# Could Sherlock Holmes Have Been a Good Geologist?/Print Version

Could Sherlock Holmes Have Been a Good Geologist?

How to Achieve a Correct View of a Geological Phenomenon

Ricardo Alberto Valls Alvarez (Valls Geoconsultant)
Liudmila Victorovna Valls Alvarez

# Summary

The efficiency of a field geologist depends of his/her capacity of observation. The correct observation of a phenomenon contributes to its better understanding and allows a more rational and effective orientation of the investigation. That is why is so important to master the steps that guarantee a correct observation of any phenomenon. These steps are: (1) the perception of the object, (2) the primary observation (first impressions) and (3) the elaboration of a plan for the description of the phenomenon. Here we included the statistical pre elaboration of the data, and a model for the codification of the information during lithogeochemical sampling.

# Introduction

" ‑ I can't explain how I miss it‑
said the Inspector with expression of nuisance.
‑ It was invisible because it was buried in the mud.
If I discovered it, was because I was looking for it."

Sherlock Holmes, in "The Silver Star"

Observation is the basis of all the empiric procedures and is the most elemental form of the scientific knowledge. There isn't any specialty or branch of the sciences where measurements, descriptions or observations in the broad sense of the word, are not carried out. The correct observation of a phenomenon contributes to its better comprehension, also allowing a more rational and effective orientation of the investigation.

A scientific observation not only should supply information about the investigated object, but it must also provoke the formulation of new problems or should reveal new facets of the same problem.

In this paper, we show that observation is an interpretive process and we argue its cyclical character, suggesting the steps to follow during the observation of a phenomenon. We believe it is imperative that the researcher possesses at least an hypothesis or initial idea about of the process under study before beginning its analysis. This way the objective of the observation is reduced to verify the certainty of the hypothesis, on the basis of only a few observations.

Now, the question that needs to be answered is "How can I achieve a correct observation?"

# The Measurement

"-But, ... the height of the murderer...
how could you find it out, if we have never seen the man?

-Dear Watson, you saw me doing measurements in the room.
The distance between two footsteps showed me clearly the size of our man..."

Sherlock Holmes, in "Study in Scarlet".

Measurement is a quantified observation of a phenomenon and it is one of the more commonly forms employed in investigative work. However, almost never the results of a measurement are informative by itself. Therefore, it is necessary to "refine" them to reach to conclusions based on these data. To "refine" the data we suggest two procedures:

a‑ the search for experimental errors, and

b‑ the synthesis of the information.

The search for experimental errors is based on the determination and subsequent correction of the systematic error and the evaluation of the casual error. To determine the systematic error, we use equation (1).

[1]

${\displaystyle S_{sist}=10[{\frac {1}{n}}'(\log x_{i}^{a}\log y_{i})]}$

Where,

Ssist - is the systematic error of the data

xi - is the basic result (the result of the analysis)

yi - is the control result

n - is the amount of data, which in this type of test should be equal or bigger than 100 pair of events.

If the systematic error oscillates between 0.95 and 1.05 (both values included), we can assume that there isn't any systematic error, so we can calculate directly the casual error. Otherwise, we first eliminate this error by subtracting the systematic error to each basic value in the studied sample.

The casual error is calculated using equation (2).

[2]

${\displaystyle S_{c}=10[{\frac {1}{n{\sqrt {2}}}}'(logx_{max_{i}}^{a}logx_{min_{i}})]}$

For quantitative analysis, Soloviov and Matvieiev (1985) indicate that if the casual error is superior to 1.6, we can't relay on the quality of the data. For qualitative or semi-quantitative analysis, other authors (N.A.S.S.S.R., 1983) propose a limit value of 3. In any case, if the casual error is superior to the established limit, it should be recommended the repetition of the analysis with another methodology or with a more accurate equipment.

The synthesis of the information also consists of two stages:

a- The construction of histograms and polygons of frequencies, and

b‑ The statistic pre‑elaboration of the measurements.

The histograms and polygons of frequencies not only permit a graphic and compact representation of the measurements, but they also can give us other characteristics of the sample, such as the existence of outliers values, the value of the modes, the median, the average, etc. (Ostle, 1973; Valls, 1985).

A detailed explanation of the statistic pre‑elaboration of the data remains out of the objective of this paper. Here we will only mention some steps that are common to most of the geological data.

a- Detection and processing of the statistical outliers and of the extreme values of the studied sample,

b‑ Transformation of the data (optional),

c‑ Determination of the Distribution Law (Kashdan et al., 1979),

d‑ Determination of the median and the relative and absolute modes,

e‑ Determination of the mean, the standard deviation, the variability coefficient, and their confidence intervals, and

f‑ The determination of the thresholds.

Though these are not the only possible variables to be calculated, they are sufficient to refine the results, allowing us to present them in a more understandable way. The use of tables and comparative graphics is very recommendable.

# Systematizing the Information

"-Dear Watson -said Holmes approaching to his incredibly detailed and orderly archive
- I am sure that we will find a reference to that type of letter in this file..."

Sherlock Holmes, in "The Hound of the Baskerville"

When most of the variables of the phenomenon under study are well known, and especially when our descriptions have a discreet character, it is very recommendable the use of codified systems for the systematization of the information.

The codification guarantees a uniform observation of the essential parameters of the phenomenon, even when in the process intervene more than one researcher. It also presents the advantage of reducing the subjective factor of the obtained information (Valls, 1990).

The codified information (Kashdan, 1982) is easy to understand, to process, to regroup and study, all of which influences positively in the final quality of the observation and in the rapidity of its interpretation. The codified systems are not difficult to prepare. First we establish the basic parameters that we want to observe or measure at each point, and all possible variables of its results. Don't forget to include always a field under the name "and other" to save any forgetfulness or unexpected result.

Next we order the selected parameters in a logical way and we represent them in card of 3 for 5", for example. The centre of each card and its back will remain free for "irregular" descriptions (those that are extremely variable or when we don't know all their possible behaviors). We will use the edges of the card to codify the information. As an illustration, Figure 1shows a card created by the author for the codification of the lithogeochemical sampling (Valls, 1990).

Figure 1. A codified card for lithogeochemical sampling.

Each card is an independent and complete unit of information, where we can reflect all the useful information, from the date and hour of the observation until the results of the measurements and analysis accomplished. The combination of these cards will form the database of our investigation than can be easily exported to any computerized data system.

# The Primary Document

"-Mr. Mac, the more practical thing that you could do
in all your life is to lock yourself for three months
to read during twelve ours a day the annals of the crime".

Sherlock Holmes, in "The Valley of the Terror".

The main objective of all this process is to obtain a quality primary documentation, which keeps its informational value even after the publication of the results. When the primary documentation is made correctly, the data can become very useful in the case of a reevaluation of the studied area.

A good primary documentation will fulfil the conditions listed bellow:

a- UNIFORMITY; represented by the same investigator, the same analytical equipment, the same technology or work technique, etc. The use of

codified cards or any other codification method will be of great help to achieve uniformity, mainly in those occasions when more than one person works in the same place.

b‑ PRECISION; all the statistical treatment, experimental designs, etc., most be accomplished with the same degree of precision.

c‑ REPRODUCIBILITY; the observations collected in the primary documentation should answer to some description system that will allow its later verification. All the key samples should have a duplicated.

d‑ READABILITY AND EASY ACCESS; the primary documentation should be located in adequate places, duly ordered, to ease their consultation and conservation. The text, unless it is in microfilms, codified or typewritten, should be written with legible letter. It is expected that the primary documentation will be simple and objective, so that its study will be quick and effective.

# Conclusions and Recommendations

Given the importance of a correct observation for the verification of an hypothesis and for the development of the scientific ideas, it is necessary to pay attention to the collection and conservation of the primary documentation.

Before beginning an observation, it is very useful to have an idea or objective defined. This will allow us to verify the accuracy or incongruity of our initial hypothesis, with only some key observations. Also is important to remember that the observation must be selective and interpretive, to become scientific.

Don't "tie" yourself to a hypothesis. It is the hypothesis which must adapt to the facts and not the other way around.

Since observations by themselves are almost never a source of sure and univocal information, it is always advisable to process statistically the obtained results. The calculations proposed in this paper are usually sufficient for the preliminary elaboration of any assemblage of geological measurements.

The practical application of these recommendations, plus a constant search of improvement based in the comparison and the critic analysis of previous observations (you’re own or someone else's) will soon allow you to improve the quality of your observations.

As for the codification of the information, it is recommended chiefly when the studied system is well known and when the observations are accomplished only at independent points, and we assume that among both points does not exist meaningful differences. If so, we recommend establishing a codification system to save time and to increase the interpretive quality of the observations. We are sure that Sherlock Holmes has done it in that way... if he has been a geologist!!!

# References

N.A.S.S.S.R. (1983). Instruktsia dlia geojimicheskiie metodi poiskov rudnij mestoroshdenii, Nedra, Moscow, 263 p.

Bunge, M.A. (1972). La investigación científica, su estrategia y su filosofía. Ed. Ciencias Sociales, Havana, 955 p.

Cheeney, R.F. (1986). Statisticheskie metodi v geologii. Recheniie polevij i laboratornij zadach. Mir, Moscow, 185 p.

Kashdan, A.B. (1982). Prospección de yacimientos minerales. Mir, Moscow, 376 p.

Kashdan, A.B. and O.I. Gushkov (1979). Matematicheskoie modelirovanie v geologii i poiskov rudnij mestoroshdenii. Nedra, Moscow, 233 p.

Ostle, B. (1973). Estadística aplicada. Ed. Pueblo y Educación, Habana, 638 p.

Valls Alvarez, R.A. (1990). Tercera versión de las fichas codificadas. Ventajas de su uso. Geological Report, 18 p.

‑‑‑‑‑‑‑‑‑‑ (1985). El uso de histogramas y de polígonos de frecuencias en la interpretación de los datos geoquímicos, in Resúmenes de la II Jornada Científico Técnica de la Filial Santiago de la Sociedad Cubana de Geología, Santiago of Cuba, Cuba. p. 8-21.

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