Graphical Summary of 12 Case Histories.
Click on image to enlarge, or click on title to go further down the page to case history initial documentation.
1. Colorado County, Texas2. Integrating aeromagnetic data3. Major oil company study
4. Predicting anisotropy5. Cumulative Oil Production6. Relating fields and strikes
7. Michigan8. Lightning predicted faulting9. Lightning time consistency
10. Lightning by Wind Towers11. Lightning at BEG faults12. Positive Strikes at faults

Lessons Learned:

Consistant results from lightning studies to date include the facts:
  1. Lightning strikes and lightning strike attributes vary spatially. Yes, this means lightning is more likely to strike some places than other places.
  2. There is consistency in lightning strike density from year to year. This is particularly true once the data are normalized and noise and bias are removed from the data.
DML's model to explain these noted variances and consistencies is telluric currents control the location and intensity of lightning strikes. Lightning starts in the atmosphere and is regionally controlled. There are more lightning strikes in Florida than anyplace else in the U.S. specifically because of oceans on both sides of Florida. There are fewer strikes in California because there are fewer storms and it is a desert climate. The southern Lake Maracaibo basin in Venezuela is another very active lightning strike area. In this area methane seeps create a breakdown in atmospheric resistance, and enhance lightning stike density and intensity. The mountains surrounding the lake force warm humid Caribbean air up to meet cold air from the Andes, creating thunder storms. Methane seeps from subsurface hydrocarbon deposits rise and break down the insulating properties of the air. Bursts of blue, pink, and white lightning flashes are almost continuous, nearly 200 nights and days of the year.[see National Geographic, February 2010]

Lightning Basics
Lightning is a rapid discharge of electrical energy in the atmosphere.[see NOAA video] These electrostatic dicharges occur within a cloud, between clouds, or between a cloud and the ground. Lightning begins in clouds with large concentations of positive and negative space charge. Cloud-to-Cloud (CC) lightning often travels 250 km or more. This is 2 1/2 times the distance of recently discovered (late 1990's) upper atmosphere lightning events like blue jets, sprites, and elves. Cloud-to-Ground (CG) lightning discharges have polarity (e.g. there are positive strikes and negative strikes). Preliminary cloud breakdown initiates an intermittent, highly branched stepped-leader dicharges which propagate horizontally and downward. The large atmospheric electric field connects with telluric currents and causes one or more upward propagating discharges to form. When the upward discharge makes contact with the stepped-leader, the first return stroke begins. The return stroke is an intense wave of ionization starting at or just above the ground and propagating up the leader channel at one-third the speed of light. The peak current in return strokes can reach several hundred thousand amperes. There are few lightning strikes in deep water oceans, which DML asserts is because there is more separation from telluric currents. There are few lighting strikes in the winter in northern latitudes. This is certainly related to the lack of upward thermal currents in cold weather. DML asserts this is also because snow acts as a resistor, shielding the atmosphere from telluric currents.

Tradition attributes lightning strike location to topography, trees, and infrastructure (oil and gas wells, pipelines, facilities, radio towers, cell towers, wind turbines, etc.). While topography, trees, and infrastructure each have an impact on strike location, none of them are the fundamental cause of lightning strike location nor intensity.

For instance, DML found topography has a linear increase on the number of lightning strikes, at least in North Dakota. Applying two filters, DML found:
  1. The local relative relief for each 1 km grid cell in North Daktoa (i.e. the maximum relief within 2 km of the center of the grid cell). North Dakota is fairly flat, and local relief varies between 1 meter and 40 meters.
  2. The number of strikes within 2 km of the center of each grid cell. This number varied between 1 and 8 strikes per year.
There is a linear increase in the number of lightning strikes with local relief (from 1 strike with 1 meter of local relief to 8 strikes with 40 meters of local relief in North Dakota). These small variations appear to only be explained by the shortening of the path through the atmosphere (between 8 and 40 meters out of 2,000 meter cloud height, or between 0.5% and 2.0% of the atmospheric path of the lightning.

The electrical conductivity of air is 0.3-0.8 * 10-14 siemens/meter. Using Archie's equation to calculate the rock conductivity for a porous rock with 100% brine saturation shows an expodential increase from 0 siemens/meter at 0 porosity to 0.025 siemens per meter at 35% porosity. For a typical sedimentary rock, with 5% porosity, the electrical conductivity is 5.0 * 10-4 siemens/meter, or about 1010 times the conductivity of air. It follows from the hundred fold differences in conductivity, the variations in porosity or fluid resistivity in the earth have a larger impact on lightning strike frequency and location than topography.

The most common trees to be struck by lightning are oak and elm trees. Oak trees have an average height of between 50 and 70 feet. Elm trees have an average height of between 100 and 125 feet. Both these heights are less than the 40 meters (131 feet) local relief in North Dakota discussed above. It folows the same logic of this distance being less than 2% of the atmosheric path of a typical lightning stroke has less impact on lightnig strike frequency and location than telluric currents. The difference is trees have root systems which connect to the subsurface and thus to telluric currents. Certainly in the case of oak trees, the root system can be compared to an inverted Van de Graaff Generator. There does not seem to be research on the roll of roots in connecting the electrostatic charge on trees to telluric currents. DML did find a relationship between the distribution of oak trees and the countours of the count of +CG strikes in Colorado County, Texas. Note these two linked images cover the same area at the same scale. There certainly appears to be a lot of opportunity for research in this area.

Infrastructure certainly impacts lightning. There are a lot of photos of lightning hitting radio towers, or at least in the vacinity of radio towers. However, by comparing maps of topography, strike density, average Rise Time, and average Peak Current it becomes obvious the distribtution of lightning strikes and lightnigs strike attributes is neither controlled by topography nor infrastructure. The densly drilled Beaver Lodge Field is located on the the crest of the Nesson Anticline and is approximately where the small box in the 5th longitude cell from the left and the 3rd latitude cell down from the top on each of the four North Dakota maps linked above. There are no more lightning strikes in the center of the densely drilled Beaver Lodge Field than there are 50 miles to either side, where there is no infrastructure and there are no wells. Case History 9, Lightning Time Consistency, and Case History 10, Lightning by Wind Towers, each describe a similar situations, where lightning strike locations are not controlled by infrastructure in the area of the study.

Earth Tides
In North Dakota earth tides, tied to the rotation of the moon around the earth, have more impact on when and where lightning strikes occur than topography, vegetation, or infrastructure. By calculating the moon local longitude, another column can be added to the lightning strike data specifying what degree of earth tide to assigned to each strike. High Tides occur when the moon is directly overhead (0o) or on the other side of the earth (180o). Low Tides occur when the moon is off to the side (either -90o or +90o). A graph of the Lightnning Strike Frequency vs Tital Gravity illustrates there can be 22% more lightning strikes at high earth tide than occur at low earth tide. In North Dakota this is 180,000 strikes at high luner tide and 140,000 strikes at low lunar tide between 1999 and 2008. The DML model is these strikes are occuring because there is a difference in Earth's stress strain field at different stages of earth tides, and/or because the faults are opening and methane gas is escaping and disrupting the resistivity of the atmosphere during high earth tide.

What Lightning Measures
The telluric currents controlling lightning strikes are impacted by:
  1. Faults, which we have been able to predit, map, and confirm with seismic. There is an unresolved issue as to depth of penetration. We are mapping basement faults which come to the surface, as well as growth faults which come to the surface.
  2. Brines and hydrocarbons in fault.
  3. Salt Domes.
  4. Mineralization in salt dome caprocks.
  5. Mineralization in faults.
  6. Sediment thickness.
  7. Fracturing or anisotropy, which appears to have a direct relationship to ductile or brittle shales in resource plays.
  8. Seeps (methane is resistive in a reservoir and yet methane seeps are highly electrostatic).
  9. Earth Tides.
  10. Stress and strain in rocks, possibly a precurser to earthquakes.
Early on, Jim Siebert found the following quote found regarding lightning and natural resources:
"Not expected, however, was the unusally high percentage of Cloud-to-Ground lightning flashes of negative polarity with Imax > 75 kA found over the salt waters of the northern Gulf of Mexico, and off the southeastern U.S. coastline. The reason for the large number of intense -CG strikes in this region is not clear. While perhaps associated with the high conductivity of the underlying saltwater, the fact this pattern tends to extend more than 100 km inland suggest that surface features are not the only causative factor." [Large Peak Current Cloud-to-Ground Lightning Flashes during the Summer Months in the contiguous U.S., Lyons, Uliasz, and Nelson, FMA Research, Inc., American Meteorological Society, 1998]
Both of these areas are where there are concentrations of hydrocarbon seeps. Seeps from the concentration of oil and gas fields along the Gulf Coast coastline and from gas hydrate deposits on the Southeastern US shelf help explain the large number of intense -CG strikes.

Telluric currents have been studied by geophysicists for decades. Magnetotellurics is a mature geophysical exploration tool. Lightning data is a new data type, and DML is just beginning to open an understanding of how this data type can help explore for natural resources. The examples below show documentation status of various studies DML has worked on.

Case History 1:

1. Colorado County, Texas
The first study DML did was in Colorado County, Texas. The goal was to study variations in lightning strike data and see if there was a relationship to subsurface geology and known oil and gas fields. To display a map of known oil and gas fields in 2004 click here (similar links are available in this summary to key images associated with this study). These locations are a screen capture made using Landmark Graphics Seisworks 3-D software. The evaluation process started with setting up a grid with 1,000 feet per cell grid covering Colorado County. Lightning attributes were derived from the Vaisala database, plotted as control points, and various combinations of maps were overlaid to evaluate the variations in lightning strike data and attributes. The map of control points for the count of all lightning strokes in Colorado County shows how dense lightning strikes from 1998-2008 are in coastal Texas counties. Note there were up to 263 lightning strikes per 1,000 foot square grid cell from 1998 to 2008.

Geostatistical Analysis
The data were also loaded into Spotfire to do geostatistical analysis. Again, a map display of the count of lightning strokes shows the density, as well as the variation in the density, of lightning strikes in Colorado County. We evaluated the data on many different dimensions in Spotfire. This type of data mining provided a basic understanding of how rich the Vaisala NLDN (National Lightning Detection Network) data is. We also learned there are limitations with the data. On displays like plotting strike data against peak current (PC) and rise time (RT) it became immediately obvious the data are not consistant across time. It turns out there was a major lightning sensor instrument upgrade in the spring of 2002. They also started filtering out stokes with rise time greater than 30.2 microsecondds because a large number of these strokes were CC (Cloud-to-Cloud) lightning strokes.

Mapping Basic Attributes
The maps generated on the Landmark workstation each show the lateral variations in lightning strike density and attributes. Contours of the density of lightning strokes shows much more lightning activity to the southwest, south, and east, which is where the largest gas fields in the area are located. With this initial correlation, the next step was to understand the spatial variations in the basic collected lightning data. For each stroke there is a location and a time of occurrence recorded in the database. The key attributes measuring the waveform of the stroke are: Rise Time (RT) - up to about 50 micro seconds; Peak Current (PC) - up to 50 kilo-amperes; and a Peak-to-Zero (P2Z) time - up to 25 microseconds. The number of strikes per cell with a specific range of attributes can be counted, measured, averaged, or otherwise analyzed. RT can be added to P2Z to get the Total Wavelet Time (TWT), and RT/TWT provides a measure of Wavelet Symmetry (WS). We did not realize we could calculate TWT and WS when this study was done.

The count of RT strokes from 1-35 microseconds shows minor variations across Colorado County. while there is a higher Average RT in the eastern half of the county where the Eagle Lake fields are, than in the western half of the county. The Average PC shows an interesting non-geologic north-south trend through the county. It is non-geologic in the sense there are three large growth faults across Colorado County which strike west-east. It is interesting the largest Average PC occurs on the downdip portion of the northern growth fault, which is directly above where sediment growth wedges would occur. The count of P2Z measurements from 30-75 microseconds is more even, and the higher count appear to follow the downdip strike of the middle growth fault across the county. The largest The P2Z count anomally is directly over the large Sheridan gas-condensate field, which dominates this map of 1994 Known Resources in the western part of the county. The map of Average P2Z Time shows fewer anomalies.

Oak Trees
The north-south trend noted above with the map of Average PC is highlighted even more with the map of Positive Strokes. The strongest positive strokes follows the distribution of oak trees through Colorado County. This correlation is especially obvious when the map of the count of Positive Strokes is displayed at the same scale, or by flipping back and forth between the images. Note there are completely different trends for a contour map of the Count of Negative Strokes. These strong anomalies appear to be closer related to known hydrocarbon deposits. There is much more work to be done to better define what is correlation and what is causaulity.

Vaisala also collects several attributes which help define the quality of their data, and which are useful in doing statistical analysis of lightning strikes. These attributes start with the Number of Sensors used to record a particular lightning strike. In Colorado County, Texas, there are typically 5 or 6 sensors recording each lightning strike. This varies across the country. The more sensors recording a strike, and the better correlation between these recordings, which is measured by the Chi-Squared attribute, the better the strike data is. Mapping the Average Chi-Squared shows higher correlation in the western half of the county. To triangulate the location of a strike, there are two axes defined from data from the contributing sensors: the Semi-Major Axis; and the Semi-Minor Axis. Contouring the Average of the Semi-Major-Axix shows significant anomalies in Lavaca County to the west. Contouring the Average of the Semi-Minor-Axis shows banding which is possibly related to insufficient sensors collecting data to towards the northeast. These anomalies need to be worked through and steps taken to remove data recording anomalies. As a result of this work, DML's license agreement with Vaisala includes the right to set up our own or client lightning sensors and to have this data remain private or be integrated in with the NLDN, depending on customer requirements.

We also did mapping tests to evaluate lightning density variations across time. It seems like if there is any impact from methane seeps they would show up in night time lightning strikes, when there is not a lot of other atmospheric contamination. So we made a map of the Number of Strokes between 9:00 PM and midnight and of the Number of strokes between 3:00 and 7:00 AM. The PM map shows a trend similar to the Positive Strokes, with an additional east-west trend which follows the reservoir pressure or geopressure pods we mapped from production data between the two northern growth faults. The AM map shows fewer anomalies, except around the edges of the county. These anomalies are mutted and become "out of focus" with a gridded map of the number of strokes between 3:00 and 7:00 AM. The anomalies at the north-northeast end of the county overlay a gridding of the highest topography in the county. As a test to see if this possible topography effect could be filtered out, the topography map was used to rework the map of 3-7:00 AM strokes, now filtered by the topography. The relationship between these three maps is seen by flipping between the 3-7:00 AM count, grid, and filter maps.

Another way to look at lightning strikes and time is to think of the time of the year. There are more storms, and thus more lightning strokes, in the summer (except for 2011), fewer in the winter, and spring and fall can be considered fringe storm periods. A map of a count of spring strokes shows between 30 and 90 strikes per grid cell, with anomalies over the eastern Lavaca County fields and over the Eagle Lake Fields. A map of the count of summer strokes across 1998-2008 shows anomalies in the southwest and northern portions of the county. A map of the count of fall strokes shows anomalies south of the county and where the Eagle Lake fields are located. A map of the count of winter strokes shows anomalies to the south and northeast. Mapping the sum of the count of strikes in the spring plus in the fall shows strong anomalies to the west, south, and over the Eagle Lake fields. Again, this project was a first pass at understanding the distribution and variations in lightning strike densities, and needs to be followed up with many more studies.

Spotfire was used to look at the number of lightning strikes each hour of the day (01:00-02:00, 02:00-03:00, 03:00-04:00, 04:00-05:00, 05:00-06:00, 06:00-07:00, 07:00-08:00, 08:00-09:00, 09:00-10:00, 10:00-11:00, 11:00-12:00, 12:00-13:00, 13:00-14:00, 14:00-15:00, 15:00-16:00, 16:00-17:00, 17:00-18:00, 18:00-19:00, 19:00-20:00, 20:00-21:00, 21:00-22:00, 22:00-23:00, and 23:00-24:00), and then these were composited into a movie stepping through the hours of the day (00:00-24:00).

Lightning strike density varies. This study was done before DML understood the need to and had developed methodologies to clean the lightning database. With each project we learn more about the data, it's strengths and it's limitations.

Mapping control of Known Resources in 1994 from the Nehring Data shows where historical fields in Colorado County are located. These fields can be can also be looked at by plotting the spatial distribution of reservoir pressures, or by looking at reservoir pressure vs. depth history. When a map zoomed on stroke count is overlaid on the 1994 Known Resources there appears to be some correlation between lightning strike density and hydrocarbon deposits. In the image summarizing this Case History (strike density contours overlaid on a Geomap display of known hydrocarbon fields), areas of correlation with known fields (white circles) and areas of potential exploration (yellow circles) are noted. This image was extracted from a larger Geomap with lightning strike density contours overlaid. Note the contours range from 75 to 250 strikes per cell. We did look at some 2-D spec seismic data across two of the areas of potential exploration, and there are solid geologic reasons to believe these might be valid exploration opportunities.

DML's working model is while the gas in the subsurface is resistive, gas in seeps is highly electrostatic, and disrupts the resistivity of the atmosphere enabling a path for lightning leaders to follow, which creates more lightning strikes over hydrocarbon deposits. This would especially be true along the Gulf Coast, where seals are not strong enough to trap gas for long geologic times, and where The Global Basin Network has taught us only 2% of the hydrocarbons generated are trapped.

Case History 2:

2. Integrating aeromagnetic data
At the same time DML did the siliciclastic study in Colorado County, TX, we wanted to do a carbonate study. We selected Stuben's County, New York because we had access to a small 3-D seismic survey for a client project. We are only sharing maps for a quarter of the area studied in this presentation in order to retain client data confidentiality. There were many things we learned doing this study, which these notes summarize in order to insure a common geologic framework for future discussions.

Basement Shear Zones
S. Parker Gay, Jr., owner and president of Applied Geophysics, Inc. in Salt Lake City, has spent his career espousing the importance of reactivation tectonics to natural resource exploration. In his book, Reactivation Tectonics, Parker describes how plate movement has reactivated basement shear zones over the years. We see these basement faulting patterns in outcrops across the world, including on the Canadian Shield by Toronto, where the basement shear zone faulting patterns can easily be highlighted. Other examples from Parker's book include the Guyana Shield outcrop, the African Shield outcrop, the Canadian Northwest Territories outcrop, the Canadian Nunavaut Territory outcrop, and the Saudi Arabia outcrop. Basement Shear Zones are a worldwide occurrance, with significant implications to natural resource exploration. One of the best proven geophysical ways to evaluate reactivated fault zones buried under sedimentary cover is with aeromagnetic data. This case study is about an area buried under carbonate (including Black River - Trenton) and siliciclastic deposits in western New York state, which deposits have been fractured by reactivation of basement shear zones.

Aeromagnetic Data
Applied Geophysics sold their aeromagnetic data to our common client, and we are sharing portions of this data with permission from Parker. The location of the study area is shown with a map of the aeromagnetic flight lines over the area. These data were loaded in to a Landmark Graphics interpretation workstation and a variety of maps generated. The first example from the subset area being shared herein is a total intensity map. Applied Geophysics has developed a residual mapping process named NewMag. The basic concept is to accurately interpret the aeromagnetic data, smooth the total intensity map, and calculate the residual between the smoothed and unsmoothed maps. The New mag residual map shows Parker's interpretation in red overlaying a Landmark horizon map of the NewMag residual. Scaling this residual map gives a better understanding of how the interpretation of basement shear zones was derived.

Dolomitization Process
Dolomitization, like mineralization is closely tied to temperature. In the Rocky Mountains there are numerous examples of large intrusive bodies which have raised the temperature of an area, and where mineralization typically occurse as heated fluids in the faults cool. Hydrothermal fluids flowing along basement shear zones create dolomitization and mineralization in the Ordovician Black River - Trenton Formations from Michigan through West Virginia, and to upstate New York. The process can be summarized by the following five stages:
  • Geothermal fluids move vertically along fault boundaries, tending to align along basement shear zones.
  • The fault shear zone creates fracture permeability, which increases hydrocarbon trapping potential.
  • Geothermal fluids provide magnesium, which replaces calcium molecules in carbonate rocks, causing dolomitization.
  • This means the best place to look for gas will be at the intersection of crossing basement shear zones.
  • Reefs, like the Onondaga in Stubens County, New York, were possibly fed by hycrocarbons seeping up basement controlled fracture shear zones from deeper source rocks, or from the overlaying Middle Devonian Marcellus Shale, another source rock which has generated considerable interest the last few years.
The first stage of exploration is to understand the extent of these faults. NewMag interpretation provides one view of fault planes along shear zones. To illustrate this faulting, a series of fault planes were generated and rotated (see rotation 1, 2, 3, 4, 5, 6, 7, 8, 9, or the fault planes rotation animation. This shear zone interpretation ties to fields, as shown by using Spotfire to overlay the location of fields and formations.

Lightning Stroke Count per Cell
The New York study had considerably more topgraphic changes (1,937 feet) than the Colorado County, Texas study (508 feet). This New York topography map can be used as a reference to the various lightning density maps below to evalute the impact of topography on lightning density variations over the portion of the study area being shared herein. Note that on a map contouring a count of all strokes, if anything, there are fewer lighting strikes over the higher topographic areas. Also note how well the lightning strike concentrations are bounded by the NewMag defined basement shear zones. Cross-sections with air mag total intensity and NewMag, topography, and stroke count show little correlation. Whereas cross-sections with new mag and strokes show considerable correlation.

Contouring the count of negative strokes shows density variations to contouring a count of all strokes. Note the number of concentrations which occur along interpreted basement shear zones or at the intersections of basement shear zones. This is even more dramatic contouring the count of positive strokes as there are 30 maximum positive strokes per cell compared with 119 maximum negative strokes per cell. Cross-sections showing each of the Vaisala measurements shows the correlation between the measured lightning attributes.

Lightning Data Across Time
One of the significant benefits of this new geophysical data type, is the time of occurrance. These next set of displays begin an evaluation of the importance of the number of lightning strikes between 1998 and 2008 which occurred at different seasons and different times of the day. These displays start with contours of the number of lightning strokes between March and May, between June and August, between September and November, the sum of the number of strikes for March-May and for September-November, and between December and February. This last display was from another portion of the study area where there were winter lightning strike anomalies. Note there are very few lightning strikes in the winter, which DML believes has a meteorlogical (no upwelling thermals to create thunderstorms) and a geophysical basis (snow covers the earth and dampens telluric currents, creating less cloud-to-ground lightning. These three month relationships can also be studied on cross-sections as the number of strokes per quarter.

The difference in density of lightning strikes can also be evaluated on an hourly basis: number of strokes from midnight to 3:00 AM; number of strokes from 3:00-7:00; number of strokes from from 7:00-10:00; number of strokes from 10:00 AM - 2:00 PM; number of strokes from 2:00 PM - 5:00 PM; number of strokes from 5:00 PM - midnight; or an animation of the number of lightning strikes across 24 hours. Like the quarterly data, the hourly data can be displayed as cross-sections from midnight - 3:00 AM, from 3:00 AM - 10:00 AM, from 7:00 AM - 2:00 PM, from 10:00 AM - 5:00 PM, from 3:00 PM - 9:00 PM, from 5:00 PM - midnight, or as an animated image across 24 hours derived from data from 1998 to 2008.

Lightning Data
Mapping averages of the basic lightning data attributes Vaisala records shows similar spatial density variations: contours of Rise Time; contours of Rise Time limited from 1-35 microseconds (in the other area); contours of Peak Current; contours of Peak-to-Zero; contours of Peak-to-Zero from 30-75 microseconds; contours of the average Chi Squared quality measurement; contours of the average semi-major axis (km); contours of the average semi-minor axis (km); and contours of number of sensors recording lightning strikes. These data can be evaluated with data mining tools like a Spotfire Peak Current density map, a Spotfire display of Peak Current vs. date and semi-major axis, or Landmark cross-sections of averaged Vaisala measurements 1, and 2.

Lightning strike density varies spatially. Yet there is consistency across time and across various lightning attributes. These variations and consistencies do not seem to be related solely to topography differences. They have no identified relationship to vegetation nor infrastructure. The variations appears to be related to telluric currents, basement shear zones, and possibly to natural resources like hydrocarbons or hydrothermal alteration along basement controlled faulting.

It appears these relationships can allow the remote and inexpensive mapping of basement shear zones. If true, this new lightning data type can have a major impact in shale resource plays, like the Marcellus, which occurs in this study area. Mapping large faults allows identification of areas to avoid when doing multi-stage frac jobs, where the frac could be lost in the faulting. Mapping fractures and fracture orientation allows the identification of sweetspots where there is fracture porosity. As the process is further tested and proven, it has the potential to provide a new way to high-grade lease sweetspots within large lease positions.

Case History 3:

3. Major oil company study
Details being prepared to add.

Case History 4:

4. Predicting anisotropy
Details being prepared to add.

Case History 5:

5. Cumulative Oil Production
Details being prepared to add.

Case History 6:

6. Relating fields and strikes
Details being prepared to add.

Case History 7:

7. Michigan
Details being prepared to add.

Case History 8:

8. Lightning predicted faulting
Details being prepared to add.

Case History 9:

9. Lightning time consistency
Details being prepared to add.

Case History 10:

10. Lightning by Wind Towers
Details being prepared to add.

Case History 11:

11. Lightning at BEG faults
Details being prepared to add.

Case History 12:

12. Positive Strikes at faults
Details being prepared to add.