METHOD FOR PREDICTION OF A SURFACE EVENT

• US 20200132881A1
• Filed: 04/24/2018
• Published: 04/30/2020
• Est. Priority Date: 04/24/2017
• Status: Active Grant

• Current Assignee: University of Melbourne
• Original Assignee: University of Melbourne
• Inventors: Tordesillas, Antoinette, BATTERHAM, Robin
• Granted Patent: US 11,555,942 B2
• Time in Patent Office: Days
• CPC Class Codes: G01V 20/00 Geomodelling in general
• CPC Class Codes: G01V 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

Methods and systems for predicting surface failure of a surface, for example a method comprising the steps of: obtaining a group of measured datasets, each including: a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and a time value, such that the group of datasets includes datasets associated with a plurality of unique time values, identifying an interface set of grid elements for each measured dataset, each interface set comprising grid elements of the associated measured dataset meeting a connection threshold according to a connection rule in dependence on the measurement values of the grid elements, determining a risk of surface failure in accordance with identification of a pattern of grid elements of the interface set which has a persistent location with respect to the surface of interface sets over a plurality of measured datasets.

Claims

1-25. -25. (canceled)

26. A method for predicting surface failure of a surface, the method comprising the steps of:

obtaining a group of measured datasets, each including;

a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and

a time value, such that the group of datasets includes datasets associated with a plurality of unique time values,

identifying an interface set of grid elements for each measured dataset, each interface set comprising grid elements of the associated measured dataset meeting a connection threshold according to a connection rule in dependence on the measurement values of the grid elements,

determining a risk of surface failure in accordance with identification of a pattern of grid elements on the interface set which has a persistent location with respect to the surface of interface sets over a plurality of measured datasets.

27. A method as claimed in claim 26, further comprising the steps of:

for each dataset, constructing a network of nodes, wherein each node is uniquely associated with a grid element, and in which two nodes are assigned a connection according to a connection criterion,

wherein the grid elements of the interface set of grid elements are determined in accordance with identification of a set of nodes having a closest connection to all other nodes.

28. A method as claimed in claim 27, wherein the step of constructing a network of nodes includes:

connecting each node to k other nodes in accordance with a node connection rule on the basis of a closeness in measurement value between the node to the k other nodes,

wherein k is selected in accordance with a minimum number of connections for each node that provides a connected network.

29. A method as claimed in claim 28, wherein the step of constructing a network of nodes further includes:

determining, for each node, a closeness centrality for the node; and

identifying a predetermined number of nodes for the set of nodes having largest closeness centralities.

30. A method as claimed in claim 29, wherein the predetermined number is between 10% and 30% of the total number of nodes.

31. A method as claimed in claim 29, wherein the step of constructing a network of nodes further includes:

determining, for each node, a closeness centrality for the node; and

generating a plurality of candidate interface sets,

wherein the interface set is selected from one of the plurality of candidate interface sets.

32. A method as claimed in claim 29, wherein, for each nodes i, the closeness centrality C_c is determined according to the following equation:

33. A method as claimed in claim 29, including the step of determining that the location of the grid elements corresponding to the nodes of the set of nodes is persistent, or at least substantially persistent, between a plurality of datasets.

42. A method as claimed in claim 26, wherein the measurement values comprise movement values.

43. A method as claimed in claim 26, wherein the measured datasets are generated periodically or at least substantially periodically.

44. A method as claimed in claim 26, further comprising the step of:

generating an alert in response to determining the risk exceeds a predefined risk threshold.

45. A method as claimed in claim 26, further comprising the step of generating a plurality of spatial synthetic datasets, wherein the step of identifying an interface set of grid elements for each measured dataset comprises analyzing the one or more spatial synthetic datasets in addition to the measured datasets.

46. A method as claimed in claim 26, further comprising the step of generating a plurality of temporal synthetic datasets, wherein the step of identifying an interface set of grid elements for each measured dataset comprises analyzing the one or more temporal synthetic datasets in addition to the measured datasets.

34. A method for predicting surface failure of a surface, the method comprising the steps of:

obtaining a group of measured datasets, each including;

a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and

a time value, such that the group of datasets includes datasets associated with a plurality of unique time values,

for each measured dataset, analyzing the measurement values to identify one or more clusters of measured values and at least one outlier measured value not associated with a clusters

identifying an interface set of grid elements for each measured dataset, each interface set comprising grid elements of the associated measured dataset associated with outlier measurement values, and

determining a risk of surface failure in accordance with identification of a pattern of grid elements of the interface set which has a persistent location with respect to the surface of interface sets over a plurality of measured datasets.

35. A method as claimed in claim 34, wherein the measurement values comprise movement values.

36. A method as claimed in claim 34, wherein the measured datasets are generated periodically or at least substantially periodically.

37. A method as claimed in claim 34, further comprising the step of:

generating an alert in response to determining the risk exceeds a predefined risk threshold.

38. A method as claimed in claim 34, further comprising the step of generating a plurality of spatial synthetic datasets, wherein the step of identifying an interface set of grid elements for each measured dataset comprises analyzing the one or more spatial synthetic datasets in addition to the measured datasets.

39. A method as claimed in claim 34, further comprising the step of generating a plurality of temporal synthetic datasets, wherein the step of identifying an interface set of grid elements for each measured dataset comprises analyzing the one or more temporal synthetic datasets in addition to the measured datasets.

40. A surface failure prediction system comprising:

a measurement apparatus configured to

obtain a group of measured datasets, each including;

a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and

a time value, such that the group of datasets includes datasets associated with a plurality of unique time values, and

a computing device configured to;

identify an interface set of grid elements for each measured dataset, each interface set comprising grid elements of the associated measured dataset meeting a connection threshold according to a connection rule in dependence on the measurement values of the grid elements, and

determine a risk of surface failure in accordance with identification of a persistent location with respect to the surface of interface sets of a plurality of measured datasets.

41. A surface failure prediction system comprising:

a measurement apparatus configured to

obtain a group of measured datasets, each including;

a measurement value of at least a first type for each of a plurality of grid elements, each grid element associated with a location on the surface; and

a time value, such that the group of datasets includes datasets associated with a plurality of unique time values,

for each measured dataset, analyse the measured values to identify one or more clusters of grid elements based on a similarity between the respective measurement values and at least one outlier grid element not associated with a cluster,

identify an interface set of grid elements for each measured dataset, each interface set comprising grid elements of the associated measured dataset associated with outlier grid elements, and

determine a risk of surface failure in accordance with identification of a pattern of grid elements of the interface set which has a persistent location with respect to the surface of interface sets over a plurality of measured datasets.