestrial data to identify
approximate time periods when enhanced and degraded
performance is expected. Then, by monitoring the ELF
environment during these periods, the exact effects can
be known or anticipated.
(U) The objectives of a follow-on effort should be to (1) confirm
the findings (especially with respect to the ELF data), (2) seek finer
time resolution, and (3) clarify the mechanisms involved in the RV solar-
terrestrial relationship. The final product of such an effort would be
trial forecasts in a blind study in order to determine whether forecasting
(and its companion, scheduling) can lead to increased reliability of the
RV process.
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VII SUMMARY (U)
(U) Past experiments in RV have shown considerable variability in
RVer performance over time; that is, RVers are seen to perform better at
some times than at others. This variability has not yet been fully
explained nor examined in detail. The possibility exists a priori that
performance might be affected by certain external factors. If this is
the case, identifying these factors and understanding the way they influ-
ence a trainee's performance could lead to significant improvement in the
RV product.
(U) As a starting point, human beings have been shown to be sensi-
tive to certain forms of electromagnetic radiation that are known to
exist in the geophysical environment; there is also some evidence that
certain aspects of human behavior and performance are related to changes
in yet other aspects of the geophysical environment. An important question
relative to RV performance is the extent to which a trainee's performance
is subject to such electromagnetic or geophysical factors. Investigation
of this question is necessary in the course of developing a reliable RV
capability. The results of such an investigation would show whether RV
performance is influenced by these factors, and, if so, the degree to which
such influences can be controlled, and whether they point to directions
for further research into the fundamental nature of psi function in its
broadest sense.
(U) The project described in this. report is a first step in the
process of determining the relationship between RV performance and
various environmental factors. This project had a two-part aim. First,
it was necessary to set up an environmental monitoring facility for real-
time measurement of certain variables, and to identify available, reliable
sources of other environmental data. Second, the degree of correlation
between the available environmental data and the RV results needed to be
developed. In this report, we describe the monitoring facility and the
37
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LAS
(U)
specific environmental variables, and a preliminary analysis of the cor-
relations between RV performance and these factors is given. Some inter-
pretation is also provided as an aid to planning future work along these
lines.
(U) Basically, in the monitoring facility we detect and record the
local geomagnetic field, using a Fourier analysis technique to separate
the various frequency components in the electromagnetic spectrum (in the
range 0 to 30 Hz). A prototype facility had been in operation for more
than two years, and some relatively coarse data were already available
when the project began. These data, and the experience gained while
gathering them, were used to design and calibrate more precise instrumen-
tation for this program. Data from these systems were then used in the
investigation to correlate against RV performance data for the corresponding
time period of operation of this facility. An equally important part of
the project consisted of correlating RV performance data against other
geophysical observations available from the National Oceanographic and
Atmospheric Administration--some of it by real-time acquisition from a
Westar IV satellite downlink installed specifically for this purpose.
(U) As shown in this report, several significant correlations have
been found between various geophysical factors and RV performance. These
correlations have a strong tendency to cluster in certain time intervals
with respect to high- and low-performance RV sessions. In some cases,
the clustering precedes the correlated RV activity, thereby yielding the
possibility of performance prediction, should such correlations continue
to be viable in further work.
(U) Among the most interesting correlations to RV performance found
are flares occurring on the sun, especially those on the edge (limbs) of
the solar disk. These flares play an important role in producing upper
atmospheric disturbances known as SIDs (sudden ionospheric disturbances),
which influence terrestrial radio-signal propagation. SIDs are known to
block higher-frequency communications, but at the same time, to enhance
lower-frequency propagation at LF and VLF frequencies. Although the
effect of SIDs on the yet lower-frequency ELF portion of the electromagnetic
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1
L
(U)
spectrum is unknown, it could provide a promising link between the solar-
terrestrial environment and known electromagnetic effects on biological
processes. With regard to ELF itself, preliminary evaluation of the ELF
environment in half-hourly time intervals has shown a possible relation-
ship to frequencies between 10 and 30 Hz, particularly as ELF intensities
change from below average to above average values.
'b Considering the modest level-of-effort for the survey
of geophysical/ELF factors, and their possible relationship to RV per-
formance, a considerable amount of progress has been made in delineating
potential correlations of value. What can be said at this point is that
this pilot study provides evidence that the quality of RV functioning may
be intimately related to the geophysical environment. What remains to be
done is (1). an in-depth statistical evaluation of those findings of this
study that were strongly intercorroborated by the various data sets used,
and (2) a structured attempt at blind RV performance forecasting. As a
result, continued collection and analysis of such data will be pursued
to determine whether the correlations found are stable over time, and
will thus provide a solid continuing basis for RV performance prediction.
From both scientific and practical viewpoints, knowledge of this kind makes
it possible' i to consider methods for`
enhancing the overall RV product,.
F,
39
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REFERENCES (U)
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Electric Fields and the Different Aspects of These Studies,"
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on Reaction-Time Performance," Nature, pp. 949-950 (4 March 1967).
4. Konig, H. L., "Behavioral Changes in Human Subjects Associated with
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 Approved For Release y8/e8A ~l 0788ROO1800300001-0
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